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Topics include: definitions, working memory and matching load with skill.

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MOOC EDSCI1x | Video 1: Cognitive Load | Student Learning Strategies

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Topics include: a guideline grid, including: engagement, representation, action & expression.

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

Reenvisioning the UDL Guidelines

Because the UDL Guidelines are meant to be informed by feedback from the field as well as new research, they have been updated several times in the past. We are in the process of updating the Guidelines once again in our UDL Rising to Equity initiative.

This update will focus specifically on addressing systemic barriers that result in inequitable learning opportunities and outcomes. CAST aims to develop a transparent, inclusive, and community-driven process. If you are interested in collaborating and staying updated on our progress, we invite you to complete a brief survey.

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

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Topics include: Sweller and a general overview.

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by Terry Heick

Preface: I'm (very clearly) not a neurologist. While I often have dedicated a lot of thought and research into things I write, sometimes I write about things in order to understand them-or understand them better. This is one of those times. Caveat emptor.

Generally, the Cognitive Load Theory is a theory about learning built on the premise that since the brain can only do so many things at once, we should be intentional about what we ask it to do.

It was developed in 1998 by psychologist John Sweller, and the School of Education at New South Wales University released a paper in August of 2017 that delved into theory. The paper has a great overview-and even stronger list of citations-of the theory. They also, obviously, define and explain it:

'Cognitive load theory is based on a number of widely accepted theories about how human brains process and store information (Gerjets, Scheiter & Cierniak 2009, p. 44). These assumptions include: that human memory can be divided into working memory and long-term memory; that information is stored in the long-term memory in the form of schemas; and that processing new information results in 'cognitive load' on working memory which can affect learning outcomes (Anderson 1977; Atkinson & Shiffrin 1968; Baddeley 1983).'

Put another way, the Cognitive Load Theory saysthat because short-term memory is limited, learning experiences should be designed to reduce working memory 'load' in order to promote schema acquisition.

Since both can't be done well at the same time, teachers can be specific about not just what is being learned (e.g., content knowledge versus procedural knowledge) and the sequence of the learning (e.g., learn about a 'thing,' then how that 'thing' works, then how to use that 'thing' critically and creatively) it is, but also the nature of what's being learned (e.g., domain-specific knowledge and definitions versus design thinking through knowledge and definitions).

For example, if you asked a student to critically examine various economic systems (higher-order thinking) while also defining and 'making sense of' what an 'economic system' was and how they worked, you'd be overloading short-term memory. Because the student doesn't yet 'understand' economic systems, they would need to consistently access their short-term memory while processing-while 'learning.' The concept of 'economic systems' is not yet in their long-term memory, so as they 'create knowledge' (moving new 'information' into existing or emerging schema), their short-term memory becomes cluttered because it is the primary 'ground zero' for the learning.

The student could likely still learn under these circumstances, but the instructional design in this scenario is non-optimal-the student would literally be fighting the way their brain works in order to learn.

Or so says the Cognitive Load Theory anyway.

Of course, a teacher doesn't want students fighting an uphill battle just to acquire new knowledge. We want students to grapple with complexity, but that's very different than defying neurology.
Another (Slightly Longer) Definition For Cognitive Load Theory
Wikipedia has one of the best (though slightly longer) definitions for the Cognitive Load Theory I've seen.

'Cognitive load theory provides a general framework and has broad implications for instructional design, by allowing instructional designers to control the conditions of learning within an environment or, more generally, within most instructional materials. Specifically, it provides...guidelines that help instructional designers decrease extraneous cognitive load during learning and...refocus the learner's attention toward germane materials, thereby increasing germane (schema-related) cognitive load.'
The Theory In Sweller's Own Words: A Psychologist's Definition
In 1988, Sweller himself wrote that:

'Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged."

See also Is The Cognitive Load Theory The Most Important Thing Every Teacher Should Know?

Ultimately, the Cognitive Load Theory also suggests that 'knowing things' is necessary to think critically about those things-or at least is most efficient when that is the case.

This further suggests that two of the primary information processing 'activities' here (knowledge acquisition and problem-solving) should be considered separately, oftentimes focusing first on schema, then on problem-solving with and through that schema.

Sweller continues, "It is suggested that a major reason for the ineffectiveness of problem-solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem-solving in the form of means-ends analysis requires a relatively large amount of cognitive processing capacity which is consequently unavailable for schema acquisition."

Put another way, the reason problem-solving and domain knowledge aren't directly proportional is because of how the human brain works. Problem-solving takes up crucial 'brain bandwidth,' reducing what's 'left' to 'learn new things.'

Of course, this has significant implications for how teachers might design lessons, units, and assessments, and for how curriculum developers use instructional design elements that brain-based learning.
A Few Thoughts About The Theory
The paper from NSW offers a slightly confusing 'cognitive load-friendly' strategy:

'Cognitive load theory supports explicit models of instruction, because such models tend to accord with how human brains learn most effectively (Kirschner, Sweller & Clark 2006). Explicit instruction involves teachers clearly showing students what to do and how to do it, rather than having students discover or construct information for themselves (see Centre for Education Statistics and Evaluation 2014, pp. 8-12).

I'm honestly not sure what to take away from this bit-maybe that the nature of explicit instruction is 'Cognitive Load Theory'-friendly because students are less frequently overloading short-term memory...maybe because the teacher is reducing 'load' by modeling and explaining and being extremely clear and explicit? In which case, the brain is free to 'learn' the specified learning objective?

That sounds great, but by being so narrow and 'clear,' it leaves very little room for personalization unless the teacher:

1. Was crystal clear-from the academic standard to the curriculum map to the unit to the lesson objective-what was was being learned and the cognitive level it was being learned at. For example, any academic standard that require the student to evaluate the bias in an author's position, for example, would itself need to be parsed to separate

2. Had fresh and accurate assessment date evaluating the prior knowledge each student came to that lesson with

3. Designed some way to accommodate a wide variety of existing understandings so that the 'load' was reduced per student rather than per standard or content topic

And there's zero chance of that happening consistently-which suggests education be looked at with fresh and honest eyes of how the brain works, and everything else-from curriculum to classrooms-be designed in response.
Conclusion
Anyhow, there you are. A definition for and brief discussion of the Cognitive Load Theory. There are scores and scores of 'theories' worth looking at, but this one stood out to me because it doesn't mean exactly what it sounds like it means, and in a neat and tidy way, kind of frames how all teaching and learning most naturally happen: in small movements, building in small increments of progress until something close to mastery emerges and learners can see what the value of the 'learned thing' is, and what the true utility of that knowledge might be.
What Is The Cognitive Load Theory? A Definition For Teachers

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What Is The Cognitive Load Theory? A Definition For Teachers -

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What Is The Cognitive Load Theory? A Definition For Teachers by Terry Heick Preface: I'm (very clearly) not a neurologist. While I often have dedicated a lot of thought and research into things I write, sometimes I write about things in order to understand them-or understand them better.

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Topics include: types and ways to reduce.

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As you receive information, the senses pass some of it onto your working memory, while some is ignored (you can't take in every detail in your field of vision!).

Your working memory might rehearse the information for clarity (or not), then it's processed (or encoded) into your long-term memory. The same is true in reverse, when you recall information that you want to use.

Your working memory acts like a gatekeeper, as it works hard to filter all the information it receives and decides which to keep.

Unfortunately, working memory isn't that large (some people have said it's like the RAM on a computer, whereas the long-term memory is like a hard drive). So working memory can suddenly become a bottleneck when it hits its processing limit.

When our students are learning in our lessons, there are several ways that we can overload their working memory. Let's look at the different ways this can happen.

Types of Cognitive Load

Sweller noted that cognitive load types fall into three broad types.

1. Intrinsic Cognitive Load

Intrinsic cognitive load is how complicated the topic or task is. Calculus is innately more difficult than simple addition, and most would agree that the present simple is easier to grasp than the third conditional. The intrinsic load is higher in these tasks because the complexity is higher.

2. Extraneous Cognitive Load

Think of extraneous cognitive load as extra information that distracts from the key information you want to get across. Too much information, too confusing, or unnecessary information.

We see this in the classroom as overly complex or badly designed materials, or a noisy environment (or even overly complex teacher instructions).

3. Germane Cognitive Load

Germane cognitive load looks at how easy it is for students to link their current knowledge to the additional information. It's part of the processing that takes place as the brain encodes the information into the long-term memory.

5 Principles to Reduce Cognitive Load in the Classroom

Richard E. Meyer developed these in a 2002 paper. Remember them when you're planning your lessons and in the classroom!

1. The Coherence Principle

Reduce the amount of information to only what's necessary. Keep things simple and clear, and focus on clarity over style.

This typically applies to two key areas in a class:

- Materials (where possible!). Don't write unnecessary instructions. Make sure images are clear and unambiguous.

- Teacher Talk (e.g. giving instructions). Grade your language to their level. Don't overload students with too much information when you speak. Leave room to think when you ask them a question.

Top tip: differentiation is a key strategy in managing intrinsic cognitive load in the classroom.

2. The Signalling Principle

Highlight important information somehow. Draw attention to it. Applies to both spoken and written information.

Top tip: when you speak, alter your pacing. Pause dramatically. Whatever you do, don't speak in a monotone at all times.

3. The Redundancy Principle

A classic example for this is reading the information from a screen.

While it might be necessary to present information in different formats for your students, do it because they might need it, not because of lazy teaching.

Top tip: don't keep repeating instructions when students already 'get it'. Same for language points that they've already mastered.

4. Spatial Contiguity

Show things related to each other, close together (or at least show that they're linked).

For materials, if you're labelling a diagram, you try not to add the label three pages after the image. Make sure related items are close together.

Top tip: use your common sense about linking items and their meanings close together!

5. Temporal Contiguity

The same as (4), but with time instead of space. So link related concepts close together, without leaving large time gaps in between. In conversation, you wouldn't suddenly jump to a topic from 10 minutes ago without warning and expect students to understand!

Top tip: when presenting language to learners, it's better to present linked items (I.e. an image of an item and the name of that item) together, as soon as possible.

For both temporal and spatial aspects, it helps to present language in context. This is fantastic for helping with germane cognitive load, as it automatically links the information to a relatable situation.

Final Tip

Don't make things too simple!

I've seen teachers go to extremes and make things so simple that it affects clarity. Materials that are too simple become hard to understand because they're ambiguous. Overly simplified spoken language can become grammatically incorrect, or be a bad language model.

The key is moderation!

Good luck with integrating cognitive load theory into your planning and classroom, drop me a comment below to let me know how it goes!

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What is Cognitive Load Theory? - Barefoot TEFL Teacher

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Cognitive load theory explains how we process and remember information, and how too much (or too complex) information is harmful to learning. In this article we'll look at cognitive load in our lessons, and 5 principles to reduce cognitive load for our students.

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Topics include: course content, meaning goals, transfer goals, engagement and backward design.

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Grant Wiggins - Understanding by Design (2 of 2)

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The Learning Sciences Framework in Educational Leadership 

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The Learning Sciences Framework in Educational Leadership 

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Author: Tracey Tokuhama-Espinosa. Topics covered: history review, backward design, framing questions

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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

# data[].attributes.attachment_link.title
The Learning Sciences Framework in Educational Leadership

# data[].attributes.attachment_link.description
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.

# data[].attributes.body
Six steps to 21st century brain-based learning, topics include: emotional climate, physical environment, think holistic, memory, application (i.e., problem solving)

# data[].attributes.attachment_link.title
TEDxEnola - Dr. Mariale Hardiman - The Brain-Targeted Teaching Model

# data[].attributes.body
Topics include: neuromyths, stereotype threats, mindfulness, mindset and attention.

# data[].attributes.body
Topics include: emotions, social world and relationships.

# data[].attributes.attachment_link.title
Ed-Talk: Learning with an Emotional Brain - Mary Helen Immordino-Yang

# data[].attributes.body
Topics include: long-term goal focus and autonomous learners.

# data[].attributes.attachment_link.title
Grant Wiggins - Understanding by Design (1 of 2)

# data[].attributes.body
Topics include: 2001 revision of the theory, cognitive process dimensions, LOTS and HOTS.

# data[].attributes.attachment_link.title
Revised Bloom’s Taxonomy – Center for Excellence in Learning and Teaching

# data[].attributes.headline
What is Transformative Learning Theory? 

# data[].attributes.subject
What is Transformative Learning Theory? 

# data[].attributes.body
Topics include: transformative learning, Jack Mezirow and reflection.

# data[].attributes.attachment_link.readable_text
Everyone learns differently, and understanding the different ways that humans learn is crucial to educational success. Teachers and students alike can greatly benefit from understanding how learning works for different students. By understanding how learning happens, educators can maximize their efforts and create classrooms where learners can thrive.

Transformative learning is one theory of learning, and particularly focuses on adult education and young adult learning. Transformative learning is sometimes called transformation learning, and focuses on the idea that learners can adjust their thinking based on new information. Jack Mezirow is known as the founder of transformative learning. Jack Mezirow began this theory of transformational learning when he did studies on adult women who went back to school. Mezirow's initial research led him to theorize that adults don't apply their old understanding to new situations, instead they find they need to look at new perspectives in order to get a new understanding of things as they change. Mezirow theorized that students had important teaching and learning opportunities connected to their past experiences. Mezirow found that critical reflection and critical review could lead to a transformation of their understanding. Adult education and adult learning is key in this theory, as children often don't have the same kind of transformation with their learning experiences. Mezirow found that adult learning involves taking the very things we believed and thought as a child, and letting critical reflection and teaching impact the transformation to what we should believe and understand now. Mezirow's theory has developed into a larger idea that our world view is changed the more we learn, and that helps us grasp new concepts and ideas.

Current and aspiring educators can greatly benefit from understanding learning theories and learning models, and implementing strategies in their classrooms that help them appeal to more learners. Learn more about Mezirow's transformative learning theory and how you can apply it in your classroom, particularly for older students.

Mezirow's transformative learning is defined as "an orientation which holds that the way learners interpret and reinterpret their sense experience is central to making meaning and hence learning." Put in simple terms, transformative learning is the idea that learners who are getting new information are also evaluating their past ideas and understanding, and are shifting their very worldview as they obtain new information and through critical reflection. It goes beyond simply acquiring knowledge, and dives into the way that learners find meaning in their lives and understanding. This kind of learning experience involves a fundamental change in our perceptions-learners start to question all the things they knew or thought before and examine things from new perspectives in order to make room for new insights and information. Many learners and experts agree that this kind of learning leads to true freedom of thought and understanding.

Mezirow says that transformative learning has two basic focuses-instrumental learning and communicative learning. Instrumental learning focuses on task-oriented problem solving, and evaluation of cause and effect relationships. Communicative learning focuses on how people communicate their feelings, needs, and desires. Both of these elements are important in transformative learning-students need to be able to focus on different types of their understanding and view new perspectives that are both logical and emotional in order to challenge their previous understanding.

Meaning schemes or meaning structures are another important element of the transformative theory and transformational learning according to Mezirow. Perspectives and meaning schemes two major elements of meaning structures, and are our predispositions and assumptions, which set the state for our expectations. A meaning structure is basically the concepts, beliefs, judgments, and feelings that shape an interpretation of information. Students are able to understand their meaning structure through self-reflection, self-directed learning, and critical theory. They are able to critique their assumptions to understand if what they understood as a child still holds true now that they are an adult. We are thereby able to understand ourselves, and our learning better. The understanding of our past perspective and the ability to look at new structures and perspectives are key to the transformative learning theory.

In the transformative learning theory there are typical phases that adult education learners follow. These phases are key in helping adult education learners transform their prior notions as they get new information and insight.

A disorienting dilemma. A disorienting dilemma is a situation where a learner finds that what they thought or believed in the past may not be accurate. This is the first part of transformational learning. This can be an "a-ha" moment where a student hears or consider something they may not have understood before. This disorienting dilemma can be uncomfortable or challenging for students, but is the key spark in starting a fire of transformational learning.

Self-examination. After a disorienting dilemma, students will do a self-examination of their beliefs and understanding. They will think about their past experiences and how they connect to this disorienting dilemma. This can create a perspective transformation, where students understand that their perspective may not be the only perspective.

Critical assessment of assumptions. Students in this phase of transformational learning are able to take a more comprehensive look at their past assumptions and review them critically. They are able to accept that perhaps some of their past assumptions were wrong, and are thereby more open to new information and thoughts. This creates perspective transformation as they are able to look with more unbiased eyes at their own past.

Planning a course of action. After students understand how their past assumptions and beliefs may have been wrong and have a perspective transformation, they are able to plan a course of action. They are able to consider what kinds of learning they will now need to more fully understand a problem or situation. They will be able to have a strategy for learning new things, seeing new perspectives, talking to new people, and more.

Acquisition of knowledge or skills to carry out new plan. Now is the time for students to carry out their plan and get further in their transformational learning. They may have to learn new things and consider different perspectives in order to fully enhance their learning. This may take extensive work and effort, but this is where the real learning is happening.

Exploring and trying new roles. As part of transformational learning, we need to act. In transformative learning, exploring and trying to understand changes is key to success. It goes beyond just learning about something, but actively working to understand and experience new things for yourself.

Building self-efficacy in new roles and relationships. Self-efficacy involves us being able to make our own decisions and have our own beliefs. As we follow transformative learning phases it's extremely important to build confidence in our beliefs and understanding, and to continue to practice this transformative cycle as we move forward.

As an educator it's important to help students learn using different kinds of learning strategies. The transformative learning theory can be especially important for older students to be able to grasp new ideas and concepts. There are many ways that educators can introduce this kind of learning into their classroom, including:

Give students chances to learn about new perspectives. Teachers can provide an action even or disorienting dilemma inside their classroom. A picture, story, demonstration, or guest that doesn't quite meet what they've understood can help do this. For example, students can learn about different political parties by actually meeting political candidates. They may have certain beliefs about government, but meeting a candidate who is kind and passionate about different viewpoints could be an action event for them. Similarly, teachers can introduce diverse authors and literature into the classroom, helping students see things from a perspective that isn't like their own.

Help students identify and question their assumptions. In the classroom it's important for students to critically analyze their assumptions. Teachers can model this behavior by sharing stories of how their own understanding and perspectives have shifted, how they have been able to analyze their own beliefs, and more. Teachers can give journal questions that help students by giving questions that they can ask about their own assumptions, give opportunities for students to share, and provide writing and reflective time for students to analyze.

Create opportunities for critical discourse. In a classroom that is focused on transformative learning, there must be opportunity for critical discussions. Teachers can help students have these critical discussions by providing space for conversation, helping students create pro and con lists of literature or articles, giving students opportunity for debate, or requiring students to prepare both sides of a debate to understand how there are multiple perspectives always at play. Additionally, small group discussions and activities can help students with this critical understanding. This kind of classroom work can help students really embrace other thoughts and experiences different from their own.

Transformative learning examples. There are many ways that educators can use transformative learning in their classroom. For example, when students are learning about world religions, teachers can introduce literature and articles that discuss different religions, helping them gain new perspectives and understanding. Additionally, teachers can give students an assignment to go and interview different people about their perspectives on a historical event. This helps students get many points of view and perspectives. Another way teachers can use transformative learning in the classroom is to give students contrasting articles on a topic, and ask them their thoughts and insights on the different elements of each article. This helps them to question their own understanding and move through the process of learning.

Educators can greatly benefit from continuing their education to learn more about learning theories and implementation practices. If you're a current teacher, a master's degree in education can help you learn more about pedagogical practices and continue your own learning. Staying up-to-date on best practices is vital for the continued success of your classroom, and a degree is an ideal way to enhance your learning.

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What Is The Transformative Learning Theory

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It's important for aspiring educators to understand how different learning theories can help them connect with students in the classroom. Learn more about the transformative learning theory and how to apply it in your classroom.

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Creating a Culturally Responsive Classroom

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Creating a Culturally Responsive Classroom

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Topics include: tips on communication, relationship building, student's life experience, technology tools and feedback.

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Creating a Culturally Responsive Classroom

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Authors: Whitman and Hardiman. Topics include: feedback, retrieval and ideas (e.g., tic, tac, toe).

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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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Resources for Online Education Assessment

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Resources for Online Education Assessment

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Topics include: learning goals and ideas on how to assess for each.

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Effective Assessments in Online Courses

Online Education Assessment

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FIU Libraries: Resources for Online Teaching & Learning: Assessment

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Skip to main content It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results. Effective Assessments in Online Courses

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The 6 Types of Assessments [+How to Use them]

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The 6 Types of Assessments [+How to Use them]

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Topics include: types, how to apply and rubrics.

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Topics include: retrieval, spaced, elaboration, interleaving, metacognition, growth mindset

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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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Topics include: behavior and response to stimuli.

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The theory of B.F. Skinner is based upon the idea that learning is a function of change in overt behavior. Changes in behavior are the result of an individual's response to events (stimuli) that occur in the environment. A response produces a consequence such as defining a word, hitting a ball, or solving a math problem. When a particular Stimulus-Response (S-R) pattern is reinforced (rewarded), the individual is conditioned to respond. The distinctive characteristic of operant conditioning relative to previous forms of behaviorism (e.g., connectionism, drive reduction) is that the organism can emit responses instead of only eliciting response due to an external stimulus.

Reinforcement is the key element in Skinner's S-R theory. A reinforcer is anything that strengthens the desired response. It could be verbal praise, a good grade or a feeling of increased accomplishment or satisfaction. The theory also covers negative reinforcers - any stimulus that results in the increased frequency of a response when it is withdrawn (different from adversive stimuli - punishment - which result in reduced responses). A great deal of attention was given to schedules of reinforcement (e.g. interval versus ratio) and their effects on establishing and maintaining behavior.

One of the distinctive aspects of Skinner's theory is that it attempted to provide behavioral explanations for a broad range of cognitive phenomena. For example, Skinner explained drive (motivation) in terms of deprivation and reinforcement schedules. Skinner (1957) tried to account for verbal learning and language within the operant conditioning paradigm, although this effort was strongly rejected by linguists and psycholinguists. Skinner (1971) deals with the issue of free will and social control.

Application

Operant conditioning has been widely applied in clinical settings (i.e., behavior modification) as well as teaching (i.e., classroom management) and instructional development (e.g., programmed instruction). Parenthetically, it should be noted that Skinner rejected the idea of theories of learning (see Skinner, 1950).

Example

By way of example, consider the implications of reinforcement theory as applied to the development of programmed instruction (Markle, 1969; Skinner, 1968)

1. Practice should take the form of question (stimulus) - answer (response) frames which expose the student to the subject in gradual steps

2. Require that the learner make a response for every frame and receive immediate feedback

3. Try to arrange the difficulty of the questions so the response is always correct and hence a positive reinforcement

4. Ensure that good performance in the lesson is paired with secondary reinforcers such as verbal praise, prizes and good grades.

Principles

1. Behavior that is positively reinforced will reoccur; intermittent reinforcement is particularly effective

2. Information should be presented in small amounts so that responses can be reinforced ("shaping")

3. Reinforcements will generalize across similar stimuli ("stimulus generalization") producing secondary conditioning

References

Markle, S. (1969). Good Frames and Bad (2nd Ed.). New York: Wiley.

Skinner, B.F. (1950). Are theories of learning necessary? Psychological Review, 57(4), 193-216.

Skinner, B.F. (1953). Science and Human Behavior. New York: Macmillan.

Skinner, B.F. (1954). The science of learning and the art of teaching. Harvard Educational Review, 24(2), 86-97.

Skinner, B.F. (1957). Verbal Learning. New York: Appleton-Century-Crofts.

Skinner, B.F. (1968). The Technology of Teaching. New York: Appleton-Century-Crofts.

Skinner, B.F. (1971). Beyond Freedom and Dignity. New York: Knopf.

Related Websites

There are two journals that contain current behaviorist research: The Journal for the Experimental Analysis of Behavior (JEAB) and the Journal of Applied Behavior Analysis. While the work reported in these journals is not necessarily Skinnerian, much of it does continue the legacy of Skinner's ideas. A bibliography and access to Skinner's works is provided by the B.F. Skinner Foundation. More background on operant conditioning can be found at http://www.simplypsychology.org/operant-conditioning.html

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The theory of B.F. Skinner is based upon the idea that learning is a function of change in overt behavior. Changes in behavior are the result of an individual's response to events (stimuli) that occur in the environment. A response produces a consequence such as defining a word, hitting a ball, or solving a math ...

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Topics include: Interleaving. A quick overview & fun!

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Topics include: challenge, critical reflection, feelings and socioeconomic implications.

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Author: Jack Mezirow. Topics include: dialogue involving the assessment of beliefs, feelings, and values.

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Jack Mezirow - Transformative Learning Theory

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Topics include: framework for CRT: awareness, partnerships, information processing, community and environment.

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Culturally Responsive Teaching: What you need to know

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Culturally Responsive Teaching: What you need to know

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Topics include: definition, why use it, what does it look like? and putting into practice.

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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

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

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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.

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The SUNY Online Course Quality Review RubricOSCQR

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The SUNY Online Course Quality Review RubricOSCQR

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Topics include: rubric and course evaluation. This is open to anyone and specializes in online courses & accessibility.

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To help campuses ensure that their online courses are learner centered and well designed, a team of SUNY staff and campus stakeholders has designed the OSCQR rubric, a customizable and flexible tool for online course quality review.

The OSCQR rubric specifically targets online course design. The OSCQR rubric is unique and differs from other online course quality rubrics in several ways. It is not restricted to mature online courses. The rubric can be used formatively with new online faculty to help guide, inform, and influence the design of their new online courses, and, it is non-evaluative.

Conceptually, the rubric and the online course review and refresh process are implemented as a professional development exercise designed to guide online faculty to use research-based effective practices and standards to improve the quality, effectiveness, and efficiency of their online course design, rather than as an online course evaluation, or quality assurance procedure.

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OSCQR - SUNY Online Course Quality Review Rubric

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To help campuses ensure that their online courses are learner centered and well designed, a team of SUNY staff and campus stakeholders has designed the OSCQR rubric, a customizable and flexible tool for online course quality review. The OSCQR rubric specifically targets online course design.

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Author: James Zull. Topics include: neuroscience & learning, brain plasticity, practice, and why educators should not "explain".

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About this journal: "a forum for the scholarly communication of basic and applied research on learning and development, including analyses from biology, neuroscience, cognitive science, and education" (About, 2021)

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Dr. Laurie Cutting, Dr. Amanda Martinez-Lincoln, and Ms. Andrea Burgess are editing a Special Issue of Mind, Brain, and Education entitled " Underutilized Techniques and Underrepresented Samples in Educational Neuroscience Research." This special issue will 1) showcase research that expands diverse, often understudied student samples and 2) highlight studies utilizing novel approaches, frameworks, and strategies in education. We are seeking to assemble a collection of studies that pioneer the next generation of educational neuroscience research, and in turn, drive the field forward. The issue will include both theoretical/review and empirical contributions.

Interested researchers should submit titles and abstracts (150 words or less) via email to the editor contact ( Amanda.Martinez-Lincoln@Vanderbilt.edu) on or before April 1st, 2022. Abstracts will be reviewed and a select number of submissions will then be invited to submit a full paper for peer review. Please note that an invitation to submit a full paper does not guarantee acceptance; all papers will be subject to the full peer review process as per any submission to Mind, Brain, and Education.

Lead authors will be contacted by May 1st, 2022 with submission determinations, and full manuscripts will be due by November 1st, 2022. Manuscript preparation guidelines can be found here. Abstracts that meet MBE's criteria for scientific merit and importance but are not selected for the special issue will be encouraged to submit to a regular issue of MBE. Inquiries, including questions about appropriate topics, may be sent electronically to Amanda Martinez-Lincoln at the email address above.

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About This Journal

Mind, Brain, and Education (MBE) publishes peer-reviewed articles addressing biological, brain, cognitive and behavioral questions that are relevant to the broad field of education.

MBE provides a forum for highest quality presentation of basic and applied research on learning and development across the ages, in typically developing and special needs populations, including analyses of biology, neuroscience, cognitive and social-emotional sciences, and education.

Read the full Aims and Scope here.

Registered Reports

Mind, Brain, and Education accepts the submission of Registered Reports in which methods and proposed analyses are pre-registered and peer-reviewed prior to research being conducted. For further information on this article type please refer to this article by the Center for Openness in Science. The guidelines for the submission of Registered Reports can be found here.

Supporting Authors

In order to optimize the peer-review process and to minimize duplication of effort for authors, reviewers, and editors, Mind, Brain, and Education collaborates with Brain and Behavior through Wiley's Manuscript Transfer Program.

Brain and Behavior is a peer-reviewed, open access, interdisciplinary journal, providing rapid publication of scientifically sound research across neurology, neuroscience, psychology and psychiatry. The journal publishes quality research reports that enhance understanding of the brain and behavior. The journal strives to help authors and look for reasons to publish rather than reject.

Read the full Aims and Scope here.

Articles

Exploring Potential Educational and Social Contributors to Relational Reasoning Development
First Published: &nbsp28 December 2021

LAY ABSTRACT

The relational reasoning performance of three groups from South Korea who either differed significantly in age or in the timing of their formal education was examined. The results showed that atypically schooled older adults performed significantly below the young adolescents and typically schooled peers on the composite scores of the Test of Relational Reasoning-Junior and its four scales. Implications for relational reasoning development and the potential contributions of educational and social experiences are discussed.

"Why Can't I See My Friends and Family?": Children's Questions and Parental Explanations About Coronavirus
First Published: &nbsp27 December 2021

LAY ABSTRACT

Young children may rely on parents to acquire knowledge about COVID-19. Yet, we know very little about what children understand about COVID-19. We explored children's questions and parental explanations about COVID-19. We found that parents' explanations referenced germs and compared COVID-19 to other illnesses. In addition, whereas older (aged 6-8) children asked more about death, younger (aged 3-5) children asked about loss of activities. The findings advance our understanding of the impact of COVID-19 on parents and children.

College Students Who Are Mindful About Math Achieve Better Grades
First Published: &nbsp9 December 2021

Lay Abstract

We investigated how math skills, math anxiety, and mindfulness about math contributed to college students' midterm grades in introductory math courses. The results showed that students who received higher grades at midterm began the semester with better math skills and were more mindful about math. These findings help identify students at risk for failure and suggest that mindfulness interventions may help improve students' performance in entry level college math courses.

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Dr. Laurie Cutting, Dr. Amanda Martinez-Lincoln, and Ms. Andrea Burgess are editing a Special Issue of Mind, Brain, and Education entitled " Underutilized Techniques and Underrepresented Samples in Educational Neuroscience Research." This special issue will 1) showcase research that expands diverse, often understudied student samples and 2) highlight studies utilizing novel approaches, frameworks, and strategies in education.

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Topics include: a good overview of epistemic cognition and heuristic thinking.

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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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Topics include: Cognition, Metacognition and epistemic cognition, including definitions.

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Topics include: channels, media options, etc.

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The theories of David Kolb, John Dewey, Kurt Lewin and Jean Piaget are explored. Topics include: adult learning and experiential learning.

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This site includes questions and answers on Adult Education that were written by students in the course 'Outline of Adult Education' at OISE/UT. The questions are first raised in class by the students themselves. Then they organize in teams in order to research and answer them. New entries are added regularly.