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Foundations of Universal Design for Learning (UDL)

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Foundations of Universal Design for Learning (UDL)

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Epistemic Cognition + Concepts on Cognition and Metacognition

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Epistemic Cognition + Concepts on Cognition and Metacognition

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Types of memory, cognitive load theory (CLT), and the different types of cognitive load.

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Types of memory, cognitive load theory (CLT), and the different types of cognitive load.

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Different Types of Assessments/ UDL Framework

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Different Types of Assessments/ UDL Framework

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CAST

CAST

2018

Abstract

What we know about the brain is central to the Universal Design for Learning Framework. Knowing key facts about the brain from a UDL perspective can help inform learning design to address learner variability. Find out more in this downloadable resource designed for educators.

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CAST (2018). UDL and the learning brain. Wakefield, MA: Author. Retrieved from http://www.cast.org/products-services/resources/2018/udl-learning-brain-neuroscience

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What we know about the brain is central to the Universal Design for Learning Framework. Knowing key facts about the brain from a UDL perspective can help inform learning design to address learner variability. Find out more in this downloadable resource designed for educators.

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Patricia Furstenberg, Writer of Historical Fiction, Children's Books - Travel via Stories

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The Six Principles That Move Mind, Brain, Education, MBE - Patricia Furstenberg, Writer of Historical Fiction, Children’s Books

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The Six Principles That Move Mind, Brain, Education, MBE, ITSI SA. Education, the limits of the human brain. What are the limits of the human brain? How do we learn best? How can we aid and support our children in reaching their full potential as students? Throughout the centuries, great teachers have been guided by

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Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing

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Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing

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Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing

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UDL/ The UDL Guidelines

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Harvard University Extension School, Cambridge, MA, United States

Faculty for Latin American Social Science Research, Quito, Ecuador

The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences. To determine why knowledge from the learning sciences has not had a greater impact on educational policy, this study considered evidence from the learning sciences through a previously published systematic review of the literature followed by a Delphi panel of experts on the learning science ( Tokuhama-Espinosa, 2017). This was compared with a literature review of the trends in leadership decision-making models (data-based; context; distributed; transformative; goal-orientated, results-oriented) (Appendix A in Supplementary Material). This review found 30 current educational policies that contradict evidence from the learning sciences, suggesting a disconnect between educational science and other learning sciences. While there are some initiatives underway in a small number of educational leadership sectors to incorporate more learning science data into decision-making, it is not a norm. The current business-oriented model in educational policy design may explain this divide. The analysis of these results considers how switching from a business to a learning science model may result in different educational priorities. Such a vision offers a distinct and possibly more universally acceptable measure of "quality" education, detached from the immediate social and political goals and independent of the historical times in which they are taken. This paper suggests further research into this new learning sciences evidence-based framework on which educators base policy decisions.

Introduction

Educational Leadership

Public expectations of educational leaders have changed over the past 50 years ( Hallinger, 2011). Educational administration has given way to a broader personal role for educational leaders though it continues to maintain the universal objective of free and equal education for all ( Shields, 2017). This vision evolved in the United States from the 1960s and 1970s as schools sought out people with "courage, initiative and imagination," ( Broudy, 1962, p. 132) to the 1980s−2000s when the goal was to be caring ( Beck, 1994), moral ( Hodgkinson, 1991), and ethical ( Beck and Murphy, 1994). Starting in the early 2000s educational leadership added on elements of social justice ( Bogotch, 2002) and equity in leadership roles ( Grogan and Shakeshaft, 2010). Beginning at the turn of the twenty-first. century, technology ( Picciano, 2002) and innovation ( Jackson and Kelley, 2002) were added to the growing list of educational policy objectives, as was a new value in international comparisons of school leadership and decision-making (e.g., Darling-Hammond et al., 2007).

In a 2019 analysis of competency-based standards for school principals in Canada, Australia and the United States, Lambert and Bouchamma found that 85% of the measures that improved student success were similar or identical across countries, suggesting a shared vision of good leadership. These included 20 "professional behavioral standards," only three of which came directly from the learning sciences ("knowledge of pedagogy/programmes [sic], "learning practices and strategies" and "needs of the students"). Ten of the 20 standards came directly from business models (laws and policies; material and financial resources; human resource management; organizational climate; improvement and innovation; collaboration, shared leadership and empowerment; supervision; decisions and responsibilities; mission and vision formation; and data use and management). The last seven standards can be found in both the learning sciences and business models of educational leadership (balanced personal and professional life [self and others]; professional development [self and others]; technology [ICT]; communication; adaptation to change and feedback; community partnership and outreach initiatives; cultural diversity [inclusion]) ( Lambert and Bouchamma, 2019, p. 61). Other international comparisons similarly place educational leadership skills and behaviors into similar categories and suggest both a humanization of leaders in which personal investment into group dynamics is valued, as well a deeper reliance on data for decision-making (e.g., Amanchukwu et al., 2015).

Whereas, it was once thought that the role of principals and department heads was to manage ( Grace, 2005), effective leaders are now asked to inspire and transform ( Anderson, 2018). The current literature on educational leaders reflects their ever-growing areas of responsibility. On one end of the spectrum this spans from "instructional leadership" (e.g., Hallinger et al., 2015) in which school leaders are meant to drive better teaching initiatives (e.g., Smylie et al., 2016), social-emotional learning and wellness programs (e.g., Bryant et al., 2016), to the other end of the spectrum where they are also responsible for the design and maintenance of sustainable buildings (e.g., Veronese, 2012), and community outreach initiatives (e.g., Epstein, 2018).

To achieve this broad band of responsibilities special competencies are required. Some have researched how these competencies impact success through the effectiveness of different personality types or behaviors (e.g., Judge et al., 2002; Amanchukwu et al., 2015), or how theoretical models of leadership transpose onto school environments ( Wang et al., 2017; Gumus et al., 2018). Only a handful of studies have considered the broad range of responsibilities and the difficulties of prioritizing them (e.g., Cheong Cheng and Ming Tam, 1997; Scheerens, 2012). The challenges of being a good educational leader grows with every new competency, which continue to be added to the profile over time and as the role of schools expands. That is, what was valued 50 years remains valued today, but along with those competencies are newly added responsibilities.

The complex nature of educational leadership can make it tempting to go "back to the basics" of good administrative skills and core management concepts. It can be argued that managing numbers is easier than managing people ( Pfeffer and Veiga, 1999). Therefore, many find the decision of how to be a good educational leader easier when they lean toward the business and management side of their role than the educational side, meaning many schools are run more like businesses than places of learning ( Senge et al., 2012; Onorato, 2013). The business or economic model of quality education celebrates the achievement of cost-effective practices ( Levin and Belfield, 2015). As a public good that is almost always underfunded ( Ikpa, 2016), many educational leaders feel that their role is to stretch each dollar as far as possible, which in many cases means favoring decisions in which budget items that provide a high level of impact at low cost are favored. Standardized testing is an example of an attractive decision which promises a lot with a relatively low investment ( Dolton and Marcenaro-Gutierrez, 2016). Other researchers suggest, however, that education is not actually underfunded, but rather poorly managed ( James, 2015), adding to the pressure on educational leaders to spend wisely. An international comparative study found that there is a global trend is the use of "market terminology in educational settings" and "increased accountability and responsibility for school leaders," ( Townsend, 2011, p. 93). There are many, however, who also see the importance and potential gains of embracing more complex models of educational leadership.

Pont, for example ( Pont, 2013), describes both professional as well as behavioral standards of principals. In the professional standards he suggests five basic groupings: (a) prioritize guidance (e.g., mission, vision, innovation); (b) establish organizational conditions (e.g., material and financial resources); (c) develop self and others (e.g., professional development and human resource management); (d) exercise pedagogical management (e.g., use of relevant data, technology); (e) create harmony within the school (e.g., communication, organizational climate). In the behavioral standards, he suggests four groupings: (a) flexible management of change (e.g., adaptation to change and feedback); (b) communication; (c) values (e.g., decisions and responsibilities, cultural diversity); and (d) theory linked to practice (e.g., use of relevant data, results, and research). Pont's detailed work suggests that many educators are actively involved in trying to adapt to the growing list of responsibilities and several have managed to systematize the understanding of this range of tasks.

The Learning Sciences

The goal of the learning sciences is to, among other things, study how people learn best and under which conditions ( Sawyer, 2005a). Originally called the "science of learning" and changed to the "learning sciences" after the Cambridge Handbook of the Learning Sciences was revised in 2006, this field is really a collection of more than a dozen fields that share common foci on problems ( Sawyer, 2008). The learning sciences include a wide range of fields such as neuroscience, psychology and education ( Fischer et al., 2018), which grow independently but also collectively.

The new field of Mind, Brain, and Education science, founded in 2004 at the Harvard University School of Education, seeks to elevate teacher practice through greater professionalization ( Fischer et al., 2007). In part, this is done by helping educators see their roles are equally important in resolving school challenges as that of neuroscientists and psychologists. Mind, Brain, and Education science is a sub-field of the greater learning sciences and responds to challenges on topics of equal concern to neuroscientists, psychologists and educators. For example, "motivation," is studied by multiple learning sciences, but each with its own methods and units of analysis (neuroscientists study the brain, psychologist study the mind and educators study learners in classroom contexts). Mind, Brain, and Education science attempts to unify the collective knowledge on any one of these topics through translational research and/or unique methodologies. The logic of this approach is that it is more likely that a transdisciplinary vision of a problem in education will yield a better solution than a single discipline perspective ( Tokuhama-Espinosa, 2008).

There have been international recommendations, such as the OECD's expert panel on Pedagogical Knowledge and the Changing Nature of Teaching ( Guerriero, 2017), and counsel from academia pushing educational leaders to consider "How Can the Learning Sciences (Better) Impact Policy and Practice?" ( McKenney, 2018). Additionally, there have been two international Delphi expert panels which sought to gain consensus by learning scientists around the world about how educational leaders' work and teachers' new pedagogical knowledge should be shaped by a learning sciences knowledge base ( Tokuhama-Espinosa, 2008, 2017). These studies point to a consistent base of knowledge that should inform educational decisions and included a list of research, practice and policy goals that should guide educational decision-making processes ( Tokuhama-Espinosa, 2015, 2017). Only a small number of teacher colleges and policy makers, such as the Deans for Impact (2015), have actually incorporated learning sciences documentation into their decision-making practices, however. To better understand the slow uptake of information from the learning sciences into educational practice, this study sought to compare educational decision-making with evidence from the learning sciences to see where they differed and why.

Research Question

The primary research question of this study was How and to what extent do educational leaders take into consideration information from other learning sciences when making policy decisions?

Once answered, a new question emerged which was considered in the Conclusions: Can the learning sciences catalyze a change in educational policy priorities?

Materials and Methods

Materials

This study reports on the fourth of four stages of research, which is a comparative review of the literature from educational leadership and the learning sciences. The literature review specifically studied how decisions in favor of quality education are made from the educational leadership perspective as compared with the learning sciences perspective. The first stage of this research was a triangulated mixed-methods design including a grounded theory meta-analysis of the literature using open coding, followed by an international Delphi panel on Mind, Brain and Education science conducted by the author for her doctoral thesis ( Tokuhama-Espinosa, 2008). The second stage was conducted in 2015 which consisted of identifying 217 indicators that have been used to measure quality education over the past 40 years in 34 OECD nations ( Tokuhama-Espinosa, 2015) and how countries prioritized them based on individual choices including long- vs. short-term investments, budgetary restraints and political promises. This prioritization was used to identify key areas for educational leadership decision-making that were analyzed from both educational administrator perspectives and learning scientists' viewpoints. The third stage was a 10-year follow-up to the international Delphi panel on Mind, Brain, and Education science, proceeded by a meta-analysis of the literature 2007-2017 ( Tokuhama-Espinosa, 2017), the goal of which was to confirm learning science principles, tenets, neuromyths and research, practice and policy goals. This study along with the first Delphi in 2008 were used to establish the learning scientists' perspective. These background studies provided the platform upon which a comparison of the learning sciences could be made with educational leadership.

The literature review of this paper explores educational leadership research to determine areas of responsibility (Appendix A in Supplementary Material) based on key words used in meta-analyses over the past decade. Research was limited to studies for which there was free or open access or available through the Harvard Online Library (HOLLIS) and published between 2000 and 2019. This list was used to (a) appreciate the spectrum of responsibilities of educational leaders and (b) and compare it with the types of problems learning scientists research.

Methods

Backward Design

This literature review study applied Backward Design planning (objectives, evaluation, activities) ( Wiggins and McTighe, 1998) to compare and contrast the ways educational leaders and learning scientists articulate problems, frame research questions and identify objectives. One hypothesis was that heuristics was responsible for both actors' decision-making frameworks.

Backward Design or Understanding By Design from the book title ( Wiggins and McTighe, 1998) means beginning with the end in mind, which points to a clear path to success by marked stages of advancement. Backward Design is a planning mentality that was first introduced in the 1940s in business contexts ( Tyler, 1949) and since the late 1990s in educational models ( Wiggins and McTighe, 1998). In Backwards Design, users are prompted to first identify the objective. Second, they choose the evaluation method(s) that will best offer evidence of advancement toward that objective. Third and last, users are asked to select the activities which would most likely be able to generate the indicators for evaluation.

Wiggins and McTighe note that the most complicated aspect of Backwards Design is appropriately identifying the objective. It is for this reason that this study undertook a review of multiple literary sources to identify the collective agreement on objectives in education, rather than use a single perspective. Wiggins and McTighe note that identifying objectives individually can be deceptively illusive to educators. It is not uncommon to hear teachers declare an activity as an objective ("We will play a crossword puzzle" rather than "Students will improve their vocabulary"). Policy makers also mistakenly label global policy expectations as objectives ("Improve education") without understanding that the precision with which they can identify the objectives, the easier it will be to both measure and plan activities around it. It is for this reason that meta-analyses and systematic literature reviews were used to identify the objectives of learning scientists and objectives of educational leaders.

The reference to "backwards" is that many educators and educational leaders complete this planning process in the opposite order. That is, it is not uncommon to find that teachers and educational policy makers execute activities/policies and then evaluate them, sometimes without ever considering how closely the objective matched the activity or the evaluation tool.

In business, leaders set a target objective (for sales, or growth in market share, for example). They then choose indicators for success (e.g., earned income or brand affiliation testimonies). Lastly, they select the best activities to reach the objective(s) (e.g., investing in advertising, interviews with product users, and so on). In education, leaders do much the same. Educational leaders identify objectives (e.g., equitable timetabling for teachers or improved test scores), select an evaluation method (e.g., satisfaction rates of teachers and number of hours assigned per person or additional time for student test preparation), then choose activities that help them reach their goals (e.g., buy a timetabling software and review prior years' planning or devise test preparation activities, and so on).

Figure 1. Educational leadership requirements.

Figure 2. The learning sciences.

Figure 3. Backward design literature review parameters.

Determining the objective in Backwards design is complicated mainly due to the "framing" created by word choice ( Nelson, 2011) and influenced by heuristics ( Lockton et al., 2013). The precise articulation of objectives permits a clear and logical decision about the choice of evaluation tool and activities.

In summary, the current study consisted of a literature review of educational leaders' areas of responsibility based on publications between 2000 and 2019, which was compared with learning scientists' areas of responsibilities based on two Delphi panels on the learning sciences ( Tokuhama-Espinosa, 2008, 2017).

The evaluation of educational leader responsibilities was gauged by using the 217 indicators used to measure quality education by OECD member countries between 1975 and 2015 ( Tokuhama-Espinosa, 2015). The evaluation of the learning scientists' responsibilities was measured by using the second Delphi panel's stated research, practice and policy goals ( Tokuhama-Espinosa, 2017).

The results permitted the comparison and contrasting of the ways educational leaders and learning scientists approach the objective of quality education. This comparison identified 30 current educational policies that contradict evidence from neuroscience, psychology and other learning sciences, suggesting a disconnect between educational science and other learning sciences.

Theoretical Frameworks

This paper examined the contrasting views of educational leaders and learning scientists in their articulation and response to important topics in education including school start times, learning modalities, curriculum priorities, evaluation, teacher formation among others. To do so, two distinct theoretical frameworks were used: (a) Heck and Hallinger's Educational Leadership perspective; and (b) Sawyer's construct of the Learning Sciences, each of which is briefly described below.

Educational Leadership

First, today there is less agreement about the significant problems that scholars should address than in past years. Second, scholarly directions seem to be changing, as an increasing number of scholars are approaching educational leadership and management as a humanistic and moral endeavor rather than a scientific one. Third, although there are more diverse and robust methodological tools available for inquiry, programs of sustained empirical research are few in number. Fourth, a reluctance to evaluate the worth of contrasting conceptual and methodological approaches according to an accepted set of scholarly criteria leaves researchers, policy-makers and practitioners to fall back upon individual judgments of what is useful and valid knowledge. Finally, a lack of empirical rigor in the field continues to impact the development of a future generation of researchers (p. 229).

Based on Heck and Hallinger's work, this paper begins with five premises. First, there are diverse problems in education require leadership attention, and there is not always agreement on the priorities. Second, education is both a humanistic as well as scientific endeavor. Third, more diverse and robust methodological tools should be used in educational inquiry. Fourth, individual judgement is valid but should be informed by data. And fifth, empirical rigor should be improved in educational policy decisions.

The Learning Sciences

The theoretical framework for the learning sciences is derived from Sawyer's work ( Sawyer, 2005a, b, 2008) as he has been one of the emergent field's best chronologists and has been featured as a main author in this field by the OECD and the Cambridge Handbook of the Learning Sciences. Sawyer suggests that

Learning sciences is an interdisciplinary field that studies teaching and learning. Learning scientists study learning in a variety of settings - not only the more formal learning of school classrooms, but also the more informal learning that takes place at home, on the job, and among peers. The goal of the learning sciences is to better understand the cognitive and social processes that result in the most effective learning, and to use this knowledge to redesign classrooms and other learning environments so that people learn more deeply and more effectively...This new science is called the learning sciences because it is an interdisciplinary science; the collaboration among these disciplines has resulted in new ideas, new methodologies, and new ways of thinking about learning [bold by author] ( Sawyer, 2008, p. 1).

A premise of this paper derived from Sawyer's work is on the role of researchers, teacher-practitioners and educational leaders as people who can leverage the new knowledge from the learning sciences to redesign learning.

Analysis

To analyze the comparison of the educational leadership objectives with learning science objectives, Tokuhama-Espinosa's decision-making process for the evaluation of quality education ( Tokuhama-Espinosa, 2015) was compared with the Delphi panel's 2017 delineation of goals for quality education ( Tokuhama-Espinosa, 2017).

The Evaluation of "Quality" in Education

A quote attributed to many people including Cameron (1963) is apropos in analyzing findings: "Not everything that counts can be counted, and not everything that can be counted counts." There is a temptation in education to measure what is easily measurable, rather than identifying what may be the most important ( Buros, 1977; Madaus and Stufflebeam, 1988; Coe and Fitz-Gibbon, 1998; Harvey and Newton, 2004; Ozga et al., 2011; Belfield and Brooks Bowden, 2019). This paper considers how educational decision-making often falls into the temptation of low-cost initiatives rather than framing decision-making around stated objectives using the learning sciences. This slant in favor of a business model of education is easily justified in the realm of economic planning, but becomes less defensible when framed in light of student learning objectives.

As Tokuhama-Espinosa found in a 2015 review of 40 years of literature on the evaluation of quality education, decision-makers "may resort to measuring easily available or less costly data, as opposed to appropriate data for their objectives" ( Tokuhama-Espinosa, 2015, p. 115). The 271 indicators used by OECD countries to measure quality indicators were divided into 11 categories, which were similar to the areas of responsibility mentioned by educational leaders: (a) Coverage; (b) Equality and Equity; (c) Retention, Completion and Staying Rates; (d) Standards; (e) Academic Achievement; (f) Teachers and Teaching; (g) Evaluation; (h) Finance; (i) Governance; (j) Family and Community; (k) Context (Culture, Legal, Demographic). Findings from the study showed that the "common or standard approach" of those responsible for educational quality was to identify quality education indicators by appraising "what is easily accessible, less costly, politically weighty, or recommended by large ...organizations," ( Tokuhama-Espinosa, 2015, p. 115) rather than to "find systematic, recurring reflective processes that respond to the many distinct aspects of indicator choice," (p. 115). In the case of educational decision-makers at the school level, pressure to use evaluation tools is compounded by market forces ( Burch, 2006) which sometime reveal a circular relationship between textbook publishers and testing companies ( Saltman, 2016) and may all but ignore indicators for student success that do not lend themselves to a multiple-choice test ( Duckworth et al., 2012). It is notable that educational leaders who prioritize student learning over economic thrift often have long-term payoffs at both the individual student level as well as at a macro level of regional, state or country benefits ( Hanushek and Woessmann, 2010, 2015).

Results

Two important findings were made. First, priorities in educational leadership are not the same as priorities in the learning sciences as determined by the way each group frames educational problems or decisions. Second, what is deemed important by society, educational leaders, parents and students related to education may vary and is not always easy to measure.

Problem Framing and Articulation

Educational leaders and learning scientists often see similar problems but articulate their research questions differently, leading to distinct ways of both measuring progress toward objectives as well as the choice of appropriate activities. For example, both learning scientists as well as educational leaders are concerned about the curriculum (e.g., Glatthorn et al., 2018; Tokuhama-Espinosa, 2019), the school calendar (e.g., Farbman et al., 2015; Finnie et al., 2018), and teacher training (e.g., Allen and Penuel, 2015; Darling-Hammond et al., 2017), but they approach these issues differently based on the words they use to state their objectives. The literature suggests at least 30 questions for which both educational leaders and learning scientists have conducted research and sought answers (see Table 1). Their approaches, however, were influenced by word choice and heuristics in how they framed the problems.

Table 1. How decisions in education would look if mind, brain, and education scientists ran schools.

Word Choice: Framing Questions to Identify Objectives

Table 1 explains 30 different topics in education broken down into six groupings: (a) time; (b) space; (c) content; (d) order; (e) the meaning of "success"; and (f) teachers and teaching. Each table identifies the general topic, followed by a question or problem statement. After this question or problem statement there are two columns, one each for educational leaders and learning scientists. Below each is a question based on actual research studies that may run through the mind of each coupled with its matching objective.

The ways problems are articulated are shaped by at least three factors: (a) vocabulary ( Dove, 2014), (b) heuristics and bias ( De Martino et al., 2006), and (c) the professional vocabulary of one's field of formation ( Loewenstein, 2014). Fairhurst points out that communication style and problem articulation are not merely surface decisions, but rather are deeply seeded in the ontological foundations of different types of thinkers. Because educational leaders and learning scientists frame problems differently, they see different objectives within each category. This results in different priorities in educational improvement initiative, which will be explored in the discussion.

Identifying Important Problems in Education: Measurable vs. Important

Measurable

A second interesting result was that the literature review revealed that the 217 indicators mentioned in the 2015 study by Tokuhama-Espinosa was an accumulative list of all of the indicators used over a 40-year period by 34 OECD member countries. This means it reported what had been used but did not factor in potential question or problem statements posed by the larger community nor those posed by learning scientists. This suggests a disconnect between educational leaders' decision-making priorities and what both the general public and learning scientists consider worthy of resources.

While the "Top 10 Issues in Education" ( Knowles, 2018; Chen, 2019; Rich, 2019) reported in the popular press are for the most part included in the indicators used by the OECD countries to measure quality-including hiring the right people to be teachers in the first place and supporting them to find professional success-some important indicators with a high potential to change educational outcomes were not measured by the OECD countries-such as how early bilingualism improved overall cognition or how later school day start times influenced learning outcomes.

The strong focus on measurable outcomes is rooted in data driven decision-making which prioritizes the quantitative (often short-term) aspects of quality education and often overlooks the qualitative (often long-term) aspects of quality education, which imbalances the evaluation of multiple indicators ( Schintler and Kulkarni, 2014). Cost is often a factor in determining measurement indicators; budget restraints may push educational leaders to opt for evaluation tools that that thrifty rather than thorough or important.

Important

A third example comes from early childhood education. It has been known for the past 60 years that high quality early childhood education has a lasting impact on student learning outcomes ( Pianta et al., 2016). Good early childhood programs also improve self-regulation skills ( Montroy et al., 2016), which have greater impact than innate intelligence on academic success ( Moffitt et al., 2011). It is also clear that strong early childhood education programs shape good nutritional habits ( Schwartz et al., 2011), sleep habits ( Cespedes et al., 2014), and social skills ( Jones et al., 2015), among others.

A fourth and final example comes from bilingual education. Early biliteracy skills have been shown to mitigate the effects of poverty ( Petitto and Dunbar, 2009), yet foreign language and bilingual programs are some of the first courses (along with physical education and the arts) to be cut from educational curricula to make room for more first language and math in order to respond to standardized tests ( Crocco and Costigan, 2007). It is ironic that the enhanced executive functions from early bilingualism ( Bialystok, 2018) which have a spillover effect in all other subject areas, is eliminated from some schools in order to try and improve learning which would naturally be enhanced by bilingual education.

The decision to choose indicators which are easily measurable over those that may be more important and potentially yield better educational outcomes will be explored in the Discussion section.

Discussion

This suggests that the way problems are framed influences perspective-taking changes primarily in one of two ways. Either the understanding and articulation of the problem are changed by the heuristics of the end user (leader or scientist), or the choice of available evaluation tools drives the objective and not the other way around. This implies that there are four possible reasons why educational science has not yet considered itself a learning science: (a) Problem articulation, (b) priorities from the learning sciences not being on educational leaders' agendas, (c) the need to nudge educational leaders into the learning sciences, and (d) no natural access to learning science information on the part of educators. Each will be explained in more detail below.

Why Don't Educational Leaders Think Like Learning Scientists?

The findings suggests that the indicators currently used to make educational decisions by school leaders may be incomplete as they ignore much of the data from the learning sciences. Given the gravity of the stakes-quality education-it is important educational leaders take advantage of all of the information possible to make the best decisions in all areas of responsibility. This means that educational leaders may improve outcomes if they think more like learning scientists. Evidence from the literature indicates that there are at least four contributors as to why educational leaders do not normally consider these high impact decisions. Each of these reasons is followed by a hypothetical solution.

Problem Articulation

Contribution 1

An initial hypothesis as to why educational leaders do not think like learning scientists suggests that the problem is not only systemic in terms of professional access, but that it may also be a problem of semantics. The first contribution is that educational leaders and learning scientists articulate problems differently. That is, when asked, what is the objective? In Backward Design thinking, they would phrase and focus questions and problems with the lenses of their professions, blinding them to different perspectives.

While the use and the steps in Backward Design are the same for business leaders as for educational leaders, business leaders are often facilitated in their objectives by the quantitative nature of problem conception. That is, business goals tend to be less "messy" as they broadly focus on "clean" numbers ( Mandinach and Jackson, 2012), whereas educational goals are complicated by multiple human variables. Educational leaders walk a fine line between the business objectives of efficiency, balanced budgets, satisfied workers, and so on, and educational objectives or quality teaching and learning and the human side of education ( Daly, 2012).

The professional schizophrenia between efficiency and expediency on the one side and caring human investment on the other also explains why some educational leaders confound variables in decision making. For example, learning scientists know that test scores do not measure intelligence but rather sub-elements of limited aspects of intelligence. The complexities of human intelligence cannot be measured in a single test, no matter how comprehensive ( Tokuhama-Espinosa, 2018). Ever since "intelligence" tests were conceived in the 1920s, the objective of improved "student learning outcomes" has used test scores, primarily in math and language, as a proxy for intelligence. Despite multiple detailed arguments to the contrary, the objective (learning) and the evaluation tool (multiple choice tests) do not align as Backward Design would suggest. However, it is very expedient to use a single tool such as a standardized test. Tests are also less costly, which explains why many educational leaders may be convinced of standardized test utility ( Gipps, 2011). Mistakes stemming from the articulation of the problem explain why educational leaders and learning scientists reach different conclusions about standardized testing and other key decisions in school settings.

Not on the Radar

Contribution 2

Second, it has been suggested that the lack of attention toward some of the information from the other learning sciences, such as neuroscientific data about school start times or social-anthropological data about school calendars, is because these are not recurrent or annual leadership decisions ( Robinson et al., 2008) and are simply not "on the agenda" or "the radar" due to time constraints ( Maule et al., 2000). That is, the general day-to-day demands and the wide spectrum of responsibilities of educational leaders does not permit them the time to add in additional decisions that are not regularly attended to.

This could potentially be resolved by calling attention to the importance of these topics and their relatively weighty impact on student learning outcomes, including significant effect sizes reported in recent work by Hattie and Yates (2013) and others. Despite decades of evidence, however, the National Academies of Sciences, Engineering and Medicine suggests in their consensus study report, How People Learn II (2018) that educational leaders may be aware, they just do not prioritize these findings. This leads to a third hypothesis which is at the heart of leadership.

The Need for a Nudge

Contribution 3

Most humans conform to the status quo unless otherwise nudged ( Thaler and Sunstein, 2009). Leaders tend to be the people who can do the nudging; however, this can go against some leaders' human nature. Some suggest that the lack of attention to these types of decisions is due to heuristics ( Busenitz and Barney, 1997; Hafenbrädl et al., 2016). Humans do not pay attention or question school start times, school calendars, early childhood education policies or bilingual education because they presume if there was an important decision to be made, "everyone" would be talking about doing it ( Kim and Gambino, 2016). It takes a truly bold leader to break from the pack and the risk-aversion that accompanies change. Most people are uncomfortable with "rocking the boat" or creating dissonance in the community, and therefore, safe inertia is prized over unstable change ( Hayes, 2018).

To compound this challenge, some educational leaders may know about the evidence from the learning sciences but either are unwilling or unable to communicate it to their constituencies or do not have a built-in context through which to facilitate this data. This means that educational inertia is both a sin of inactivity as well as inability. In the first case, the leader is culpable of taking the path of least resistance and ignoring the information. In the second, however, leaders may be willing to try and nudge for change, but they may either not be good enough communicators of the message, or they may be faced with a more articulate resistance. For example, many educational leaders report that their attempts to change school start times or academic calendars was met with push-back from the community who "resisted change." Rather than "power through" toward successful implementation, they bow to the pressure of a less informed community. Or, if they attempt to rebut the resistance and fail, it may be due to their lack of persuasive dialogue. Leadership requires perseverance as well as good communication ( Marques and Dhiman, 2016), characteristics not always present in administrator profiles.

No Natural Access to Information

Contribution 4

A final contribution as to why educational leaders do not think about high-impact decisions as seen by learning scientists is that they are simply unaware they exist. Many from the learning science community believe educational leaders do not access the information or data they need to make decisions ( Thomas et al., 2019), possibly because information from the learning sciences is not naturally shared with those in educational leadership ( Morris and Sah, 2016).

Information on the learning sciences has slowly become more available to educators, however. Access has improved over the past two decades with the emergence of more frequent high-quality opportunities for professional development such as the International Mind, Brain, and Education conference, the Learning & the Brain conference and educational leaders themselves who seek out professional advice and consulting from learning scientists. The Deans for Impact, a consortium of teacher colleges in the U.S., published a basic guidebook on the learning sciences and motivates educational leader participation in academic programs that bridge administration and knowledge from the learning sciences (2015). New online communities, such as Nature Partner Journal's Science of Learning community and the journals Learning Sciences, Science of Learning, Mind, Brain and Education, and others seek to merge knowledge from educational sciences and other learning sciences such as neuroscience and psychology, for better decision making.

The existence of such resources, however, does not guarantee that educational leaders use them. This means that educational leaders should be more pro-active in exploring the other learning sciences until a natural conduit of information to and from each of the learning sciences is facilitated.

What Would Happen if Educational Leaders Thought More Like Learning Scientists?

These reflections beg the final question: What would happen if educational leaders could be more conscious of their heuristics and how they articulate problems, and consequently attempt to view problems within schools from a learning science perspective?

There have been many shifts in the role of educational leaders over the past 50 years. None perhaps is as challenging as bridging knowledge across disciplines. The slow uptake of information produced in the learning sciences into schools is testament to the complexity of transdisciplinary thinking ( Fam et al., 2018). The mental reframing of problems to reflect a broader use of data from all of the learning sciences has the potential to significantly improve education and should, therefore, be considered by educational leaders.

This suggests that educational leaders must ask themselves whether they can see the forest for the trees. That is, what is the real objective of education? For many decades, the cost-effective model ( Levin and Belfield, 2015), which has now been fueled by the data-driven model ( Meyer et al., 2016), has steered leadership decisions in favor short-term economic indicators rather than long-term learning as the ultimate measure of "quality" and often without considering the roles of all actors involved ( Levin and Datnow, 2012). If education is meant to produce human cogs in a societal wheel of progress in an efficient way, then we may already have the perfect system. However, if formal education conducted in schools is meant to maximize the potential of all students to learn, then perhaps we should start thinking more like learning scientists.

Conclusions

This study centered around the research question, How and to what extent does educational science take into consideration information from other learning sciences when making policy decisions? It was determined that policy decisions by educational leaders are complex, broad in their scope and have been studied by many researchers over the years in multiple context and with varying lenses (e.g., Darling-Hammond et al., 2007). Aside from the recommendations from the Deans for Impact (2015), however, the literature review of this study did not reveal consistent or systematic use of data from other learning sciences such as neuroscience by educational leaders. This means that the answer to the original research question is that many educational leaders do not take into consideration much of the information available from the other learning sciences when they make policy decisions, despite its availability. This is due to numerous factors, including problem articulation (semantics, heuristics), not being on educational leaders' radars due to other priority issues which are more familiar, the need for a nudge into the unfamiliar territory of the learning sciences, and that education about the learning sciences is not a "natural" part of educational leaders' typical formation. This clearly points to an explanation as to why educational science has lagged behind other learning sciences, and creates an interesting invitation to do so now.

After considering the results, this study suggests a new research question: Can the learning sciences catalyze a change in educational policy priorities? Which is explained below.

Can the learning sciences catalyze a change in educational policy priorities?

Educational leaders and learning scientists both contribute to the improvement of educational objectives in society but do so from distinct perspectives due to their academic formations and accompanying heuristics. Educational science is a sub-field of the learning sciences, yet few educators consider themselves scientists in this respect. The results of this study suggest that if educational leaders thought more like other learning scientists, priorities in educational policy decisions would change to privilege the important quality indicators in education rather than easily measurable or less costly indicators of student learning outcomes.

Some promising examples have been seen in international schools which have the luxury of applying learning science findings into real school settings. Punahou School's educational leadership team in Hawaii, for example, has made school policies on daily start-times, physical space and ergonomics, transdisciplinary thinking and mastery learning goals, and the planning of social-emotional integration policies a priority (McCaren, personal conversation May 2018) thanks to their learning science decision-making. The International School in Geneva has modified school policies in hopes of ensuring all education contributes to a life-long love of learning by articulating the ways "school" and "work" can and should be integrated (Hawley and David, personal conversation, June 2028). The International School of Bangkok has recently tried "flipping" their leadership meetings (sending video summaries before actually meeting) to leverage time for deeper and more personal exchanges and to make policy decisions after taking advantage of the social aspects of learning (Simmers, personal conversation, October 2019). A recent meta-analysis of K-12 schools that have moved to year-round schooling show modest but positively improved results ( Fitzpatrick and Burns, 2019). Glen Whitman at the Center For Transformative Teaching has been training teacher leaders to understand the principles of learning science and to incorporate them into real classroom settings. His school, St. Andrew's Episcopal, assures that "100% of their teachers are trained in Mind, Brain, and Education science" before working with students. The Kamehameha Schools in Hawaii have also recently undergone a 2-year induction into the learning sciences and are now basing policy decisions on the learning sciences (Wong, personal conversation September 2019), including the decision to incorporate early bilingual education (Fields, personal conversation, October 2019).

While these examples are of privileged school systems, they nonetheless point to encouraging examples of better learning science decision making into educational leadership practice. Many of the low to no cost policy changes which rely on new teacher training for professional development possibilities have already been adopted by some teacher colleges ( Deans for Impact, 2015) and thousands more educational leaders in the public school systems have been trained in conference formats such as Learning & the Brain and Mind, Brain, and Education ( Kelleher and Whitman, 2018) and through programs like "BrainU" at the University of Minnesota ( Dubinsky et al., 2013).

To reach the stage where educators begin to think more like learning scientists, several barriers will have to be overcome. Most importantly, access and integration of data from the learning scientists needs to be shared in a more systematic way with educators. Once available, this data must be communicated well to all actors in formal education. This in turn is dependent on broadening educational leaders' perspectives to include the semantics and heuristics of learning scientists, a choice left solely to the leaders themselves. It is also important to remember that the integration of the learning sciences alone will not cure all of the ills facing education. To respond more agilely to the challenges in education, a broader look across not only the learning sciences is needed, but also visionary perspectives from social and economic sciences as well.

Additional research will help advance the discussion around educational leaders in the learning science context. First, more research should be conducted to directly query actors about their views. How do educational leaders feel about the utility of the information coming from the other learning sciences? Do educational leaders consider that data from the learning sciences has a role to play in their decision-making processes, and if so, what is it? Second, there should be additional research as to the specific heuristics and semantics used in the different learning sciences to better understand the historical evolution of communication challenges. Third, more research is needed to identify additional areas that are of mutual concern to educational leaders and to learning scientists. Fourth, it is recommended that collaborative research efforts by educational leaders and learning scientists be conducted in order to cross-pollinate ideas for problem resolution. Such steps can potentially launch a shift in educational leadership and decision-making that is informed by the learning sciences, which would benefit both individuals and their communities.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material.

Author Contributions

TT-E design, applied, summarized, analyzed and wrote the study, and its findings.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author wishes to thank the following people for their time and energy toward the generation of this study, the lively debate and the on-going commitment to better educational practices. Thanks to the following for participating in the Delphi survey, offering to review findings, recommending panelists and/or for words of encouragement: Doris Alvarez, Eliot Berkman, Virginia Berninger, Jane Bernstein, Jeffrey Bowers, John T. Bruer, David Daniel, Bert DeSmedt, Stanislas Dehaene, Phil Fisher, Uta Frith, Howard Gardner, Walter Glannon, Ezequiel Gleichgerrcht, Usha Goswami, Ronald H. Grabner, Sonia Guerreiero, Mariale Hardinman, Takao Hensch, Paul Howard-Jones, Mary Helen Immordino-Yang, Jelle Jolles, Mary Layne Kalbfleisch, Johannes König, Iveta Kovalčíková, Abigail Larrison, Marilyn Leaske, Gigi Luk, Susan Magsamen, Pedro Maldonado, Richard Mayer, Bruce McCandliss, Renata Meneces, John Morrison, Eric Pakulak, Michael Posner, Sidarta Ribeiro, Vanessa Rodriguez, Todd Rose, Pankaj Sah, Daniel Schwartz, Jack Shonkoff, Beate Sodian, Elsbeth Stern, Michael Thomas, Nienke van Atteveldt, Judy Willis, and Patricia Wolfe. Thanks to Thomas Gorham, Julia Volkman, Juad Masters, Drew Nelson, Robert Murphy, and Curtis Kelly for their enthusiasm for this project and to Dee Rutgers for keeping me on a writing schedule.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2019.00136/full#supplementary-material

References

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The Learning Sciences Framework in Educational Leadership

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The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences.

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Abraham Maslow - Focuses on humanistic psychology and self-actualization 

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Abraham Maslow - Focuses on humanistic psychology and self-actualization 

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Abraham Maslow
1908 - 1970

"I was awfully curious to find out why I didn't go insane," remarked Abraham Maslow, one of the founders of humanistic psychology.

He was born and raised in Brooklyn, the eldest of seven children. He was smart but shy, and remembered his childhood as lonely and rather unhappy. Maslow attended City College in New York. His father hoped he would pursue law, but he went to graduate school at the University of Wisconsin to study psychology. While there, he married his cousin Bertha, and found as his chief mentor Professor Harry Harlow. At Wisconsin he pursued an original line of research, investigating primate dominance behavior and sexuality. He went on to further research at Columbia University, continuing similar studies. He found another mentor in Alfred Adler, one of Freud's early followers.

From 1937 to 1951, Maslow was on the faculty of Brooklyn College. In New York he found two more mentors, anthropologist Ruth Benedict and Gestalt psychologist Max Wertheimer, whom he admired both professionally and personally. These two were so accomplished in both realms, and such "wonderful human beings" as well, that Maslow began taking notes about them and their behavior. This would be the basis of his lifelong research and thinking about mental health and human potential. He wrote extensively on the subject, borrowing ideas from other psychologists but adding significantly to them, especially the concepts of a heirarchy of needs, metaneeds, self-actualizing persons, and peak experiences. Maslow became the leader of the humanistic school of psychology that emerged in the 1950s and 1960s, which he referred to as the "third force" -- beyond Freudian theory and behaviorism.

Maslow saw human beings' needs arranged like a ladder. The most basic needs, at the bottom, were physical -- air, water, food, sex. Then came safety needs -- security, stability -- followed by psychological, or social needs -- for belonging, love, acceptance. At the top of it all were the self-actualizing needs -- the need to fulfill oneself, to become all that one is capable of becoming. Maslow felt that unfulfilled needs lower on the ladder would inhibit the person from climbing to the next step. Someone dying of thirst quickly forgets their thirst when they have no oxygen, as he pointed out. People who dealt in managing the higher needs were what he called self-actualizing people. Benedict and Wertheimer were Maslow's models of self-actualization, from which he generalized that, among other characteristics, self-actualizing people tend to focus on problems outside of themselves, have a clear sense of what is true and what is phony, are spontaneous and creative, and are not bound too strictly by social conventions.

Peak experiences are profound moments of love, understanding, happiness, or rapture, when a person feels more whole, alive, self-sufficient and yet a part of the world, more aware of truth, justice, harmony, goodness, and so on. Self-actualizing people have many such peak experiences.

Maslow's thinking was surprisingly original -- most psychology before him had been concerned with the abnormal and the ill. He wanted to know what constituted positive mental health. Humanistic psychology gave rise to several different therapies, all guided by the idea that people possess the inner resources for growth and healing and that the point of therapy is to help remove obstacles to individuals' achieving this. The most famous of these was client-centered therapy developed by Carl Rogers.

Maslow was a professor at Brandeis University from 1951 to 1969, and then became a resident fellow of the Laughlin Institute in California. He died of a heart attack in 1970.

"Human nature is not nearly as bad as it has been thought to be."

Related Features

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A Science Odyssey: People and Discoveries: Abraham Maslow

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Abraham Maslow1908 - 1970 "I was awfully curious to find out why I didn't go insane," remarked Abraham Maslow, one of the founders of humanistic psychology. He was born and raised in Brooklyn, the eldest of seven children. He was smart but shy, and remembered his childhood as lonely and rather unhappy.

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Maslow’s hierarchy of needs is a motivational theory in psychology comprising a five-tier model of human needs, often depicted as hierarchical levels within a pyramid.

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Highlights critical thinking needed to address complex challenges; preparing student in the 21st century; policy intiatives

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Memory is the ongoing process of information retention over time. Because it makes up the very framework through which we make sense of and take action within the present, its importance goes without saying. But how exactly does it work? And how can teachers apply a better understanding of its inner…

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Disclaimer

The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here.

This model highlights just a few of the key brain regions involved in different memory functions. Since no part of the brain exists in isolation, most types of memory include more areas than the ones listed here. The big idea to take away is that different areas of the brain have different functions.

If you wish to delve more deeply into the types of memory or the brain anatomy involved, we encourage you to explore the references linked at the bottom of the page.

When we learn, our brain takes in information about the world that causes us to change our behavior in some way.

For example, a young baby doesn't understand that fire can hurt them. But thanks to learning, if they touch a candle flame even one time, they will probably not do it again. The information that feeds into the brain-candle, flame, hot, pain-is physically recorded in the connections between cells. And those connections influence our future behavior-don't touch!

As the brain takes in any information and stores it, there are physical changes to the connections between brain cells. And those connections influence our behavior.

Neurons that fire together wire together

The brain is an enormous interconnected network of specialized cells called neurons and supporting cells called glia. To transmit information in the brain, neurons pass signals to one another. This passing of signals can cause physical changes to the connections between cells.

When we learn a skill (like riding a bike) or new information (like multiplication tables), we are building brain connections. The more we use those brain connections, the stronger they get. It's the reason "practice makes perfect."

In the activity above, you can read about how injuries to certain brain areas cause certain memory functions to stop working. But just as important are the connections between them. If two brain areas can no longer communicate, then learning and memory can break down.

Like trails through the wilderness, brain connections get stronger the more they're used

Learning is a process that relies on long-term memory systems in the brain. People who study learning typically divide it into 4 steps: attention, storage, consolidation, and retrieval. These steps dovetail with the Types of Memory described above, and they provide a slightly different framework for thinking about what happens when one or more of these steps go wrong.

Attention - Our sensory systems are constantly bombarded with information, yet most of it fades before we are even aware of it. Attention is like a filter that lets important information into the brain while keeping the irrelevant information out. Without attention, we would remember very little.

Encoding - As sensory information comes in, the brain processes it. Strings of sounds become words, flashes of light and color become images. The brain begins connecting this new information with what it already knows about the world. Some information that is encoded will fade, and some will stick around.

Storage - Long-term storage of information requires new brain connections. The brain has its own way of rehearsing experiences (there's evidence that this is what dreaming is all about), which helps strengthen brain connections. But we can also consciously repeat information or skills to help the brain form strong memories.

Retrieval - Retrieval is the process of accessing stored memories. We use it when we recall a fact or reimagine an experience. But we also use it when we take in new information. It's part of what helps us connect new information to old.

Even when memory systems function normally, memory is imperfect. To learn more about the fallibility of memory, visit The Seven Sins of Memory.

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The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here. This model highlights just a few of the key brain regions involved in different memory functions.

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"Neurons that fire together wire together"

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"Neurons that fire together wire together"

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Teaching Strategies: Cognitive Load Theory

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Understanding how we learn and the science behind the brain influences the types of systems we create (think: schools and organizations), and the conversations we have with our students, our children and one another.

Consider this: If a child does not have a nurturing and supportive environment during his or her first two years, there are profound effects on the young brain that can last a lifetime.

If you are a brain science junky (or need an entry point to become one), the new PBS Show The Brain With Dr. David Eagleman is exactly what you have been waiting for. The six one hour episodes share the "inner workings of the brain, taking viewers on a visual journey into why they feel and think the things they do." The show is hosted by Dr. David Eagleman , neuroscientist, New York Times best-selling author and a Guggenheim Fellow.

Eagleman tells the human story of the brain by blending neuroscience and storytelling with visual effects. (And because I had a sneaky preview, I can say that the visual effects are amazing). :

"With barely a brain scanner or a white coat in sight, THE BRAIN focuses on understanding the fundamental truths of what it means to be human now and in the coming centuries, while communicating these elegant and simple ideas as they apply to us and our experiences."

The series explores the neural landscape by answering and contextualizing questions on why we're driven to certain behaviors, why we seek individuality, and "what is reality?"

I chatted with Dr. Eagleman to learn what new and emerging brain science means for students, teachers and parents.

Neuroplasticity Means We Are Always Changing

Neuroplasticity is as, " changes in neural pathways and synapses due to changes in behavior, environment, neural processes, thinking, and emotions - as well as to changes resulting from bodily injury. The concept of neuroplasticity has replaced the formerly-held position that the brain is a physiologically static organ, and explores how - and in which ways - the brain changes in the course of a lifetime."

Anyone who works with children and/or has children has probably seen this in action. When asked what parents need to know about young brains, Dr. Eagleman said, "One of my big interests is in neuroplasticity. Brains are rewriting themselves all the time. As a parent, this both terrifies me and liberates me. Every single thing I am doing next is shaping their brain and crafting who they are and who they have the capacity to become. It's a lot of responsibility. There is a deep complexity of the system and my job is to be a good parent. The best a parent can do is open doors and make sure all the educational opportunities are there."

Dr. Eagleman also added that the brain arrives in the world unfinished, and the job of experience is to further wire the brain. Our children's brains of course need love and a nurturing environment. In the episode I watched prior to our interview, titled "What Makes Me", I saw the story of young Romanian children who were living in an orphanage and had very little interaction with adults. When the children were finally adopted at the age of four, the long lasting impact of neglect and little language has impacted their ability to learn throughout their lives (those children are now adults living in the United States, and their story is profiled in the series).

Digital Natives Expect to Engage

Dr. Eagleman and I discussed the impact that technological change is having on the brain, and particularly on young learners. This is of interest to me because of my own children and also because of the changes that are happening (and more that needs to happen) to change the ways we engage with our young people in learning. Dr. Eagleman said, "We are in a rapidly changing moment in time because of digital natives. Educators have to understand that with this next generation they are used to [a fast] pace and interactive learning," Children who cannot pay attention in class in a typical "sit and get" environment do not necessarily have ADHD. In fact, they simply have a different set of expectations for how they learn.

Activate Interests to Learn

So, the ways in which we are learning are rapidly changing, and the most significant impact may be on digital natives, loosely as children born in the age of digital technology. By 2020, Mark Anderson of Strategic News Corporation and technology enthusiast, futurist and advisor to major businesses and philanthropists, most people will be connected and coined the term AORTA (Always On RealTime Access).

I asked Dr. Eagleman what the pace of technological change means for learnings. He said, "Teachers are in a position to make learning interactive and engaging. This is a much better way for the brain to learn and discover by interaction. We are good with young children, but by the time we get to older kids- we expect kids to listen to powerpoint lessons. What the brain fundamentally wants and especially in this generation is to learn by exploration, to solve problems that are engaging and interesting to them."

When explaining the science behind learning, Dr. Eagleman said, "What's happening in the brain, so it learns something and remembers something - you need a particular cocktail of chemicals and neurotransmitters. This observation goes back to the ancient Greeks. The highest level of learning is when you are curious about something. Curiosity about something equals those chemicals being present. So, in order to have kids learn whatever topic you are trying to teach they have to be wrapped in a way to engage and create curiosity. Stories and narrative and that's always an effective technique, interact and pose questions and solve something that is mysterious and interesting."

Technology Can Make Us Smarter

On the role technology can play in learning and the brain, Dr. Eagleman remarked that he's a cyper optimist. He cited Ted Talks, Wikipedia and adaptive learning software that create the potential for this next generation to be smarter. "There's a million ways that you can have interactive knowledge- graph, slider bars, interacting w/ the data. This is the kind of thing that previous generations didn't had the opportunity to do. We all had 'just in case' education- we learned all kinds of things; now we have 'just in time' education- which is very fruitful. Students now can have a higher level of engagement when an answer comes in context with the questions. The answer comes right away-it's coming at the right time while they're are curious about their topic."

For more on learning science, see:

Tweet your favorite quote from Dr. Eagleman @DavidEagleman and #TheBrain and share with us @Getting_Smart. The Brain with David Eagleman premieres in October on PBS Stations. Check your local listings and follow on Twitter @DavidEagleman and .
Stay in-the-know with all things EdTech and innovations in learning by signing up to receive the weekly Smart Update

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We are Wired to Learn, Change and Engage: The Brain with Dr. David Eagleman

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Understanding how we learn and the science behind the brain influences the types of systems we create (think: schools and organizations), and the conversations we have with our students, our children and one another.

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Many students start college using the study strategies they used in high school, which is understandable-the strategies worked in the past, so why wouldn't they work now? As you may have already figured out, college is different. Classes may be more rigorous (yet may seem less structured), your reading load may be heavier, and your professors may be less accessible. For these reasons and others, you'll likely find that your old study habits aren't as effective as they used to be. Part of the reason for this is that you may not be approaching the material in the same way as your professors. In this handout, we provide information on Bloom's Taxonomy-a way of thinking about your schoolwork that can change the way you study and learn to better align with how your professors think (and how they grade).

Why higher order thinking leads to effective study

Most students report that high school was largely about remembering and understanding large amounts of content and then demonstrating this comprehension periodically on tests and exams. Bloom's Taxonomy is a framework that starts with these two levels of thinking as important bases for pushing our brains to five other higher order levels of thinking-helping us move beyond remembering and recalling information and move deeper into application, analysis, synthesis, evaluation, and creation-the levels of thinking that your professors have in mind when they are designing exams and paper assignments. Because it is in these higher levels of thinking that our brains truly and deeply learn information, it's important that you integrate higher order thinking into your study habits.

The following categories can help you assess your comprehension of readings, lecture notes, and other course materials. By creating and answering questions from a variety of categories, you can better anticipate and prepare for all types of exam questions. As you learn and study, start by asking yourself questions and using study methods from the level of remembering. Then, move progressively through the levels to push your understanding deeper-making your studying more meaningful and improving your long-term retention.

Level 1: Remember

This level helps us recall foundational or factual information: names, dates, formulas, definitions, components, or methods.

Level 2: Understand

Understanding means that we can explain main ideas and concepts and make meaning by interpreting, classifying, summarizing, inferring, comparing, and explaining.

Level 3: Apply

Application allows us to recognize or use concepts in real-world situations and to address when, where, or how to employ methods and ideas.

Level 4: Analyze

Analysis means breaking a topic or idea into components or examining a subject from different perspectives. It helps us see how the "whole" is created from the "parts." It's easy to miss the big picture by getting stuck at a lower level of thinking and simply remembering individual facts without seeing how they are connected. Analysis helps reveal the connections between facts.

Level 5: Synthesize

Synthesizing means considering individual elements together for the purpose of drawing conclusions, identifying themes, or determining common elements. Here you want to shift from "parts" to "whole."

Level 6: Evaluate

Evaluating means making judgments about something based on criteria and standards. This requires checking and critiquing an argument or concept to form an opinion about its value. Often there is not a clear or correct answer to this type of question. Rather, it's about making a judgment and supporting it with reasons and evidence.

Level 7: Create

Creating involves putting elements together to form a coherent or functional whole. Creating includes reorganizing elements into a new pattern or structure through planning. This is the highest and most advanced level of Bloom's Taxonomy.

Pairing Bloom's Taxonomy with other effective study strategies

While higher order thinking is an excellent way to approach learning new information and studying, you should pair it with other effective study strategies. Check out some of these links to read up on other tools and strategies you can try:

Other UNC resources

If you'd like some individual assistance using higher order questions (or with anything regarding your academic success), check out some of your UNC resources:

Academic Coaching: Make an appointment with an academic coach at the Learning Center to discuss your study habits one-on-one.

Office Hours: Make an appointment with your professor or TA to discuss course material and how to be successful in the class.

Works consulted

Anderson, L. W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Wittrock, M.C (2001). A taxonomy of learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. New York, NY: Longman.

"Bloom's Taxonomy." University of Waterloo. Retrieved from https://uwaterloo.ca/centre-for-teaching-excellence/teaching-resources/teaching-tips/planning-courses-and-assignments/course-design/blooms-taxonomy

"Bloom's Taxonomy." Retrieved from http://www.bloomstaxonomy.org/Blooms%20Taxonomy%20questions.pdf

Overbaugh, R., and Schultz, L. (n.d.). "Image of two versions of Bloom's Taxonomy." Norfolk, VA: Old Dominion University. Retrieved from https://www.odu.edu/content/dam/odu/col-dept/teaching-learning/docs/blooms-taxonomy-handout.pdf

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.
You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Learning Center, University of North Carolina at Chapel Hill

If you enjoy using our handouts, we appreciate contributions of acknowledgement.
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Higher Order Thinking: Bloom's Taxonomy - Learning Center

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Many students start college using the study strategies they used in high school, which is understandable-the strategies worked in the past, so why wouldn't they work now? As you may have already figured out, college is different. Classes may be ... Read more

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Learning Outcomes

Learning outcomes are what you want your students to learn, either as a result of a specific lesson or on the grander, more general scale of the entire course. Learning outcomes target knowledge, skills, or attitudes for change. Bloom's taxonomy specifically targets these by seeking to increase knowledge (cognitive domain), develop skills (psychomotor domain), or develop emotional aptitude or balance (affective domain). Learning outcomes might be identified by someone outside the teacher, such as state-wide or departmental standards. The taxonomy provides a basis for developing sub-goals and assessment methodology to meet these goals. It is important to note that learning outcomes are goals, and are not the activities performed to achieve those goals. Outcomes can be categorized into broad, global outcomes that may take many years to achieve and provide direction for education, educational goals that guide curriculum development over the weeks or months it takes to complete a specific course, and instructional goals that narrowly focus on the daily activities, experiences, and exercises used in a specific lesson plan.

Objective Builder Tool

For applications specific to a course, the Center for Distributed Learning has developed an objective builder tool to help craft the language for measurable learning outcomes.

Applications of the Taxonomy

Taxonomies are developed to provide a framework for organizing a continuum along an underlying structure. For example, languages may be classified as Romantic, Germanic, etc. based on their underlying grammatical structure and origin. Bloom's taxonomy primarily provides instructors with a focus for developing their course learning outcomes. There are a number of reasons why a teacher would want to use Bloom's taxonomy. Initially, it can be used to increase one's understanding of the educational process. Teachers can see and understand complex cognitive development and how lower-level skills build into higher-order thinking (e.g., recalling facts and comprehending previous problems allows a student to apply their experience to similar problems). Using this understanding facilitates the prioritizing of material and can steer the organization of lessons to maximize class time. For example, lower-level skills (e.g., memorizing factual knowledge) can be developed before higher-level skills are introduced (e.g., analysis of relationships). Current educators frequently face a confusing array of standards and curriculum requirements. Bloom's taxonomy offers a guiding framework for breaking these criteria down into accessible chunks which can be used to direct day-to-day lesson plans and can be easily compared to their own goals for the class. Just as different levels require different instructional delivery methods, they also require different assessment methods. Bloom's taxonomy can be used as a checklist to ensure that all levels of a domain have been assessed and align assessment methods with the appropriate lessons and methodologies. In this way, the taxonomy also makes it easier for you to maintain consistency between assessment methods, content, and instructional materials and identify weak areas.

Update to Bloom's Taxonomy (2001)

Originally, Bloom's taxonomy was one-dimensional with an exclusive focus on the knowledge domain. The current updated version developed by Anderson and Krathwohl (2001) reorganizes, and highlights the interactions between, two dimensions: cognitive processes and knowledge content. Anderson and Krathwohl identify two reasons for updating the original handbook. They emphasize a refocusing of educational outcomes back to the original handbook, which was ahead of its time and can still offer assistance to modern educators and to incorporate new findings in psychology and education into the framework. In their revision, cognitive processes are presented as verbs and the knowledge content are presented as nouns. Along with exchanging the levels of Evaluation and Synthesis (which they rename to Creation), Anderson and Krathwohl redefine the knowledge dimension to include four types:

Factual Knowledge: Basic elements of a discipline that a student must know and be able to work with to solve problems including basic terminology and specific details and elements.

Conceptual Knowledge: Interrelationships between basic factual knowledge that demonstrate how elements work together, for example, classifications and categories, principles and generalizations, and theories, models, and structures.

Procedural Knowledge: How something is done including the methods of inquiry, skills, algorithms, techniques, and methods needed to investigate, apply, or analyze information.

Metacognitive Knowledge: Awareness and knowledge of one's own cognition including strategies for learning, contextual and conditional knowledge about cognitive tasks, and self-knowledge.

References:
Bloom, B. S. (1969). Taxonomy of educational objectives: The classification of educational goals : Handbook I, Cognitive domain. New York: McKay.
Anderson, L. W., Krathwohl, D. R., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. New York: Longman.

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IntroductionBloom's taxonomy was developed to provide a common language for teachers to discuss and exchange learning and assessment methods. Specific learning outcomes can be derived from the taxonomy, though it is most commonly used to assess learning on a variety of cognitive levels. The table below defines each cognitive level...

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Benjamin Bloom - "developed a "taxonomy of educational objectives"

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Benjamin Bloom - "developed a "taxonomy of educational objectives"

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Benjamin Bloom (February 21, 1913 - September 13, 1999) was an American educational psychologist who made significant contributions to the classification of educational objectives and the theory of mastery learning. His research, which showed that educational settings and home environments can foster human potential, transformed education. Bloom developed a "taxonomy of educational objectives" which classified the different learning objectives and skills that educators set for students. Bloom divided educational objectives into three "domains:" Affective, Psychomotor, and Cognitive. It is hierarchical, like other taxonomies, meaning that learning at the higher levels is dependent on having attained prerequisite knowledge and skills at lower levels. Bloom intended that the Taxonomy motivate educators to focus on all three domains, creating a more holistic form of education.

Bloom also carried out significant research on mastery learning, showing that it is not innate giftedness that allows one to succeed, but rather hard work. His studies showed that the most successful in their fields all put in at least ten years of dedicated effort before achieving significant recognition. Bloom's work stressed that attainment was a product of learning, and learning was influenced by opportunity and effort. It was a powerful and optimistic conception of the possibilities that education can provide, and one that Bloom was able to bring into practice. Based on his efforts, evaluation methods and concepts were radically changed. His activism also supported the creation of the Head Start program that provides support to pre-school age children of low-income families, giving them opportunities to begin a life of learning and consequent achievement. However, his research led him to realize that early experiences within the family are the most significant in providing a good foundation for learning.

Benjamin S. Bloom was born on February 21, 1913, in Lansford, Pennsylvania.

As a youth, Bloom had an insatiable curiosity about the world. He was a voracious reader and a thorough researcher. He read everything and remembered well what he read. As a child in Lansford, Pennsylvania, the librarian would not allow him to return books that he had checked out earlier that same day until he was able to convince her that he had, indeed, read them completely.

Bloom was especially devoted to his family (his wife, Sophie, and two sons), and his nieces and nephews. He had been a handball champion in college and taught his sons both handball and Ping-Pong, chess, how to compose and type stories, as well as to invent.

He received a bachelor's and master's degree from Pennsylvania State University in 1935, and a Ph.D. in Education from the University of Chicago in March 1942. He became a staff member of the Board of Examinations at the University of Chicago in 1940 and served in that capacity until 1943, at which time he became university examiner, a position he held until 1959.

He served as educational adviser to the governments of Israel, India, and numerous other nations.

What Bloom had to offer his students was a model of an inquiring scholar, someone who embraced the idea that education as a process was an effort to realize human potential, and even more, it was an effort designed to make potential possible. Education was an exercise in optimism. Bloom's commitment to the possibilities of education provided inspiration for many who studied with him.

Benjamin Bloom died Monday, Sept. 13, 1999 in his home in Chicago. He was 86.

Benjamin Bloom was an influential academic educational psychologist. His main contributions to the area of education involved mastery learning, his model of talent development, and his Taxonomy of Educational Objectives in the cognitive domain.

He focused much of his research on the study of educational objectives and, ultimately, proposed that any given task favors one of three psychological domains: Cognitive, affective, or psychomotor. The cognitive domain deals with the ability to process and utilize (as a measure) information in a meaningful way. The affective domain is concerned with the attitudes and feelings that result from the learning process. Lastly, the psychomotor domain involves manipulative or physical skills.

Bloom headed a group of cognitive psychologists at the University of Chicago who developed a taxonomic hierarchy of cognitive-driven behavior deemed to be important to learning and measurable capability. For example, an objective that begins with the verb "describe" is measurable but one that begins with the verb "understand" is not.

His classification of educational objectives, Taxonomy of Educational Objectives, Handbook 1: Cognitive Domain, published in 1956, addresses cognitive domain versus the psychomotor and affective domains of knowledge. It was designed to provide a more reliable procedure for assessing students and the outcomes of educational practice. Bloom's taxonomy provides structure in which to categorize instructional objectives and instructional assessment. His taxonomy was designed to help teachers and Instructional Designers to classify instructional objectives and goals. The foundation of his taxonomy was based on the idea that not all learning objectives and outcomes are equal. For example, memorization of facts, while important, is not the same as the learned ability to analyze or evaluate. In the absence of a classification system (a taxonomy), teachers and Instructional Designers may choose, for example, to emphasize memorization of facts (which make for easier testing) than emphasizing other (and likely more important) learned capabilities.

Taxonomy of educational objectives

Bloom's Taxonomy of Educational Objectives is a classification of the different objectives and skills that educators set for students (learning objectives). Bloom divided educational objectives into three "domains:" Affective, Psychomotor, and Cognitive. This taxonomy is hierarchical, meaning that learning at the higher levels is dependent on having attained prerequisite knowledge and skills at lower. Bloom intended that the Taxonomy motivate educators to focus on all three domains, creating a more holistic form of education.

Skills in the affective domain describe the way people react emotionally and their ability to feel another living thing's pain or joy. Affective objectives typically target the awareness and growth in attitudes, emotion, and feelings. There are five levels in the affective domain moving through the lowest order processes to the highest:

Receiving

The lowest level; the student passively pays attention. Without this level no learning can occur.

Responding

The student actively participates in the learning process, not only attends to a stimulus, the student also reacts in some way.

Valuing

The student attaches a value to an object, phenomenon, or piece of information.

Organizing

The student can put together different values, information, and ideas and accommodate them within his/her own schema; comparing, relating, and elaborating on what has been learned.

Characterizing

The student has held a particular value or belief that now exerts influence on his/her behavior so that it becomes a characteristic.

Psychomotor

Skills in the psychomotor domain describe the ability to physically manipulate a tool or instrument like a hand or a hammer. Psychomotor objectives usually focus on change and/or development in behavior and/or skills.

Bloom and his colleagues never created subcategories for skills in the psychomotor domain, but since then other educators have created their own psychomotor taxonomies. For example, Harrow wrote of the following categories:

Reflex movements

Reactions that are not learned.

Fundamental movements

Basic movements such as walking, or grasping.

Perception

Response to stimuli such as visual, auditory, kinesthetic, or tactile discrimination.

Physical abilities

Stamina that must be developed for further development such as strength and agility.

Skilled movements

Advanced learned movements as one would find in sports or acting.

No discursive communication

Effective body language, such as gestures and facial expressions.

Skills in the cognitive domain revolve around knowledge, comprehension, and "thinking through" a particular topic. Traditional education tends to emphasize the skills in this domain, particularly the lower-order objectives. There are six levels in the taxonomy, moving through the lowest order processes to the highest:

Knowledge

Exhibit memory of previously-learned materials by recalling facts, terms, basic concepts and answers

Knowledge of specifics-terminology, specific facts

Knowledge of ways and means of dealing with specifics-conventions, trends and sequences, classifications and categories, criteria, methodology

Knowledge of the universals and abstractions in a field-principles and generalizations, theories and structures

Comprehension

Demonstrative understanding of facts and ideas by organizing, comparing, translating, interpreting, giving descriptions, and stating main ideas

Application

Using new knowledge. Solve problems to new situations by applying acquired knowledge, facts, techniques, and rules in a different way

Analysis

Examine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations

Analysis of elements

Analysis of relationships

Analysis of organizational principles

Synthesis

Compile information together in a different way by combining elements in a new pattern or proposing alternative solutions

Production of a unique communication

Production of a plan, or proposed set of operations

Derivation of a set of abstract relations

Evaluation

Present and defend opinions by making judgments about information, validity of ideas or quality of work based on a set of criteria

Judgments in terms of internal evidence

Judgments in terms of external criteria

Some critics of Bloom's Taxonomy's (cognitive domain) admit the existence of these six categories, but question the existence of a sequential, hierarchical link. Also, the revised edition of Bloom's taxonomy moved Synthesis to a higher position than Evaluation. Some consider the three lowest levels as hierarchically ordered, but the three higher levels as parallel. Others say that it is sometimes better to move to Application before introducing Concepts. This thinking would seem to relate to the method of Problem Based Learning.

Studies in early childhood

In 1964, Bloom published Stability and Change in Human Characteristics. That work, based on a number of longitudinal studies, led to an upsurge of interest in early childhood education, including the creation of the Head Start program. He was invited to testify to the Congress of the United States about the importance of the first four years of the child's life as the critical time to promote cognitive development. His testimony had an impact in promoting and maintaining funding for this program. He argued that human performance was often a reflection of social privilege and social class. Children who enjoyed the benefits of habits, attitudes, linguistic skills, and cognitive abilities available to the more privileged members of society were likely to do well at school. To confer additional privileges on those who already had a head start was to create an array of inequities that would eventually exact extraordinary social costs. He further stated that since environment plays such an important role in providing opportunity to those already privileged, it seemed reasonable to believe that by providing the kind of support that the privileged already enjoyed to those who did not have it, a positive difference in their performance would be made.

Bloom showed that many physical and mental characteristics of adults can be predicted through testing done while they are still children. For example, he demonstrated that 50 percent of the variations in intelligence at age 17 can be estimated at age four. He also found that early experiences in the home have a great impact on later learning, findings that caused him to rethink the value of the Head Start program.

Bloom summarized his work in a 1980 book titled, All Our Children Learning, which showed from evidence gathered in the United States and abroad that virtually all children can learn at a high level when appropriate practices are undertaken in the home and school.

In the later years of his career, Bloom turned his attention to talented youngsters and led a research team that produced the book, Developing Talent in Young People, published in 1985.

Mastery learning

In 1985, Bloom conducted a study suggesting that at least ten years of hard work (a "decade of dedication"), regardless of genius or natural prodigy status, is required to achieve recognition in any respected field. This shows starkly in Bloom's 1985 study of 120 elite athletes, performers, artists, biochemists, and mathematicians. Every single person in the study took at least a decade of hard study or practice to achieve international recognition. Olympic swimmers trained for an average of 15 years before making the team; the best concert pianists took 15 years to earn international recognition. Top researchers, sculptors, and mathematicians put in similar amounts of time.

Bloom's research on giftedness undermines its typical conception. Giftedness typically connotes the possession of an ability that others do not have. A gift suggests something special that is largely the result of a genetically conferred ability. While Bloom recognized that some individuals had remarkable special abilities, the use of such a model of human ability converted the educators' role from inventing ways to optimize human aptitude into activities mainly concerned with matters of identification and selection. The latter process was itself predicated on the notion that cream would rise to the top. The educator's mission, Bloom believed, was to arrange the environmental conditions to help realize whatever aptitudes individuals possessed. Bloom discovered that all children can learn at a high level when appropriate practice, attention, and support are undertaken in the home and school. Champion tennis players, for example, profited from the instruction of increasingly able teachers of tennis during the course of their childhood. Because of this and the amount of time and energy they expended in learning to play championship tennis, they realized goals born of guidance and effort rather than raw genetic capacity. Attainment was a product of learning, and learning was influenced by opportunity and effort. It was a powerful and optimistic conception of the possibilities that education can provide.

Bloom's message to the educational world was to focus on target attainment and to abandon a horse-race model of schooling that has as its major aim the identification of those who are swiftest. Speed is not the issue, he argued, achievement or mastery is, and it is that model that should be employed in trying to develop educational programs for the young. Mastery learning was an expression of what Bloom believed to be an optimistic approach to the realization of educational goals. When well implemented, approximately 80 percent of the students in mastery learning classes earned As and Bs, compared with only 20 percent in control classes.

Some of the effects of mastery learning include:

Increased student self-assurance

Reduced competition and encouraged cooperation among students;

that is, students were enabled to help one another

Assessments as learning tools rather than official grades

Second chance at success for students

Bloom was considered a world guru of education. He was first involved in world education when the Ford Foundation sent him to India in 1957, to conduct a series of workshops on evaluation. This led to a complete revision of the examination system in India. It was also the beginning of his work as an educational adviser and consultant to countries around the world. He also served as educational adviser to the governments of Israel and numerous other nations. In the U.S. and abroad, Bloom was instrumental in shifting the instructional emphasis from teaching facts to teaching students how to use the knowledge they had learned. He revolutionized education through his thinking that, backed by significant research evidence, that what any person can learn, all can learn, except perhaps for the lowest one or two percent of students.

Bloom's scholarship in education was complemented by his activism. He played a major role in creating the International Association for the Evaluation of Educational Achievement (IEA) and in organizing the International Seminar for Advanced Training in Curriculum Development, held in Granna, Sweden, in the summer of 1971. His work in the IEA, since its inception over thirty years ago, has had a significant impact on the efforts being made internationally to improve students' learning in the dozens of countries that are members of the IEA.

In the Department of Education at the University of Chicago, he developed the MESA (Measurement, Evaluation, and Statistical Analysis) program. This program was designed to prepare scholars who had the quantitative and analytical skills to think through in great depth what needed to be addressed in order to design genuinely informative and educationally useful evaluation practices. His commitment to the possibilities and potential of education as an exercise in optimism infused his views about how young scholars should be prepared in the field of evaluation. He also served as chairman of the research and development committees of the College Entrance Examinations Board and was elected President of the American Educational Research Association in 1965. Scholars recognized the stature of this extraordinary man and honored him with appointments, honorary degrees, medals, and election to office. Elliot W. Eisner wrote of Benjamin Bloom:

The field of education, and more important, the lives of many children and adolescents are better off because of the contributions he made.

Major publications

Bloom, Benjamin S. 1956. Taxonomy of Educational Objectives, Handbook 1: Cognitive Domain. Addison-Wesley Publishing Company. ISBN 978-0582280106

Bloom, Benjamin S. 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals. Longman. ISBN 978-0679302094

Bloom, Benjamin S. 1980. All Our Children Learning. New York: McGraw-Hill. ISBN 9780070061187

Bloom, B. S., & Sosniak, L.A. 1985. Developing Talent in Young People. New York: Ballantine Books. ISBN 9780345319517

1. Elliot W. Eisner, "Benjamin Bloom," Prospects: The Quarterly Review of Comparative Education (Paris, UNESCO: International Bureau of Education, vol. XXX, no. 3, September 2000).

2. www.nwlink.com, Bloom's Taxonomy Retrieved December 23, 2007.

3. Anita Harrow, A Taxonomy of Psychomotor Domain: A Guide for Developing Behavioral Objectives (New York: David McKay, 1972).

4. R. Paul, Critical Thinking: What every Person Needs to Survive in a Rapidly Changing World (Rohnert Park, CA: Sonoma State University Press, 1993).

5. David Dobbs, How to be a genius. The New Scientist Online Magazine, Sept. 15, 2005. Retrieved December 23, 2007.

6. T. R. Guskey, Benjamin S. Bloom Portraits of an Educator (Lanham, MD: Rowman & Littlefield Education, 2005, ISBN 1578862434).

References

Anderson, Lorin W., and David R. Krathwohl (eds.). A Taxonomy for Learning, Teaching, and Assessing-A Revision of Bloom's Taxonomy of Educational Objectives. Allyn & Bacon, 2000. ISBN 978-0321084057

Guskey, T.R. Benjamin S. Bloom Portraits of an Educator. Lanham, MD: Rowman & Littlefield Education, 2005. ISBN 1578862434

Harrow, Anita. A Taxonomy of Psychomotor Domain: A Guide for Developing Behavioral Objectives. New York, NY: David McKay, 1972. ISBN 978-0679302124

Paul, R. Critical Thinking: What every Person Needs to Survive in a Rapidly Changing World. Rohnert Park, CA: Sonoma State University Press, 1993. ISBN 978-0944583074

External links

All links retrieved December 14, 2016.

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Benjamin Bloom (February 21, 1913 - September 13, 1999) was an American educational psychologist who made significant contributions to the classification of educational objectives and the theory of mastery learning. His research, which showed that educational settings and home environments can foster human potential, transformed education.

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This report arises from Science of Learning Fellowships funded by the International Brain Research Organization (IBRO) in partnership with the International Bureau of Education (IBE) of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The IBRO/IBE-UNESCO Science of Learning Fellowship aims to support and translate key neuroscience research on learning and the brain to educators, policy makers, and governments.
Executive summary
Our brain is plastic-which means the brain changes its connectivity and even its structure in response to learning.

Our first learning experiences are foundational for our later education, and experiences in the early years of life can greatly impact later achievement.

Waves of overproduction and pruning back of brain connections occur until late adolescence, making childhood a special time for learning.

Our biology does not set a defined limit to what we can achieve. The plasticity of a student's brain means the student and their teacher play an important role in constructing it.

Although younger brains are more plastic, our brains remain plastic throughout our lifetime, supporting our lifelong learning ability.

Understanding plasticity is important for students and teachers. Our brain shapes our learning, but learning shapes our brain.

What is plasticity?
Most of our entire stock of neurons (~86 billion) are created prior to our birth. After birth, huge changes in the connectivity between neurons occurs over childhood and beyond. These connectivity changes are an important example of what is referred to as the brain's plasticity-i.e., its ability to change. These changes are important from an educational point of view because, from the earliest years, there is an important two-way interaction between an individual's learning experiences and their brain's changing connectivity and structure. In other words, our brain shapes our ability to learn, and learning shapes our brain.

Two important types of plasticity have been identified ¹:

1 · Experience-expectant plasticity involves the overproduction of connections (or "synapses") between neurons in different regions of the brain around particular times during development. These connections are then organised and pruned back by expected or typical/common experiences. Both overproduction and pruning help "tune" basic functions during development, including vision, movement, language, and socio-emotional response (see Figure 1). The overproduction and pruning occur first in regions related to the most primary functions (e.g., vision and hearing). Brain regions that serve higher-order thinking processes develop more slowly, reaching their maximum number of connections at preschool, while their pruning back can continue in regions key for reasoning and learning late into the teenage years.

Deprived conditions in the early years can impact negatively in terms of experience-expectant plasticity. This was demonstrated by findings from the Bucharest Early Intervention Project. Here, children were randomly allocated to foster carers having suffered early institutional deprivation in Romania, which did not provide a species-typical experience in terms of sensory stimulation, touch, speech, and personal caregiving. Children formed insecure attachments, knew only a third of the number of words, and produced less brain activity than children reared in family environments ⁸⁻¹². There also appeared a time constraint, after which remediation of attachment and cognitive development by foster care was less likely (24 months)-which points to sensitive periods of development being involved.

Most of what we understand of sensitive periods relates to infancy, but given the structural changes occurring throughout childhood, later ones are likely 13,14.

2 · Experience-dependent plasticity involves the change or production of new connections based on experiences that would never be expected to be common to all children. These include culturally-specific skills, such as reading, writing, and mathematics. Sensitive periods are not usually associated with this type of plasticity. Plasticity has been studied in a region of the brain called the hippocampus (see Figure 2). This region should be of particular interest to educators since it has a crucial role in learning and memory. Education requires a vast amount of information to be committed to memory. This type of information first arrives at the hippocampus where it is converted into a form for storage in regions across the brain.

In short, scientific understanding of the role of sensitive periods is incomplete, and their role may be restricted to significant levels of disadvantage. Still, there is already a biological basis for emphasising the importance of a child's educational experience from the earliest years. This helps understand why early care, education, and experience appear so foundational to later learning and development. While academic and cognitive benefits of extra investment in the early years can sometimes fade with time, some long-term benefits can persist, such as improved rates of high school graduation and decreased teenage parenthood and criminality ²². From the early years, the quality of the learning experience is a major factor in determining later outcomes 23,24. Indeed, early economic models based on a "learning begets learning" principle (where learning leads to greater ability to learn) show earlier is generally better for investing in children's education ²⁵⁻²⁷. However, one should be careful with the simple idea "learning begets learning," since it can suggest that less is returned from those who have suffered the least investment. In reality, a consistent message from the research has emphasised the particular importance of preschool and early years education for the disadvantaged. Maturation of abilities occurs at different times during development ¹⁴, and more sophisticated economic models have attempted to reflect this. This research shows early investment still provides the greatest economic return, but it also confirms that this would be greatest for the least advantaged ²⁸. Interestingly, the research also shows the later adolescent years may benefit more from interventions aimed at noncognitive skills iii, which aligns with suggestions of a sensitive period during adolescence for identity formation ²⁹.
Why does the message of plasticity matter in education? Lifelong plasticity References

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6. Balas, B. & Stevenson, K. Children's neural response to contrast-negated faces is species specific. J. Exp. Child Psychol. 119, 73-86, doi:10.1016/j.jecp.2013.10.010 (2014).

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17. Lovden, M. et al. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiology of Aging 33, 14, doi:10.1016/j.neurobiolaging.2011.02.013 (2012).

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19. Woollett, K., Spiers, H. J. & Maguire, E. A. Talent in the taxi: a model system for exploring expertise. Philos. Trans. R. Soc. B-Biol. Sci. 364, 1407-1416, doi:10.1098/rstb.2008.0288 (2009).

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21. Martensson, J. et al. Growth of language-related brain areas after foreign language learning. Neuroimage 63, 240-244, doi:10.1016/j.neuroimage.2012.06.043 (2012).

22. Duncan, G. J. & Magnuson, K. Investing in preschool programs. J. Econ. Perspect. 27, 109-131, doi:10.1257/jep.27.2.109 (2013).

23. Lehrl, S., Kluczniok, K. & Rossbach, H. G. Longer-term associations of preschool education: The predictive role of preschool quality for the development of mathematical skills through elementary school. Early Childhood Res. Q. 36, 475-488, doi:10.1016/j.ecresq.2016.01.013 (2016).

24. Sylva, K., Melhuish, E., Sammons, P., Siraj-Blatchford, I. & Taggart, B. Effective pre-school education: A longitudinal study funded by the DfES (1997 - 2004). (Institute of Education, Nottingham, 2004).

25. Cunha, F. & Heckman, J. The technology of skill formation. American Economic Review 97, 31-47 (2007).

26. Heckman, J. J. Policies to foster human capital. Research in Economics 54, 3-56, doi:DOI: 10.1006/reec.1999.0225 (2000).

27. Heckman, J. J. Schools, skills, and synapses. Economic Inquiry 46, 289-324, doi:10.1111/j.1465-7295.2008.00163.x (2008).

28. Cunha, F., Heckman, J. J. & Schennach, S. M. Estimating the technology of cognitive and noncognitive skill formation. Econometrica 78, 883-931, doi:10.3982/ecta6551 (2010).

29. Blakemore, S. J. & Mills, K. L. Is adolescence a sensitive period for sociocultural processing? Annual Review of Psychology 65, 187-207 (2014).

30. Dekker, S. & Jolles, J. Teaching about "brain and learning" in high school biology classes: Effects on teachers' knowledge and students' theory of intelligence. Frontiers in Psychology 6, 8, doi:10.3389/fpsyg.2075.01848 (2015).

31. Paunesku, D. et al. Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science 26, 784-793, doi:10.1177/0956797615571017 (2015).

32. Blackwell, L. S., Trzesniewski, K. H. & Dweck, C. S. Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development 78, 246-263 (2007).

33. Howard-Jones, P. A., Franey, L., Mashmoushi, R. & Liao, Y.-C. The neuroscience literacy of trainee teachers. In British Educational Research Association Annual Conference (University of Manchester, 2009).

34. Pei, X., Howard-Jones, P., Zhang, S., Liu, X. & Jin, Y. Teachers' understanding about the brain in East China. Procedia - Social and Behavioral Sciences 174, 3681-3688, doi: http://dx.doi.org/10.1016/j.sbspro.2015.01.1091 (2015).

35. Bruer, J. The Myth of the First Three Years: A New Understanding of Early Brain Development and Lifelong Learning. (The Free Press, 1999).

36. Oliveira-Pinto, A. V. et al. Do age and sex impact on the absolute cell numbers of human brain regions? Brain Struct. Funct. 221, 3547-3559, doi:10.1007/s00429-015-1118-4 (2016).

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39. Then, F. S., Luck, T., Angermeyer, M. C. & Riedel-Heller, S. G. Education as protector against dementia, but what exactly do we mean by education? Age Ageing 45, 523-528, doi:10.1093/ageing/afw049 (2016).

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i Current knowledge of sensitive periods is mostly restricted to primary functions (sensory, motor, etc.) that develop early. There are no sensitive periods for maths, reading, etc.-these are complex abilities that draw on multiple types of basic cognitive function. Even the idea that sensitive periods are involved with age-of-acquisition effects for 2nd language learning is controversial. For example, this may be better considered as a reduction general plasticity of the brain and/or accumulation effects (i.e., the sooner you start the more you learn).

ii This process requires social interaction-it does not occur simply from, for example, having the TV or radio broadcast a foreign language.

iii Cognitive skills include abilities such as reasoning, memory, IQ, etc., while noncognitive skills include those more associated with traits such as self-motivation, attitude, sociability, etc.

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IBE - Science of learning portal - The plastic brain

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Scientists are discovering how our brain shapes our ability to learn, and how learning shapes our brain.

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The Power of Mindfulness: What You Practice Grows Stronger | Shauna Shapiro | TEDxWashingtonSquare

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If you really want to see how innovative a school is, inquire about its thinking and practices regarding assessment. For the students, does the mere thought of assessment trigger stress? Do the teachers rely heavily on high-stakes, multiple-choice, Bell Curve-generating tests? Or do the students seem relaxed and engaged as teachers experiment with new forms of assessment designed to support deep and lasting learning?

Clearly, there continues to be a place for traditional assessments of learning that prepare students for standardized tests such as ERBs, AP exams, SATs, and college midterm exams. However, we believe that independent schools, unrestrained from the chains of public education policies, can take on an important leadership role in transforming K-12 assessments from a mere measure of short-term learning to a crucial component of the teaching and learning process. In this system of assessment, learning becomes personalized, encouraging students to adopt a mindset focused on discovery and engagement rather than grades and test scores. Most important, in such a system, the sharp lines between teaching, learning, and testing diminish.

This shift in thinking about assessment is a central issue for all schools. As the research strongly suggests, when students focus on mastery of learning rather than on their performance on tests, they significantly increase their intrinsic motivation for learning.

New forms of assessment can be found in an increasing number of independent schools today. One example is in a 10th-grade United States History class at St. Andrew's Episcopal School (Maryland) taught by coauthor Glenn Whitman. Instead of offering students a typical final exam, Glenn offers them a choice: "What type of final exam would help you to best demonstrate what you have learned in this class?" Based on their reflection, students select the format of their final exam. The more traditional and familiar option allows students to sit for two hours and complete an array of multiple-choice, document-analysis, historical-geography, and free-response questions. The alternative option is to complete what Glenn calls the "Historian's Head" ¹ - a collage of images and scholarly narratives that respond to the essential questions of the course.

In any given year, 60 to 80 percent of the students choose the assessment that asks them to demonstrate their understanding through images and scholarly narratives - even though this alternative is more demanding when it comes to higher-order thinking and executive functioning skills. It is also more time intensive. So why do so many students choose this option?

Here is what a few representative students have said:

I function better on projects than on tests because I do not work well under time constraints. In a two-hour exam, I get too stressed and I take too long on one answer. With the project, I am not timed and therefore I will not be stressed.

My primary reason for creating a "head" over a traditional exam is because it will allow me to be creative and use my knowledge in a different way besides memorizing what I have to know. Also I will be able to dedicate more time to it than I would to studying for an exam.

I chose this option over the regular exam because it has an artistic element to it and we are able to spend more time on it that enables a process of deeper thinking in contrast to the quick memorization that would be necessary for the traditional fi nal.

The journey to designing this assessment has been long. Not surprising, when Glenn first began his independent school teaching career in 1991, his assessments looked a lot like those he experienced as a student. And while instinct and intuition, and student feedback, suggested that there must be a better way to measure student understanding, ultimately the growing body of research in educational neuroscience became the game changer for his 10th-grade history final exam.

The field of educational neuroscience is still quite young - converging developmental psychology, cognitive science, and education. While some of its initial findings merely support what educators have intuitively believed, it is also challenging many assumptions and providing new insight into best educational practices, especially regarding assessment.

What we do know is that a plethora of assessment-related research has shed light not just on the importance of students' mindsets, but also on the importance of continual feedback and how active retrieval of information (memorization recall), in carefully spaced intervals, can produce long-lasting learning. Research also shows that providing students with assessment choice enhances attention and engagement - and confirms that the arts can help deepen long-term memory consolidation. ² These latter points were instrumental in driving the changes to Glenn's 10th-grade history final exam.

Specifically, the following research is helping to change our understanding of the correlation between teaching and learning - and altering our approaches to student assessment.

Mindsets: Performance vs. Mastery Goals

Research on the connection between motivation and learning has focused on two types of mindsets that students develop, based on the kind of experiences (including assessments) we present them with in school. Students tend to develop either performance-related goals or mastery goals. ³

Performance-related goals are those that are linked to more traditional types of assessments. Students become motivated by the grades they achieve, their rankings compared with other students, and extrinsic rewards such as honor rolls or school awards. In contrast, students who develop mastery goals are motivated by the actual learning experiences. Their rewards arise from the challenges of acquiring and applying new knowledge and skills.

While students may possess a combination of both types of goals, those motivated primarily by performance goals tend to lose motivation and confidence when faced with difficult academic challenges or when set back by failures. In contrast, students who are motivated by mastery goals are more likely to persevere in the face of such challenges. Difficult tasks or setbacks do not diminish their motivation or self-esteem. ⁴ Students with a mastery mindset, similar to those students who described their experiences in the history class at St. Andrew's, are more likely to choose more difficult but rewarding ways to demonstrate learning.

Feedback on Performance

Another focal point of research is the timeliness and method of providing feedback to students. Timely feedback has been shown to deepen one's memory for the material assessed. ⁵ Moreover, in a study comparing immediate versus delayed feedback, the mere anticipation of timely feedback produced better performance. Students who knew that they would get immediate feedback performed better on a task than those who were told that feedback would be delayed. ⁶ Finally, studies suggest that marking answers right or wrong (as in multiple-choice tests) has little effect on learning. However, providing the correct response only after a student has spent time "struggling" to find the correct answer significantly increases retention of the material. ⁷

Researchers have also discovered that the spacing of assessments and feedback produces more effective learning. If students revisit content over carefully spaced intervals, they retain information longer than if presented with information once and then only assessed immediately after initial (short-term) mastery. ⁸

These findings, among others, show us the reciprocal relationship between assessment and learning. Providing timely and effective feedback can improve students' mastery of the content and produce more efficient - and satisfying - learning experiences.

Active Retrieval of Information

A growing body of research suggests that actively retrieving information (self-testing) produces significant long-term benefits for learning compared with passive studying (merely reading one's class notes). ⁹ While any assessment requires some type of active retrieval, having students reconstruct what they know through alternative assessments (as in Glenn's U.S. history class) leads to deeper understanding and consolidates learning in more powerful ways than traditional testing. ¹⁰

Active retrieval is also crucial as students prepare for assessments. Too often, when we ask students to reflect on their study strategies, they say they simply reread class notes. Training students to build regular self-testing into their study strategies will help them embed material into their long-term memory. ¹¹

Students with a mastery mindset... are more likely to choose more difficult but rewarding ways to demonstrate learning.

From Theory to Practice

Moving neuroscientific research on assessment into classroom practice faces two barriers. First, despite the fact that the work of educators targets the organ of learning, the brain, most teachers and school leaders have little understanding of the architecture of the brain and how it receives, filters, and applies information. Second, work that falls under the various versions of "brain-based" is too often believed to be of greatest benefit to struggling learners, those who historically might be called "learning-challenged." Such thinking is shortsighted and fails to recognize the importance of research in educational neuroscience for all students.

Also, it seems that too many parents do not want to believe what K-12 teachers intuitively know: that every student needs help and guidance to meet his or her peak potential. A parent of an Advanced Placement History student recently asked Glenn, "What can educational neuroscience do for my son? He is already getting all A's." This question came at a time in the school year when hundreds of thousands of students were gearing up for AP exams and/or final course exams. These cumulative assessments of learning place intense demands and stress on each student - stress that, research tells us, inhibits optimal learning and demonstration of understanding.

Glenn's response to this AP student's parent came in the form of a question: "What if I told you that the lessons from educational neuroscience research could potentially reduce the number of hours your son spends studying?" This question should pique any parent's interest. Research on multitasking challenges the way most students study today - especially the transaction cost of switching between social media and academic responsibilities. Additionally, research also highlights the importance of active versus passive studying and how crucial sleep is for both resting and learning.

What teachers assess should be what they want students to know, not just for an upcoming test, but also for the long term. And how we assess students has important implications for whether students will retain content or procedural knowledge for the short or long term. Far too often, as neuropsychology professor Tracey Tokuhama-Espinosa points out, "While students manage to keep enough dates, facts, and formulas in their head to pass the test, this knowledge never made it to long-term declarative memory, it was never truly learned at all (only memorized in the short term)." ¹² Research in educational neuroscience, therefore, should not only inform the types of assessments teachers give students but also help shape the strategies students use to prepare for assessments, or to work through projects, which Glenn prefers to call "learning opportunities."

Assessment Tic-Tac-Toe

The following assessment options can be used with middle school and high school students. Assessment Tic-Tac-Toe is designed to offer students options that both play to their strengths and encourage the development of new skills.

During the school year, each student must score tic-tac-toe by choosing a row, column, or diagonal assessment "chain" to be used as a summative assessment for three different units. Students are responsible for creating a grading rubric for each assessment they select as part of their assessment choice.

Podcast

3-D Medium (trifold poster,
diorama)

PowerPoint/Prezi/Keynote
Presentation

Public
Speaking/Oral
Presentation

Free Space (What else is
available that your teacher
has not learned about yet?)

2-D Medium (painting,
drawing, PowerPoint
poster)

How can all this research impact assessment at your school? Think of this as a challenge to teachers to embed richness in assessments - to think about how to foster optimal long-term learning when designing assessments for their classes or for individual students. We recognize that students - all students - have areas of evolving strength and weakness, passion and disinterest. When we get the richness right, we both challenge and support all students. What is important to understand about the human brain is that it changes with experience, a process often referred to as plasticity. When we create rich assessments that require deep thinking and problem solving, teachers are expanding students' cognitive capacities. How exciting to think of teachers as "brain changers."

Every year, we need to assess every student in multiple, developmentally appropriate ways (see sidebar on Assessment Tic-Tac-Toe). This is what differentiated assessment means. Some assessments will play to a student's strengths while others will pose significant challenges; some assessments will, wonderfully, do both.

Another central value of alternative assessments is that they help students learn essential skills for success in today's world - such as critical thinking, problem solving, communication, collaboration, resiliency, and grit. These skills are best developed through a variety of alternative assessments, particularly through project-based learning and design challenges in which "failing forward" - mistakes or failures that lead to breakthroughs, understanding, and student growth - is part of the assessment process. ¹³

Projects enhance student engagement. We also know that when students can own their learning choices, and make an emotional connection to the material, their learning is enhanced. Moreover, when we challenge students to demonstrate their learning in an authentic, purposeful way, they become more engaged in the process and actually learn more. As University of Virginia psychology professor Daniel Willingham points out, "A teacher's goal should always be to get students to think about meaning." ¹⁴ Alternative assessments do just that.

New Thinking at St. Andrew's

Here are some other important, research-informed changes in the ways St. Andrew's teachers think about assessment for their students. Such translation of research to practice further extends the school's highest-achieving students and improves student achievement overall.

Most units begin with a pretest to assess prior knowledge and to be able to measure individual student growth from the beginning to the end of a unit.

Teachers use more formative assessments, such as ungraded (thus low-stress) surprise quizzes, as a self-reflective process for students. Frequent formative assessments, which can be as short as one question, allow students to practice recalling knowledge from their long- and short-term memory. Frequent retrieval of information significantly enhances recall ability. This is called the "testing effect." It is a great way to have students begin or exit a class. And there are technology tools, such as Socrative, designed specifically to help with this process.

Teachers provide students with test-correction opportunities. Delaying or "scaffolding" feedback, and having students struggle with finding the correct answer, leads to better retention than does simply providing correct answers.

In order to help students decide appropriate study strategies, teachers ask them to reflect on what demands - attention, memory, visual thinking, executive functioning, language, neuromotor function, social or higher-order cognition - a particular assessment might be placing on their brains. This process includes providing students an opportunity to reflect - think metacognitively - on their assessment performance, particularly on how well their study strategies worked and how they might study differently in the future.

Teachers share research into the transaction costs associated with multitasking and how limiting the toggling between websites or social media channels while studying can enhance memory consolidation (certainly no easy request of today's students).¹⁵

As designers of their classes, teachers decide the appropriate assessments for a certain body of knowledge or skill. But what research regarding choice and engagement shows is that teachers should also respect the ability of students to make decisions about how they can best demonstrate their understanding (as well as their confusion). That is why it is essential that teachers and educational leaders have ongoing professional development about how the brain learns. The Center for Transformative Teaching & Learning (www.thecttl.org) at St. Andrew's was established to do just this - enhance teacher quality through training in the principles and strategies of neuroeducation.

Recent research on teacher efficacy points to the importance of deepening each teacher's toolkit with a neuroscientific lens into curriculum design and working with every student. ¹⁶ Such research supports the notion that when teachers understand the principles from educational neuroscience, they expand their teaching repertoire, including the ways they measure a student's knowledge, skills, and understanding. Assessments today, therefore, are much broader than what most of us adults experienced in our own academic journeys. And that is a good thing.

Notes

1. The final exam idea was adapted from Jim Percoco's "Historical Head" project in A Passion for the Past: Creative Teaching of U.S. History. Portsmouth, NH: Heinemann, 1998.

2. See Mariale Hardiman, The Brain-Targeted Teaching Model for 21st-Century Schools. Thousand Oaks: Corwin Press, 2012.

3. Carole Ames and Jennifer Archer, "Achievement Goals in the Classroom: Students' Learning Strategies and Motivation Process," Journal of Educational Psychology, 80(3) 260-267 (1988).

4. Paul R. Pintrich, "Multiple Goals, Multiple Pathways: The Role of Goal Orientation in Learning and Achievement," Journal of Educational Psychology, 92(3), 544-555 (2000); Heidi Grant and Carol Dweck, "Clarifying Achievement Goals and Their Impact," Journal of Personality and Social Psychology, 85(3), 541-553 (2003).

5. Harold Pashler, Nicholas J. Cepeda, John T. Wixted, and Doug Rohrer, "When Does Feedback Facilitate Learning of Words?" Journal of Experimental Psychology, Learning, Memory and Cognition, 31(1), 3-8 (2005).

6. Keri L. Kettle and Gerald Häubl, "Motivation by Anticipation: Expecting Rapid Feedback Enhances Performance," Psychological Science, 21(4), 545-547 (2010).

7. Lisa K. Fazio, Barbie J. Huelser, Aaron Johnson, and Elizabeth J. Marsh, "Receiving Right/Wrong Feedback: Consequences for Learning," Memory, 18(3), 335-350 (2010).

8. See, for example, Nate Kornell, Alan D. Castel, Teal S. Eich, and Robert A. Bjork, "Spacing as the Friend of Both Memory and Induction in Young and Older Adults," Psychology and Aging, 25(2), 498-503 (2010).

9. See, for example, Jeffrey D. Karpicke and Henry L. Roediger, "The Critical Importance of Retrieval for Learning," Science, 319(5865), 966-968 (2008).

10. Jeffrey D. Karpicke and Janell R. Blunt, "Retrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping," Science, doi:10.1126/science.1199327 (2011).

11. Nate Kornell and Lisa K. Son, "Learners' Choices and Beliefs About Self-Testing," Memory 17(5), 493-501 (2009).

12. Tracey Tokuhama-Espinosa, Mind, Brain, and Education Science: A Comprehensive Guide to the New Brain-Based Teaching. New York: W. W. Norton (2010).

13. See Paul Tough, How Children Succeed: Grit, Curiosity, and the Hidden Power of Character. New York: Houghton Mifflin Harcourt (2013).

14. Daniel Willingham, Why Don't Students Like School? A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom. San Francisco: Jossey-Bass, 61 (2009).

15. Eyal Ophir, Clifford Nass, and Anthony D. Wagner, "Cognitive Control in Media Multitaskers," Proceedings of the National Academy of Science (2009).

16. Mariale Hardiman, Luke Rinne, R. John Bull, Emma Gregory, Julia Yarmolinskaya (in review), "How Knowledge of Neuro- and Cognitive Sciences Influences Teaching Practices, Attitudes, and Efficacy Beliefs." Manuscript submitted for publication. R. John Bull, Mariale Hardiman, Luke Rinne, "Professional Development Effects on Teacher Efficacy: Exploring How Knowledge of Neuro- and Cognitive Sciences Changes Beliefs and Practice." Paper presented at the American Educational Research Association conference, San Francisco (2013).
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If you really want to see how innovative a school is, inquire about its thinking and practices regarding assessment. For the students, does the mere thought of assessment trigger stress? Do the teachers rely heavily on high-stakes, multiple-choice, Bell Curve-generating tests?

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Mini-lesson on Assessment; Understanding the Role of Assessment in Learning 

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Mini-lesson on Assessment; Understanding the Role of Assessment in Learning 

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Teaching and Learning in Higher Education

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This module touches on topics such as inclusive education, student diversity, interculturalizing the curriculum and teaching across cultures in various academic settings.

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Diagnostic assessments

Formative assessments

Summative assessments

Ipsative assessments

Norm-referenced assessments

Criterion-referenced assessments

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Retrieval Practice Makes Learning Active

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Retrieval Practice Makes Learning Active

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Retrieval Practice - The Learning Agency Lab

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RETRIEVAL PRACTICE The Five Things You Need to Know About Retrieval Practice When it comes to learning, people are often focused on getting facts into their heads. But experts argue that people should be more focused on getting information out of their heads via brain dumps, summaries, and quizzes. These types of active learning strategies […]

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The understanding of how one thinks and learns. It involves planning, monitoring, assessing and adapting one's efforts to learn, understand and perform. Metacognition practices increase one's level of awareness of his/her/their strengths and weaknesses.

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B. F. Skinner - Biographical Information

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B. F. Skinner - Biographical Information

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B. F. Skinner was born on March 20, 1904 in Susquehanna, a small railroad town in the hills of Pennsylvania just below Binghamton, New York.

With one younger brother, he grew up in a home environment he described as "warm and stable".

His father was a rising young lawyer, his mother a housewife. Much of his boyhood was spent building things - for example a cart with steering that worked backwards (by mistake) and a perpetual motion machine (the latter did not work). Other ventures were more successful. He and a friend built a cabin in the woods. For a door to door business selling elderberries, he designed a flotation system to separate ripe from green berries. When working in a shoe store during his high school years, he made a contraption to distribute the "green dust" that helped the broom pick up dirt.

In high school, Skinner took an English class taught by Miss Graves to whom he was later to dedicate his book, The Technology of Teaching. Based on a remark by his father, he blurted out in class one day that Shakespeare had not written As You Like It, but rather Frances Bacon. When his teacher told him he didn't know what he was talking about, he went to the library and read quite a bit of Bacon's works. Bacon's championing of the inductive method in science against the appeal to authority was to serve him well later.

First Encounters with Behavioral Science

After attending Hamilton college, Skinner decided to become a writer.

Moving back home he wrote little. His entire production from the period he called his "dark year" consisted of a dozen short newspaper articles and a few models of sailing ships. Escaping to New York City for a few months working as a bookstore clerk, he happened upon books by Pavlov and Watson. He found them impressive and exciting and wanted to learn more.

Graduate School and Discovery

At the age of 24 Skinner enrolled in the Psychology Department of Harvard University. Still rebellious and impatient with what he considered unintelligent ideas, Skinner found a mentor equally caustic and hard-driving. William Crozier was the chair of a new department of Physiology. Crozier fervently adhered to a program of studying the behavior of "the animal as a whole" without appealing, as the psychologists did, to processes going on inside. That exactly matched Skinner's goal of relating behavior to experimental conditions. The student was encouraged to experiment. Each department, Psychology, and Physiology, assumed the other was supervising the young student, but the fact was he was "doing exactly as I pleased". With his enthusiasm and talent for building new equipment, Skinner constructed apparatus after apparatus as his rats' behavior suggested changes. After a dozen pieces of apparatus and some lucky accidents (described in his A Case History in Scientific Method), Skinner invented the cumulative recorder, a mechanical device that recorded every response as an upward movement of a horizontally moving line. The slope showed rate of responding. This recorder revealed the impact of the contingencies over responding. Skinner discovered that the rate with which the rat pressed the bar depended not on any preceding stimulus (as Watson and Pavlov had insisted), but on what followed the bar presses. This was new indeed. Unlike the reflexes that Pavlov had studied, this kind of behavior operated on the environment and was controlled by its effects. Skinner named it operant behavior. The process of arranging the contingencies of reinforcement responsible for producing this new kind of behavior he called operant conditioning. Because of a fellowship, Skinner was able to spend his next five years investigating not only the effect of following consequences and the schedules on which they were delivered, but also how prior stimuli gained control over behavior-consequence relationships with which they were paired. These studies eventually appeared in his first book, The Behavior of Organisms (1938).

In 1936, then 32 years old, Skinner married Yvonne Blue and the couple moved to Minnesota where Skinner had his first teaching job. Busy with teaching and his new family -- their daughter, Julie, was born in 1938 -- he did little to advance the science he had started. But that was to change with the war. In 1944 World War II was in full swing. Airplanes and bombs were common, but there were no missile guidance systems. Anxious to help, Skinner sought funding for a top secret project to train pigeons to guide bombs. Working intently, he trained pigeons to keep pecking a target that would guide a missile onto an enemy ship. The pigeons pecked reliably, even when falling rapidly and working with warlike noise all around them. While Project Pigeon was discontinued (because of another top secret project unknown to Skinner - radar), the work was useful. Pigeons behave more rapidly than rats, allowing more rapid discoveries of the effect of new contingencies. As Skinner put it, "the research that I described in The Behavior of Organisms appeared in a new light. It was no longer merely an experimental analysis. It had given rise to a technology." Skinner never again worked with rats. Skinner described Project Pigeon in an article with the same name. The article is in Cumulative Record.

In 1943, towards the end of the Minnesota years, Yvonne was pregnant again. Knowing of her husband's talent at solving problems with gadgets, she wondered whether he might design a crib that would be safer than the typical crib with its bars that could trap a leg and blankets that could suffocate a baby. He could, and did. Proud of his new invention, an enclosed and heated crib with a plexiglass window, he sent an article to the popular magazine the Lady's Home Journal. Changing Skinner's title to grab attention, the article came out as "Baby in a Box". The "baby tender", as Skinner called his crib, was used only as a bed for the new baby. Deborah had a playpen and spent as much time out of her bed as do other infants. But inevitably confusion occurred between the baby tender and the "Skinner Box." To the end of his life Skinner was plagued by rumors about his second daughter, hearing even that she had committed suicide. In fact, Skinner was an affectionate father and never experimented on either of his children. Deborah is a successful artist and lives in London with her husband.

When the war was about to end, Skinner attended a dinner party and mentioned to a friend that it was too bad that her son and other young people would come back to the old ways of doing things. She asked what Skinner would have them do instead. Skinner could never refuse a challenge. Almost immediately he began the book Walden Two. Walden Two was written fast, parts with much emotion. In it, a soldier just back from the war, invites friends and his former professor to visit a community called Walden Two, a group of about 1000 members. They go to the community. Its designer, Frazier, explains how the happy and the industrious behaviors they are seeing have been carefully shaped using behavioral techniques. He explains how the competitive urge of parents to favor their own children has been converted to a more equal concern for all youngsters by bringing up the babies communally rather than in families. Both women and men work. Jobs earn work credits weighted so that one can work for only a short time at undesirable jobs or longer at desirable ones. All aspects of the community have been planned. The book, after a slow start, became one of the best known works of Skinner's, receiving both praise and condemnation.

In 1945, Skinner and his family moved to Bloomington, Indiana where he became Chair of the Psychology Department at Indiana University. The field he had started was growing. In 1946 the first meeting of the Society of the Experimental Analysis of Behavior was held in Indiana. Twelve years later, it had a journal, the Journal of the Experimental Analysis of Behavior.

An invitation from Harvard University to give the William James Lectures brought Skinner and his family to Cambridge, Massachusetts in the fall of 1947. He was invited to join the Psychology Department in 1948. There, he offered to give a course for undergraduates, scrambling each week to produce materials for the 400 students who enrolled. The material eventually became the book Science and Human Behavior (1953). The 1950's and 1960's were productive for Skinner, largely due to the excellent graduate students who came to study with him, including Douglas B. Anger, James A. Anliker, Nathan H. Azrin, Donald S. Blough, A. Charles Catania, Lewis R. Gollub, Richard J. Herrnstein, Matthew L. Israel, Alfredo V. Lagmay, Harlan I. Lane, Ogden R. Lindlsey, William H. Morse, Neil J. Peterson, George S. Reynolds, and Herbert S. Terrace. Spinoffs from this work produced the book Schedules of Reinforcement (Ferster & Skinner, 1957), the field of Behavior Therapy (from the work of Ogden Lindsley who coined the term), and psychopharmacology (where Peter Dews at the Harvard Medical School worked closely with Skinner's lab).

Teaching Machines and Programmed Instruction

Skinner's children were growing up. When the younger was in fourth grade, on November 11, 1953, Skinner attended her math class for Father's Day. The visit altered his life. As he sat at the back of that typical fourth grade math class, what he saw suddenly hit him with the force of an inspiration. As he put it, "through no fault of her own the teacher was violating almost everything we knew about the learning process. In shaping, you adapt what you ask of an animal to the animal's current performance level. But in the math class, clearly some of the students had no idea of how to solve the problems, while others whipped through the exercise sheet, learning nothing new. In shaping, each best response is immediately reinforced. Skinner had researched delay of reinforcement and knew how it hampered performance. But in the math class, the children did not find out if one problem was correct before doing the next. They had to answer a whole page before getting any feedback, and then probably not until the next day. But how could one teacher with 20 or 30 children possibly shape mathematical behavior in each one? Clearly teachers needed help. That afternoon, Skinner constructed his first teaching machine.

Skinner's first teaching machine simply presented problems in random order for students to do, with feedback after each one. But this machine did not teach new behavior. All it did was give more practice on skills already learned. Within three years, however, Skinner developed programmed instruction, where through careful sequencing, students responded to material broken into small steps. The steps were similar to what a skilled tutor would ask of a student working with one student at a time. The first responses of each sequence were prompted, but as performance improved, less and less help was given. By the end, a student was doing something he or she could not have done at the beginning. For about ten years, Skinner was caught up in the teaching machine movement, answering every one of thousands of letters from parents, schools, and business and industry. With a grant, Skinner hired James G. Holland who with Skinner's supervision, created The Analysis of Behavior for Skinner's class of Harvard students to take on a mechanical machine. (There were no microcomputers yet.) The field of education embraced this newest teaching method, but many of the materials were poorly written and no company wanted to design materials for a teaching machine that might go out of production. So most programmed instruction was put into book form. But the a book does not maintain the contingencies: Students can look at the answer before writing their own. By around 1968 education publishers stopped printing programmed instruction. That same year Skinner published The Technology of Teaching, a collection of his writings on education. Some of the better programs from the 60's are still used and with the coming of the computer and internet, the perfect machine that Skinner lacked is now available. Increasingly, instructional designers are realizing that, as Skinner insisted, tutorials must do more than present blocks of content with quizzes at the end. Effective instruction requires learners to respond to what each screen of information presents and to get feedback on their performance before advancing to the next. In addition, the sequencing of steps is critical.

Skinner's analysis of how to design sequences of steps came to him as he was finishing a book on which he had worked, on and off, for twenty years. Verbal Behavior published in 1957 is an analysis of why we say, write, and even think the way we do. The book is not easy to read. It took another twenty years before researchers used Skinner's categories and found that the different controlling variables he postulated were, indeed, independent. Recently their work has led to breakthroughs in teaching children, especially those with autism, to communicate effectively.

A concern with the implications of behavioral science for society at large turned Skinner to philosophical and moral issues. In 1969 he published Contingencies of Reinforcement and two years later Beyond Freedom and Dignity which prompted a series of television appearances. Still, the lack of understanding and misrepresentation of his work prompted his writing About Behaviorism (1974). Towards the end of his life he was still active professionally. In addition to professional articles, he wrote three autobiographical volumes, Particulars of my Life, The Shaping of a Behaviorist, and A Matter of Consequences. In 1989 he was diagnosed with leukemia, but kept as active as his increasing weakness allowed. At the American Psychological Association, ten days before he died, he gave a talk before a crowded auditorium. He finished the article from which the talk was taken on August 18, 1990, the day he died.
Contributed by B. F. Skinner's elder daughter

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Biographical Information - B. F. Skinner Foundation

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B. F. Skinner was born on March 20, 1904 in Susquehanna, a small railroad town in the hills of Pennsylvania just below Binghamton, New York. With one younger brother, he grew up in a home environment he described as "warm and stable". His father was a rising young lawyer, his mother a housewife.

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A Conversation with Pooja Agarwal on Interleaving

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A Conversation with Pooja Agarwal on Interleaving

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A Conversation with Pooja Agarwal on Interleaving

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Culturally Responsive Teaching: 4 Misconceptions + Podcast

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Culturally Responsive Teaching: 4 Misconceptions + Podcast

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Culturally Responsive Teaching: 4 Misconceptions | Cult of Pedagogy

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In some cases, teachers think they’re practicing culturally responsive teaching, when in fact, they’re kind of not. Zaretta Hammond helps clear things up.

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The "Why" We Need a Framework for Culturally Responsive Teaching

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The "Why" We Need a Framework for Culturally Responsive Teaching

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Culturally Responsive Teaching & the Brain

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Just recently I lead a seminar for a group of instructional coaches and BTSA support providers and I asked them to define culturally responsive teaching. It's usually how I start my workshops. Actually, I got them into groups of 5 and asked each group to draw me a picture of the process (Robert Marzano would call that a non-linguistic representation).

I ask them to do it because it gets at the mental models they are carrying around inside their heads about what they think it is.

More often than not, every group emphasizes a different aspect of culturally responsive teaching that is a mix of truth and misconceptions. It's like the story of the king who asked six blind men to tell them what an elephant was like. One felt his tail and said it was like a rope. Another had hold of a big floppy ear and likened the elephant to a fan. And so it went.

Well, that's pretty much what it's like hearing folks talk about culturally responsive teaching. In reality it isn't a set of best practices, but a process.

The Ready for Rigor Framework

I find what teachers (and instructional coaches) need is a framework for organizing the process. The Ready for Rigor frame takes the key elements of cultural responsiveness and organizes them into four practice areas. Unlike the individual part of the elephant that is mistaken for the whole, each area of the frame is interdependent with the others. The social-emotional and cognitive aspects of teaching and learning show up in each area. At its core is the neuroscience that unifies the frame: the brain's reaction to living in a racialized world as well as how it programs itself using culture.

The Inner Circle: Crosscutting Practices

Then there is the inner circle with the four staple practices of every culturally responsive teacher.

Affirmation - noticing and naming the unique talents and gifts of each child, especially those culturally grounded ways of being and expressing oneself.

Validation - acknowledging to students through word or deed the impact of unearned disadvantage in a racialized society without guilt or excuses.

Instructional Conversation - talking with students regularly about their learning process in order to move them safely into their zone of proximal development.

"Wise" Feedback - providing students with wise feedback, what Claude Steele says is the anecdote to stereotype threat. It is corrective feedback that first affirms the student's capacity to meet the goal or standard, tells him honestly where he missed the mark, gives him actionable steps to improve his learning, and helps him monitor his progress.

It Not Mysterious or Magic

There's no magic to the Ready for Rigor frame and it's not the only culturally responsive frame out there. The real point is that you have to have some type of framework to make culturally responsiveness operational. The art of cultural responsiveness lies in weaving together these four practices areas into a process that calms the brain and sets the stage for neuroplasticity where the brain can grow itself.

Learn more about how to operationalize the framework in my new book, Culturally Responsive Teaching and the Brain: Promoting Authentic Engagement and Rigor Among Culturally and Linguistically Diverse Students (Corwin, 2014).

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Why We Need a Framework for Culturally Responsive Teaching - Culturally Responsive Teaching & the Brain

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Just recently I lead a seminar for a group of instructional coaches and BTSA support providers and I asked them to define culturally responsive teaching. It's usually how I start my workshops. Actually, I got them into groups of 5 and asked each group to draw me a picture of the process (Robert Marzano would call that a non-linguistic representation).

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Dive Deeper: Culturally Responsive Teaching

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Dive Deeper: Culturally Responsive Teaching

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"Culturally responsive teaching (CRT) is a research-based approach that makes meaningful connections between what students learn in school and their cultures, languages, and life experiences. These connections help students access rigorous curriculum, develop higher-level academic skills, and see the relevance between what they learn at school and their lives"

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At a glance

Culturally responsive teaching is a rich, intentional approach woven into every aspect of student learning.

It focuses on the assets students bring to the classroom rather than what students can't do.

It raises expectations and makes learning relevant for all students.

Culturally responsive teaching is a research-based approach to teaching. It connects students' cultures, languages, and life experiences with what they learn in school. These connections help students access rigorous curriculum and develop higher-level academic skills.

Our brains are wired to make connections. It's easier for our brains to learn and store information when we have a hook to hang it on. That hook is background knowledge. Students bring this knowledge to the classroom every day.

But for students of color, English language learners (ELLs), and other underserved student populations, those assets are often overlooked. When that happens, educators miss the chance to use them to support learning. (See recent work by Zaretta Hammond for the neuroscience behind this.)

Culturally responsive teaching values and reflects the assets of all students. By doing that, it raises academic expectations for all learners. It also sends the message that multiculturalism is an asset.

Dive deeper

The benefits of culturally responsive teaching

How culturally responsive teaching, UDL, and SEL work together

For educators: Culturally responsive teaching in the classroom

For educators: How to partner with families

Lydia Breiseth (director of Colorín Colorado), Shivohn García, PhD (senior director of the Impact team at Understood), and Shaquala Butler (Understood Teacher Fellow) contributed to this article.

About the author

About the author

Educators Team at Understood is made up of passionate writers, editors, educators, and subject matter experts who have worked in classrooms and with students who learn and think differently.

Reviewed by

Reviewed by

Karla Vigil, MAT is the chief executive officer and co-founder of the Equity Institute.

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Culturally responsive teaching helps create learning environments that validate and reflect the diversity, identities, and experiences of all students. Learn more.

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The importance of practice; her love and passion for teaching

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Dr. Gloria Ladson-Billings: You're Teaching, But Are They Learning?

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Gloria Ladson-Billings - Successful Teachers of African American Children

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Gloria Ladson-Billings - Successful Teachers of African American Children

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Gloria Ladson-Billings - Successful Teachers of African American Children

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Gloria Ladson-Billings - American Theorist and Teacher,  Fields of Culturally Relevant Pedagogy

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Gloria Ladson-Billings - American Theorist and Teacher,  Fields of Culturally Relevant Pedagogy

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Gloria Ladson-Billings is the former Kellner Family Distinguished Professor of Urban Education in the Department of Curriculum and Instruction and faculty affiliate in the Department of Educational Policy Studies at the University of Wisconsin, Madison. She was the 2005-2006 president of the American Educational Research Association (AERA). Ladson-Billings' research examines the pedagogical practices of teachers who are successful with African American students. She also investigates Critical Race Theory applications to education. She is the author of the critically acclaimed books The Dreamkeepers: Successful Teachers of African American Children and Crossing Over to Canaan: The Journey of New Teachers in Diverse Classrooms, and numerous journal articles and book chapters. She is the former editor of the American Educational Research Journal and a member of several editorial boards. Her work has won numerous scholarly awards including the H.I. Romnes Faculty Fellowship, the NAEd/Spencer Postdoctoral Fellowship, and the Palmer O. Johnson outstanding research award. During the 2003-2004 academic year, she was a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University. In fall of 2004, she received the George and Louise Spindler Award from the Council on Anthropology and Education for significant and ongoing contributions to the field of educational anthropology. She holds honorary degrees from Umeå University (Umeå Sweden), University of Massachusetts-Lowell, the University of Alicante (Alicante, Spain), the Erickson Institute (Chicago), and Morgan State University (Baltimore). She is a 2018 recipient of the AERA Distinguished Research Award, and she was elected to the American Academy of Arts & Sciences in 2018.

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Gloria Ladson-Billings - National Academy of Education

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Gloria Ladson-Billings is the former Kellner Family Distinguished Professor of Urban Education in the Department of Curriculum and Instruction and faculty affiliate in the Department of Educational Policy Studies at the University of Wisconsin, Madison. She was the 2005-2006 president of the American Educational Research Association (AERA).