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A model for evaluating training.
link link link link

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General guidelines for developing and scoring essay items link

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Tool to teach critical thinking & rational debate

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Allows for: argument mapping, debate
link link

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Kialo Edu is a custom version of Kialo ( kialo.com), the world's largest argument mapping and debate site, specifically designed for classroom use. Its clear, visually compelling format makes it easy to follow the logical structure of a discussion and facilitates thoughtful collaboration. Kialo's mission is to promote well-reasoned discussion online, and to that end, Kialo is free for educators to use.

To put their knowledge into action To sharpen their critical reasoning skills To demonstrate their understanding To engage constructively with each other
Take your classroom discussions online, break down complex subjects for students, and shake it up with new types of assignments.

Try Kialo in Your Classroom

What can you use Kialo for?

Create discussions for your students, where they can put their knowledge into practice, develop their own views on classroom content, and consolidate what they've learned. In a Kialo discussion, every student has a voice - there's no talking over each other, and students have the space to explore arguments at their own pace. Kialo's collaborative platform encourages students to work together to find the best way to express each idea.

Assign students to write a Kialo discussion instead of an essay - or use Kialo discussions to outline larger writing projects. Kialo's argument-tree structure leads students to consider counter-arguments, develop a logical outline of their argument, and visualize how their ideas fit together.

Write a discussion for your students to explore, and set out content in a clear and easy format. Link to additional sources and reading, challenge students to add arguments or question concepts.

How does Kialo work?

With Kialo, discussions and debates are clearly visualized as an interactive tree of pro and con arguments. At the top of every discussion is the thesis, which is supported or challenged by pro and con claims. Each one of these claims can in turn branch into subsequent claims that support or challenge them.

All discussions are private and can only be seen by those you choose to share them with. To start a discussion with your students, simply write a brief description and a thesis (or theses). Share the discussion with your whole class, or divide students into smaller groups.

Each claim has a comment section underneath that allows you to give students feedback, suggest improvements, and ask questions without cluttering the surface of the discussion. Similarly, students can easily collaborate with each other to improve their arguments, examples, and phrasing.

You can set up teams for each of your classes to easily share and organize the content you want your students to see. All content on Kialo-Edu.com is private, accessible only to the people you share it with.

These features are just a small fraction of what you can do with Kialo.
Find the application that works best with your teaching style.

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Kialo Edu - The tool to teach critical thinking and rational debate

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Kialo is a free tool used by educators world-wide to teach critical thinking, facilitate rational classroom debate and explain controversial issues.

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a evidence-based framework to improve and optimize teaching and learning for all people. link.
For reviews: link link link

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Backward design is a method of designing learning activities with the end-goal in mind. This process consists of: 1) Identifying the desired result, i.e. defining your learning outcomes; 2) Determining the acceptable evidence, i.e. designing your assessments; 3) Planning the learning experiences and instructional materials you will use.

link for curriculum development
link for education research

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Creating clear learning objectives 1) helps integrating planning across a day/week/unit; 2) serves to connect content & assessment; 3) guides selection of effective activities; 4) gives learners a picture of what to expect and what’s expected of them; 5) forms the basis for evaluating teacher, learner, & curriculum. link link

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Abstract

Writing effective learning objectives is a necessary skill in academic medicine. Learning objectives are clearly written, specific statements of observable learner behavior or action that can be measured upon completion of an educational activity. They are the foundation for instructional alignment whereby the learning objectives, assessment tools, and instructional methods mutually support the desired learning outcome. This review article describes the essential components of a learning objective and provides practical tips on writing well-defined learning objectives.

Keywords: learning objectives, goals, Bloom's Taxonomy

Introduction

An anesthesiologist starts his grand rounds presentation on the topic of malignant hyperthermia (MH) with the following learning objectives:

1. Understand the pathophysiology of MH.

2. Review the clinical presentation of MH.

3. Discuss the treatment of MH.

4. Become familiar with caffeine-halothane contracture testing for MH.

This list informs the attendees about the topics covered during the presentation. However, do they know what is expected of them when they apply this content in their own clinical practice?

We have all seen learning "objectives" mentioned, such as the ones above, at the beginning of a presentation or workshop. But is what we see actually a learning objective? Learning objectives are often confused with learning goals; the example above is such a case in point. Learning goals are related to-but different from-learning objectives. A learning goal is a broad statement of an expected learning outcome of a course or curriculum. Learning goals provide a vision for the future and often summarize the intention or topic area of several related learning objectives. Learning objectives are drawn from the learning goals. They are guiding statements for each learning encounter, and they connect intention with reality within the learning experience as well as to the assessment planned. In keeping with the Roman philosopher Lucius Seneca, who said, "If a man knows not to which port he sails, no wind is favorable," this review article describes the essential components of learning objectives and provides practical tips on writing well-defined learning objectives.

Defining "Learning Objective"

A learning objective is a description of what the learner must be able to do upon completion of an educational activity. A well-written learning objective outlines the knowledge, skills and/or attitude the learners will gain from the educational activity and does so in a measurable way.

An effective learning objective should include the following 5 elements: who, will do, how much or how well, of what, by when.1 The mnemonic SMART-Specific, Measurable, Attainable, Relevant, and Time-bound-can be used to describe the elements of a well-written learning objective.2 One example of a SMART objective for an airway conference is: "Upon completion of the difficult airway workshop, participants should be able to formulate an accurate algorithm for the management of an obese adult patient with inadequate face mask ventilation, according to the American Society of Anesthesiologists difficult airway algorithm."

Practical tips for writing learning objectives are summarized in Table 1 . Since the point of a learning objective is to describe the intended outcome for learning, begin the learning objective with: "Upon completion of this educational activity, learners should be able to . . . ." The next step is to identify the concepts the learners need to learn and how they will demonstrate their understanding. It is recommended that the instructor choose one action verb that is measurable and observable. Verbs such as understand, know, learn, appreciate, believe, be familiar with, comprehend, and so on, are not observable or measurable and should be avoided. Each learning objective must be separate; two actions (such as diagnosis and management) or topics (such as bronchospasm and hypotension) must not be combined. It is also important that the action verb identify the level and cognitive domain at which the learner is expected to perform. Bloom's Taxonomy connects the depth of learning with action verbs that may be used when writing learning objectives.

Table 1:

Blooms' Taxonomy and Action Verbs

When writing learning objectives, educators leverage Bloom's Taxonomy3 ( Table 2 ). Bloom's Taxonomy describes 6 levels of hierarchy in the cognitive domain: knowledge, comprehension, application, analysis, synthesis, and evaluation. This taxonomy was revised in 20014 ( Figure 1 ). Each level of the hierarchy correlates to action verbs that educators use within learning objectives. To help anesthesia educators apply Bloom's Taxonomy more readily to their own learning, we will briefly review what each level of the hierarchy means to learning.

Table 2:

Knowledge "involves the recall of specifics and universals, the recall of methods and processes, or the recall of a pattern, structure, or setting."3 This category appreciates and distinguishes factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge as separate elements of "knowledge." In anesthesia teaching, we frequently use the first 3 types. For example, we provide factual knowledge (eg, lecture to undergraduate medical learners), procedural knowledge (eg, teaching in simulation or during an operation), and conceptual knowledge (eg, case-based learning, principles of anesthesia). Metacognitive knowledge is often applicable when giving feedback to learners; for example, when helping learners understand their competency in communication with patients and families.

Comprehension "refers to a type of understanding or apprehension such that the individual knows what is being communicated and can make use of the material or idea being communicated without necessarily relating it to other material or seeing its fullest implications."3 Application refers to the "use of abstractions in particular and concrete situations."3 Analysis represents the "breakdown of a communication into its constituent elements or parts such that relative hierarchy of ideas is made clear and/or the relations between ideas expressed are made explicit."3 Synthesis involves "putting together of elements and parts so as to form a whole,"3 and evaluation includes "judgments about the value of material and methods for given purposes."3

All educators should make the important connection between the intended learning depth (eg, comprehension) and the action verbs that reflect that intended learning outcome within the learning objective. Action verbs guide the learner about the intended outcome; for example, "critically appraise the evidence related to rapid sequence induction for airway management in pregnant patients."

Incorporating Bloom's Taxonomy and SMART for Effective Learning Objectives

Both Bloom's Taxonomy and SMART are necessary for writing effective learning objectives that communicate the intention for the learner with the intended learning outcomes. Examples of weak learning objectives and suggestions for better wording, based on incorporating Bloom's Taxonomy and SMART, are listed in Table 3 .

Table 3.

When faculty are leading sessions on writing SMART objectives, the authors often hear participants challenge that SMART objectives are longer and very detailed-consequently, will learners even follow them? The counterargument is that any objective worded too broadly, with no measurable standard that is attainable and repeatable for the learner in the given time frame, leads to confusion about "what to study" or "what performance is expected." The value in writing SMART objectives includes helping educators focus and specify what they intend to teach, assess, and offer as feedback for the learner. This shapes the learning session's scope, the method of instruction, and the type and quality of the assessment.

Do Learning Objectives Add Value to Curriculum and Assessment?

For many, writing out the learning objectives appears to be an effort to its own end. However, learning objectives are the foundation for instructional alignment. Instructional alignment means that learning objectives, assessment tools, and instructional methods mutually support the same educational outcome.5 This is known as the Golden Triangle5 ( Figure 2 ). Well-defined learning objectives outline the desired outcome for learners, which will help specify the instructional method. For example, if we want the learners to demonstrate correct intubation procedure in a normal adult 100% of the time, we need the instructional method to involve some sort of hands-on experience so that learners can demonstrate their skills. In contrast, if the teacher lectures about correct intubation without the opportunity for learners to complete hands-on skills, the learning activity is not aligned with the stated learning objective.

Likewise, learning objectives guide the assessment method. Taking the above example: If the objective is for learners to be 100% successful in normal adult intubation, assessing their skill using a written exam would misalign the objective and assessment method. As educators, we would miss the accuracy of the skill! Instead, the assessment method needs hands-on performance.

Constructive alignment underscores what learning objectives contribute towards achieving the intended educational outcomes. Learning objectives also contribute to shaping expectations, preparing learners for the educational activity and the standard by which their performance will be measured; the objectives also define faculty and learner responsibilities towards achieving the intended learning outcomes. Learning objectives may also guide teaching, by defining and limiting content or activities.

Are SMART Learning Objectives a Required Component of Medical Education?

Learning objectives are a required component in the planning of all medical education curricula. At the undergraduate level, the Liaison Committee on Medical Education (LCME) requires that "the faculty of a medical school define its medical education program objectives in outcome-based terms that allow the assessment of medical student's progress in developing the competencies that the profession and the public expect of a physician."6 At the graduate medical education level, one of the common program requirements from the Accreditation Council on Graduate Medical Education (ACGME) states that each program must distribute competency-based goals and objectives for each assignment at every educational level.7 Even for continuing medical education (CME), both the American Medical Association (AMA) and Accreditation Council for Continuing Medical Education (ACCME) require that a CME activity has learning objectives to qualify for category 1 credit.8 Consequently, writing effective learning objectives is a core skill that every academic anesthesiologist should master.

Conclusion

Writing effective learning objectives is a necessary skill in academic medicine. Well-defined learning objectives describe what the learner must be able to achieve upon completion of the educational activity. Bloom's Taxonomy and SMART are two tools that educators may leverage towards writing learning objectives that effectively relate the intended outcomes to the learners, simultaneously setting up the educators to successfully attain the learning outcomes within the time and resources provided. The successful academic anesthesiologist can align the instructional method, assessment, and intended learning outcome by using SMART learning objectives rather than learning goals. In closing, we revisit the learning goals in the opening anecdote of this review article.

Upon completion of the grand rounds presentation, participants will be able to:

1. Describe at least 3 intracellular mechanisms within the muscle cells during an episode of malignant hyperthermia (MH). The description must include: unregulated calcium release from sarcoplasmic reticulum, sustained muscle contractures, and the resulting hypermetabolic response.

2. Relate the 4 most common clinical signs of MH (hypercarbia, tachycardia/tachypnea, generalized muscle rigidity, and hyperthermia) to the underlying physiological mechanisms in the muscle cells.

3. Summarize, in order, the critical steps in the intraoperative management of an MH crisis according to recommendations from the Malignant Hyperthermia Association of the United States.

4. Formulate an anesthetic plan for management of a MH-susceptible patient that must include the use of: non-triggering anesthetic agents, monitoring for signs of MH, and preparedness to treat acute MH.

5. Critically appraise the role of caffeinehalothane testing for MH including indications, location, sensitivity, and specificity.

Footnotes

rrh: Learning Objectives

Financial Disclosures: None.

Conflicts of Interest: None.

Contributed by

Author Contributions: Debnath Chatterjee, MD, wrote the manuscript and reviewed the final draft before submission. Janet Corral, PhD, reviewed the initial and final draft and contributed to the content.

References

1. Thomas PA. Goals and objectives, : Thomas PA, Kern DE, Hughes MT Chen BY. (Eds.) [ Curriculum Development for Medical Education: A Six Step Approach, 3rd ed. Baltimore, MD: Johns Hopkins University Press; 2016; 50- 64. Google Scholar]

2. Skrbic N, Burrows J.. Specifying Learning Objectives, : Ashmore L, Robinson D. (Eds.) [ Learning, Teaching and Development: Strategies for Action. London: Sage Publications; 2014; 54- 87. Google Scholar]

3. Bloom BS. [ Taxonomy of Educational Objectives: Handbook I: Cognitive Domain. New York: Longman; 1984; 201- 7 [Appendix]. Google Scholar]

4. Anderson LW, Krathwohl DR.. [ A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. New York: Addison Wesley Longman; 2001. Google Scholar]

5. Cohen SA. Instructional alignment: Searching for a magic bullet. [ Educ Res. 1987; 16 8: 16- 20. Google Scholar]

6. Liaison Committee on Medical Education Standards for Accreditation of Medical Education Programs Leading to the MD Degree. http://www.lcme.org/publications/#Standards. Published March 2016. Accessed May 10, 2017.

Articles from The Journal of Education in Perioperative Medicine : JEPM are provided here courtesy of Society for Education in Anesthesia

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How to Write Well-Defined Learning Objectives

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Writing effective learning objectives is a necessary skill in academic medicine. Learning objectives are clearly written, specific statements of observable learner behavior or action that can be measured upon completion of an educational activity. They ...

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Learning objectives should be “SMART”: Specific; Measurable/Observable; Attainable for target audience within scheduled time and specified conditions; Relevant and results-oriented; Targeted to the learner and to the desired level of learning

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- Watercolour representations of molecular structures link
- Molecule editor & visualiser link
- Interactive periodic table link
- Lab simulations link
- Open source software directory (e.g geography, maths, planetarium, etc) link

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A Model of Learning Objectives–based on A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives by Rex Heer

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Bloom's taxonomy can help with creating specific, measurable / observable, and results-oriented objectives.

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Cognitive bias codex by John Manoogian III & Buster Benson

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Biases are pervasive, and can challenge even the most well-intentioned and egalitarian-minded teacher, resulting in actions and outcomes that do not necessarily align with explicit intentions.
- Interactive codex link

- Guide link
- Graphics link link
- Implication for educators link

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The ADDIE Instructional Design method is a framework for developing educational programs. Pedagogical engineers can find this approach useful because having stages clearly defined facilitates effective implementation.

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Exemples of pedagogical scenarios for distance and face-to-face experiences: training, one class, two classes, small and big groups, inverted class, co-creation session, learners exposing personal experience, asynchrone debate, pedagogic continuity, community initiation, fostering engagement, evaluation, world cafe, tags-cloud. link

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This ressource includes a series of questions that pedagogical engineers can ask prior to developing a learning experience.

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Here are some questions to consider when designing a course. These questions stem from brainstorming sessions at CTE's Course Design Workshop as well as the literature on this topic. Exploring these questions should help you develop a detailed course plan.

The questions are organized into the five interrelated areas of the model depicted below: Intended Learning Outcomes, Context, Content, Teaching Methods, and Assessment Methods. Course evaluation processes should inform ongoing design decisions as will your philosophy of teaching and learning. Feel free to enter the model wherever it makes sense to you. For each issue area, continually consider how your answers mesh with and/or help you to identify your overarching intended learning outcomes for the course. Also, remember to keep in mind the interrelationships among the five components. For example, if you chose to respond to the course evaluation by changing one component, you should double check your overall course design to ensure that your plans are still realistic and achievable.

Intended Learning Outcomes

Identifying and articulating intended learning outcomes is key to successful course planning. To identify the learning outcomes for a course, consider what you want your students to leave the course knowing, thinking, feeling, and/or able to do. List four to six main outcomes and be as specific as possible. The time you spend identifying and articulating the outcomes can pay off in many ways. It can mean less time spent selecting and/or designing assessment tools and can result in a well-designed course with integrated outcomes, content, teaching methods, and assessment methods. Best of all, if you are very clear about the outcomes you want your students to achieve, the more likely they are to attain them.

Are the intended learning outcomes reasonable given the contextual issues?

How well do my content and methods work to ensure that students can achieve the learning outcomes? What could I change to improve the fit?

What learning outcomes outside of course content do I have (i.e., writing skills, presentation skills)? Are my outcomes theory-based and/or skill-based?

Are the learning outcomes specific, attainable, and measurable?

Contextual Issues

There are many contextual issues that will affect how you shape your course. You should take time to think about who you will be teaching, how your course fits within their studies, and about the facilities and resources that you have access to as an instructor.
Students:

Who are my students (i.e., age range, program of study, year of study, experience)?

What are their interests? What are their needs? What are their goals?

What are their expectations of this course? Of me?

What are my students' goals? How can I incorporate them into the course content?

How can I respect the diverse abilities and needs of my students (i.e., language or cultural differences, or students who learn in different ways)?

What teaching methods are most likely to engage my students?

Do I need to assess their incoming skills (i.e., a writing or technical diagnostic)? If so, how?

What is my maximum class size?

What are my goals for my TAs? Do we know each other's goals?

Course Within Program:

Where does my course fit within the degree program (i.e., what pre-requisites or anti-requisites exist, what year is the course taught, is it required or elective)?

What is the department's goal in offering this course? How can I incorporate their goal into what I am offering?

What course should my course prepare students for (if any)?

Is the course practical or theoretical? Is my course skills-based, theory-based, or both?

What extra resources are available for students (i.e., tutors, TA's, etc.)?

Course Description:

What does the calendar description tell me about what I need to do in the course?

If a description does not exist, how do I accurately describe my course?

Course Location:

Where will the course take place and at what time? What facilities are available?

Will the students have access to computers?

What A/V equipment will I have access to?

Content Issues

Choosing what to cover in a course can be very intimidating - there are often so many different ideas and perspectives to choose from within each topic! Here are a few questions to think about when you are narrowing down your choices.
Textbook/Readings/Other Resources:

What resources will I use and where do I find them (i.e., journals, libraries, student bibliographies, online searches, etc.)?

What has been used in the past (i.e., textbooks, reading packages, notes packages, computer packages, etc.)?

Are my students the same as those for whom the resources were prepared?

Should I vary the types of resources for different learning styles (i.e., concrete vs. abstract learners, students learning in different languages, etc.)?

Are there new resources I want to add? Would a course notes package be useful? Do I want to put any resources on reserve in the library? What is my deadline for selecting a text/compiling a book of readings?

Class by Class Plan:

What will I teach? Are there department regulations regarding curriculum?

Do the students have pre-requisite knowledge or do I need to refresh them?

What will I teach each class? How much can I cover? How long are my classes?

How much time should I spend on each topic area?

What concepts are particularly important, difficult, or complex?

Given that the average attention span is 15-20 minutes, how can I divide the lecture content for each class into short, manageable chunks?

How much reading or other types of activities will I assign outside of class?

How much time will students need to devote to my course each week? How well to my plans for content coverage correspond to this amount of time? (More information about time expectations is available in the expandable box below.)

How much time should a student devote to a course?
Per hour of class time
Assessment Issues

There are many different types of assessment designed for use in a university course (see the CTE Teaching Tip " Types of Assignments and Tests ") and the key is to choose assessments that allow you to evaluate whether students have attained the outcomes you have set out.

What assessment tools will I use (i.e., assignments, exams, projects, collaborative work, peer assessment)? Why?

What am I trying to accomplish by using these tools? What do I want the students to get out of them? Are they consistent with my beliefs about student learning?

Given the classroom environment I wish to create, how well do my assessment methods work toward creating this environment? What could I change to improve the fit?

How do my assessments help students learn the difficult concepts in my course?

How can I limit the possibility of plagiarism and cheating?

What weights will I assign to my assessment methods? Why?

If I want flexible grading criteria, how can I build this into the course?

Are there expected grade distributions in my department? If so, how do I build that into my assessment methods?

When does assessment fit into my course?

How well do my assessment plans match the guideline that students should spend approximately 10 hours/week on a half credit course?

Is the workload reasonable, well timed, and sustainable for my students and me?

What can I learn about my students' learning from the assessment results? How will I analyze and use these results?

Teaching Methods Issues

Teaching methods include lecturing, facilitating discussions, inviting in guest lecturers, guiding group activities, and organizing many different types of active learning exercises both in and out of the classroom. Each method has its own advantages and disadvantages. You need to select methods that support your plans for the course and that help students be able to do the assessments.

What teaching methods am I familiar with? Comfortable with?

What methods fit with my own teaching philosophy?

What methods are my students likely to be familiar with? Comfortable with?

What other methods might I consider for this course? What methods would I like to learn more about?

How do I believe that learning happens and what methods can I use that will support my beliefs?

What methods will help me emphasize particularly important content and/or help students understand particularly difficult content?

Given the classroom environment I wish to create, how well do my teaching methods work toward creating this environment? What could I change to improve the fit?

Would a learning technology help me to better convey certain concepts?

Are the necessary resources (funding, technology, guest lecturers) available?

What teaching methods seem appropriate given the assessment methods?

Given that the average attention span is 15-20 minutes, what methods can I use to help me maximize my effectiveness?

Given my students, my context, my content, my time constraints, the intended learning outcomes, and myself, what methods are reasonable to use on a class-by-class basis? Remember, instructors do not have to use the same method(s) all of the time.

What materials can I use to supplement my classes (i.e., videos, graphs, slides)?

How can I evaluate my methods (i.e., student feedback, peer feedback)?

Additional Resources:

This Creative Commons license lets others remix, tweak, and build upon our work non-commercially, as long as they credit us and indicate if changes were made. Use this citation format: Course Design: questions to consider. Centre for Teaching Excellence, University of Waterloo .

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Course Design: Questions to Consider | Centre for Teaching Excellence

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Here are some questions to consider when designing a course. These questions stem from brainstorming sessions at CTE's Course Design Workshop as well as the literature on this topic. Exploring these questions should help you develop a detailed course plan.

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Pitfalls to avoid when designing MCQ (Azer, 2006)

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MCQ are a validated educational tool for formative & summative assessment because of its objective output, simplicity of utilization, and informative feedback to exam-takers and teachers link link link link. MCQ can test higher order thinking skills link link. There is no indication in the literature that open-questions are more reliable than closed-questions link link

Tips for designing MCQ link link

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Forester & Parkinson, 2006, Issues in Educational Research

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The gap analysis model can help exploring disparity between students' expectations and perceptions of induction provision. link

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Bakki et al., 2017, 8ème conférence sur les Environnements Informatiques pour l'Apprentissage Humain

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Approach and tools for assisting the scripting of pedagogical scenario of connectivist MOOCs. link

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Chi & Wilye, 2014, Educational Psychology

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The ICAP framework links cognitive engagement to active learning outcomes. It predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase.

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Factors determining the level of cognitive load (Kirschner, 2002)

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Instructional design should integrate current knowledge of working memory, long-term memory and the relations between them.
- Summary of 20 years research on CLT link
- Implications of CLT on the design of learning link

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The Myth Of Learning Styles. Bibliography available at the end of the webpage. 

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Summary: Myth: 82% thought that teaching children in their preferred learning style could improve learning outcomes. This approach is commonly justified in terms of brain function, despite educational and scientific evidence demonstrating that the learning-style approach is not helpful (Kratzig & Arbuthnott 2006). Check The Myth of Learning Styles article and infographic to find more!

The Myth of Learning Styles
What are the Main Learning Styles?
People are different. People think and feel differently about . They have different strengths, and they definitely learn and process what they 've learned differently. And everyone's learning style is directly linked to key areas of their brain. This is the truth and it is undisputed. There are 7 main Learning Styles, but the first three are the most common and widely used:

1. Visual
Where learners prefer to use pictures, images and spatial understanding

2. Aural
Where learners prefer acoustic stimuli

3. Kinesthetic
Where learners prefer to use their body, hands, gesturing and touching

4. Verbal
Where learners prefer speech and writing

5. Mathematical
Where learners prefer using logic and reasoning

6. Interpersonal
Where learners prefer to learn and function within groups

7. Intrapersonal
Where learners prefer self-study and to learn alone

Advocates of the learning style theory argue that instructors can achieve much better results when they take their student's learning style under consideration and create a course that best fits this exact style. The adversaries of the learning style theory say that this concept is misunderstood and not scientifically proven, and they argue that learning styles do make instructors understand what motivates and cerebrally stimulates their students, but they can't guarantee a successful outcome nor predict it. So where is the truth in that and where lies the Learning Styles myth?

What truth lies beneath?

First of all, it's true that people are different and that these exact differences affect the way they learn. Some have special needs. Some have special abilities. And no one can argue that intelligence comes in various levels and forms.

Second, people have different interests and when someone is interested in a subject, chances are that he will learn about it faster and more in depth.

Third, learners have diverse backgrounds, something that undoubtedly affects their learning.

And, last but not least, there are people with various learning disabilities, something that ultimately affects the way they learn and that requires a special approach on behalf of the instructor.

Debunking The Myth of Learning Styles

1. There is no convincing evidence to prove that when an instructor changes the presentation mode of his course to match the learning style of his students actually helps them learn.

2. There is no "better" or "faster" learning as an outcome of implementing individual preferences into a course. It's just a style that ultimately makes no difference in learning.

3. Instructors should not just take under consideration the learning styles of their students, but also their background and interests.

4. Content is the parameter that should directly affect the mode of presentation and not the learning style of the students.

5. It's definitely more efficient to create a course based on the motivational characteristics of the students and not their learning styles, and always be ready to adjust the learning methods and techniques and engage multiple senses rather than just one.

6. Perceptual learning has to do with senses and there is nothing restrictive about that. It doesn't prove that someone is a specific type of learner. It merely suggests that people have preferred learning styles.

7. Not all learning happens the same way and nor should teaching. What's crucial is to decide which techniques are best for which learning outcomes and not about customizing a course based on learning styles.

8. We mostly think of learning styles as de facto, without questioning their true value, purpose and relevance. And the truth is that according to recent research conducted by major US universities there is no correlation between learning styles and successful learning.

Create a Course that matches your Audience's Learning Style with the Right Vendor!

Find, choose and compare the top eLearning Content Development Companies!

The Myth of Learning Styles Infographic

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Summary: Myth: 82% thought that teaching children in their preferred learning style could improve learning outcomes. This approach is commonly justified in terms of brain function, despite educational and scientific evidence demonstrating that the learning-style approach is not helpful (Kratzig & Arbuthnott 2006). Check The Myth of Learning Styles article and infographic to find more!

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Principles for optimal multimedia design

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Allows for: multimedia learning
link

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Puentedura (2014). SAMR and TPCK: A hands-on approach to classroom practice.

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The SAMR model is one of the most used models for technological integration. However some of its limitations include: lack scientific bases, absence of context, Linear rigid structure, focus on product over process

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Kolb, L. (2017). Learning first, technology second: The educator's guide to designing authentic lessons. ISTE.

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The Triple E framework for technology integration is recent and built upon SAMR & TPACK models, it focuses on learning rather technology itself, and is supposed to better aligns with cognitive theories than past models, be accessible and well-researched. link

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Learning theories mindmap: behaviourism, cog approach, dev approach (constructivism...), social & tech approach (activity theory, connectivism...)

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As a pedagogic engineer, understanding the conceptual framework in which your collaborators and yourself work is crucial.

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Karr-Wisniewski & Lu (2010) Computers in Human Behavior

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This a survey instrument to identify technology leverage versus overload. Technology overload occurs at the point in which a marginal addition of new technology reaches the point of diminishing marginal returns. Technology overload has three salient dimensions: system feature overload, information overload, and communication overload.

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Spacing learning is important to address the decline of memory retention in time. Indeed learning is more effective when study sessions are spaced out as more information is encoded into long-term memory by spaced study sessions (spaced repetition, spaced presentation), than by massed presentation.
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Jaap M. J. Murre,

Joeri Dros

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Abstract

We present a successful replication of Ebbinghaus' classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24 hour data point.

Citation: Murre JMJ, Dros J (2015) Replication and Analysis of Ebbinghaus' Forgetting Curve. PLoS ONE 10(7): e0120644. https://doi.org/10.1371/journal.pone.0120644

Editor: Dante R. Chialvo, National Scientific and Technical Research Council (CONICET)., ARGENTINA

Received: November 22, 2014; Accepted: January 25, 2015; Published: July 6, 2015

Copyright: © 2015 Murre, Dros. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: Data are available from the Open Science Framework at DOI: osf.io/6kfrp.

Funding: This experiment was conducted as an employee (JM) and student (JD) of the University of Amsterdam. The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

This paper describes a replication of one of the most important early experiments in psychology, namely Ebbinghaus' classic experiment on forgetting from 1880 and 1885. We replicated the experiment that yielded the famous forgetting curve describing forgetting over intervals ranging from 20 minutes to 31 days. Ebbinghaus' goal was to find the lawful relation between retention and time-since-learning. This is why he fitted the data to two different functions (a power function, 1880, and a logarithmic function, 1885), as have many theorists since (e.g., [ 1, 2- 4]). This papers also includes an analysis-including one with a new model-of the shape of the Ebbinghaus' forgetting curve and its replications. Do the replicated forgetting curves have the same shape, or must we conclude that Ebbinghaus' forgetting curve was idiosyncratic and that quite different shapes may occur?

There is currently an increasing interest in replication studies in psychology, motivated by a growing uneasiness in the community about unreliable findings in psychology. It seems particularly important to try to replicate classic studies that are included in every textbook on cognitive psychology and may also be known by the general public. A good example of this is the classic study by Bartlett [ 5], which until 1999 had only had unsuccessful replication attempts, until finally Bergman and Roediger [ 6] succeeded in replicating the basic findings. One of the reasons earlier replications may have failed is because not all details were well-documented in the original study from 1932. The exact instructions, for example, were not included. This may explain why Wynn and Logie [ 7] had found the forgetting gradient in their experiment to be quite different from the one in Bartlett's experiment. Bergman and Roediger [ 6] also argue that this may have been caused by certain differences in the study design. Replication of classic experiments, thus, serves the dual purpose of verifying the reliability of the original results and uncovering more precisely how the original experiment was conducted.

It is hard to overestimate the importance of Hermann Ebbinghaus' contribution to experimental psychology. Influenced by the work of the German philosopher Herbart, he was the first to carry out a series of rigorous experiments on the shape of forgetting, which he completed in 1880. The experiment itself was preceded by a period in which he tried out a variety of materials and methods. After having tested himself with tones, numbers, and poem stanzas, he decided that none of these served his purposes. Tones were too cumbersome to handle and too difficult to reproduce for him, he did not find digits zero to nine suitable as basic units for the long-running experiments he envisioned, and the poem fragments he tried to learn (from Byron's Don Juan) were deemed too variable in the meanings they evoked and therefore likely to cause measurement error [ 8] (p. 14-17). He, therefore, introduced nonsense syllables, which had more uniform characteristics than existing words or other verbal material. In his later experiments on learning, however, he did verify his results with the Don Juan verses, confirming both his main results on learning and his intuition that the latter stimuli did indeed yield much more variance in the data [ 9]. Since his introduction of nonsense syllables, a large number of experiments in experimental psychology has been based on highly controlled, artificial stimuli.

In all experiments reported by Ebbinghaus [ 9], he used only himself as a subject. Single-subject designs are not unusual in memory psychology. Especially in the study of autobiographical memory we find several diary studies based on one person's personal memories (e.g., [ 10, 11, 12]). They have the advantage that there is no inter-subject variability, although they still require hundreds of trials to reduce the variance due to differences in stimuli and other factors. This places a great burden on the subject. Indeed, Ebbinghaus' forgetting curve is based on seven months of experimenting, often up to three sessions per day. Wagenaar [ 13] meticulously recorded one daily memory during six years and spent several months recalling these.

A disadvantage of a single-subject design is that it remains unclear what the shape of forgetting would be with other subjects. Are the results universal or did the subject happen to have a memory that was exceptional in some way [ 14, 15]. The generality of the results can be assessed with a faithful replication. There have been a number of-mostly early-replications of Ebbinghaus's forgetting curve, notably by Radossawljewitsch [ 16] and Finkenbinder [ 17], but these authors used a much slower presentation rate of the stimuli of 2 s per stimulus, where Ebbinghaus learned at 0.4 s per stimulus. This was partially the result of the development of devices for mechanical presentation by Müller and colleagues [ 18, 19], who presented materials at a rate one stimulus per second. Slowing down the presentation this much alters the nature of the processing with more time to generate meaningful associations to otherwise meaningless syllables. Though the resulting forgetting curves are clearly of interest to the field, we feel that the slow method of presentation form a large departure from Ebbinghaus' original study. Also, Finkenbinder's [ 17] longest retention interval is 3 days, instead of 31 days and though in the experiment by Radossawljewitsch [ 16] the retention interval range extends up to 120 days, his design suffers from an uneven distribution of intervals throughout time and time-of-day. Stimuli were learned in order: in the first few days of the study all 5 min intervals were learned, then the 20 min intervals, and so on. Because he did not use a pre-experimental practice phase, the early intervals took longer to learn while the subjects were still getting used to the materials and the procedure; it is likely that this has affected the shape of the forgetting curve reported by him. There are other differences between these two studies and Ebbinghaus', for example, the degree to which was learned and whether the subjects were allowed to pause between lists.

There are several unanswered questions about Ebbinghaus' results that formed part of the motivation for us to undertake this replication. His basic stimulus was a 'row' of thirteen nonsense syllables, which he studied until he could correctly recall it in the correct order twice in succession. A question that seems pertinent is how stimuli at different serial positions were learned and how these were forgotten over time. Another question is how his measure of choice, namely savings (see below) is related to the nowadays more common measure of percentage correct. Finally, we were interested in the role of interference or fatigue in the course of the experiment.

To help answer these questions, we consulted not only the widely published text of 1885 [ 9], which was translated into English in 1913 [ 20], but also an earlier report of 1880 [ 8]. This is a handwritten manuscript that he submitted for his Habilitation, which in Germany is a requirement to be considered for a full professorship. This text (the so called Urmanuscript or original manuscript) has been typeset and republished in German in 1983. Even with this additional source, however, we still could not answer the questions above.

For these reasons, we decided to replicate Ebbinghaus' forgetting experiment. If our replication yielded similar results, this would support the generality of Ebbinghaus' curve and through a more detailed analysis of our data, we would be able to address the issues above. In the course of preparing for our study, we found that there has been at least one other replication study, namely by Heller, Mack, and Seitz [ 21]. This study has been published only in German, without an English abstract, and is not easily accessible; at the time of writing, it is not available in electronic format (i.e., it is not available online) and it has never been cited in international journals in English. It is, however, a thorough study and an excellent replication attempt. Where the Ebbinghaus [ 8, 9] texts are unclear about certain details, we have mostly followed Heller et al. [ 21] as a guideline so that we can also compare our results with theirs. Because we feel this is an important study that has not received the readership it deserves, we will mention more of its details here than we would have had it been more accessible at this point in time.

In 1885, Ebbinghaus introduces the savings measure of learning and memory (it does not appear in this form in his earlier text from 1880). Savings is defined as the relative amount of time saved on the second learning trial as a result of having had the first. Suppose, one has to repeat a list for 25 times in order to reach twice perfect recollection and that after one day, one needs 20 repetitions to relearn it. This is 5 less than the original 25; we can say that on relearning we saved 20% with respect to the original 25 rehearsals (5/25 = 0.2 or 20%). If it takes just as long to relearn the list as it took to learn it originally, then savings is 0. If the list is still completely known at the second trial (i.e., no forgetting at all), then savings is 1 or 100%. Ebbinghaus prefers to express savings in terms of time spent learning and relearning but the principle remains the same. After Ebbinghaus' publication in 1885, the savings measure remained popular for several decades [ 16- 19, 22- 24]. Eventually, researchers found the savings method too unreliable compared with other methods of measuring memory [ 24] and in the following decades it was used much less with some exceptions (e.g., [ 25]). Later, an important improvement was suggested [ 26, 27], where learning is not to the 100% criterion but to a much lower one, such as 50% correct. These improved versions of the method are used nowadays, for example, when studying forgetting of foreign languages [ 28- 30].

In the following, we will first report our replication experiment. Then, in the Discussion section we will revisit the shape of forgetting, analyze the effects of serial position on forgetting, and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. Finally, we will study whether there is evidence for a jump at 24 hours in these curves, which some authors have attributed to the effect of sleep.

The Replication Experiment

The current study was set up to replicate the findings by Ebbinghaus [ 8, 9]. Despite a quite detailed account of his experiment, we found some information to be lacking and we had to estimate or guess these details, as outlined below. Also, we did not have the seven months available that Ebbinghaus invested in the experiment, but we had to accommodate our design to a 75 day period. We nonetheless believe that our experiment is close enough to his to be still called a replication.

There were a few differences between Ebbinghaus' study, Heller et al.'s [ 21] replication, and ours. (i) Because we were limited in time, like Heller et al. [ 21], we ran only 10 replications per time interval, instead of the 12 to 45 by Ebbinghaus. This means the variance in our data is larger than in Ebbinghaus' especially at the longest time intervals; apart from that no systematic differences were introduced. (ii) We were not able to experiment at a fixed time of day. Ebbinghaus (1880), who started experimenting in the morning at (A) 10:00, and then sometimes also at (B) 12:00 and usually at (C) 19:00 to 20:00, noticed that there was a difference between these times of the day in his ability to acquire a list. He subtracted 5% for B and 13% for C from the learning times at these hours to normalize the data with respect to time A. Heller et al. [ 21] were also able to conduct experimental sessions at specific times throughout the day, but they did not find such a time-of-day effect and hence did not implement a correction. (iii) Our stimulus material conformed to the phonotactics of the Dutch language and thus differs from both Ebbinghaus and Heller et al. Also, in contrast to both Ebbinghaus and Heller et al. we removed syllables that had too much meaning in order to further balance the level of difficulty of the stimuli. (iv) Our subject, J. Dros, was younger than H. Ebbinghaus, who was 29 during his experiments in 1879-1880. The ages of the two subjects in Heller et al. [ 21] are not given. (v) Ebbinghaus [ 8] gives exact testing dates for each of the short time-intervals but not for the longer ones (24 hours and up). Hence, we do not know exactly when he learned and relearned the lists for the longer intervals. This makes it impossible to calculate the number of interfering lists between learning and relearning. It also makes it nearly certain that our schedule differed from his (and from that of Heller et al. [ 21] who also do not supply such a schedule).

Subject

The second author, J. Dros, (22 years, male) was the only subject in the experiment. This experiment was reviewed and approved by the Review Board of the Psychology Department of the University of Amsterdam (see www.lab.uva.nl). The project is filed with case number 2014-BC-3879 (contact is Dr. R.H. Phaf). Consent was implicit as the second author of the paper was also the only subject on which we report. This was also approved by the Review Board. The subject's native language is Dutch, making this the first non-German replication of Ebbinghaus' forgetting experiment.

Materials

The learning material consisted of 70 lists. Each list consisted of 104 nonsense syllables, which in turn consisted of 8 'rows' of 13 syllables.

Nonsense syllables.

Each syllable consisted of 3 or 4 lower-case letters. The structure of a syllable was a lower-case consonant-vowel-consonant (CVC) structure. The consonant of the syllable was always one of b, d, f, g, h, j, k, l, m, n, p, r, s, t, or w. The vowel could be one of e, i, o, u, aa, uu, ee, ei, eu, oe, ie, oo or ui. The double-letters stand for standard Dutch vowels. The last consonant of the syllable was one of f, g, k, l, m, n, p, r, s, or t.

The number of different possible consonant-vowel-consonant combinations on the basis of these letter combinations is 2100 (15 × 14 × 10). Not every possible consonant-vowel-consonant combination was included in the learning material; we removed words that had too much meaning in Dutch, in order to further balance the difficulty of the stimuli. Syllables with meanings in other languages spoken by the subject, such as English and German, were not excluded.

Row and list construction.

Using the pseudo-random generator of Excel 2010, rows of 13 syllables were constructed. Within a row we did not allow two syllables with the same vowel in direct succession. We also did not allow two identical syllables within one row, but we did allow them in different rows within a single list. When syllables needed to be adjusted we first tried changing only the first or second letter of a syllable until the criteria were met. If this did not suffice, additional letters were changed. The adjustment process was not purely random but was carried out by hand during stimulus preparation by the authors.

Procedure

The only independent variable in this experiment was the time-interval, which started at the end of learning a list for the first time. The time-interval ended at the beginning of learning a list for the second time. The time-intervals between learning and relearning were the same as Ebbinghaus [ 8]: 20 minutes, 1 hour, 9 hours, 1 day, 2 days, 6 days and 31 days. For each time interval, 10 lists were learned and relearned (for the 9 hour interval only 9 lists were learned due to unforeseen circumstances).

We need to elaborate on the choice of these time intervals as there is some confusion about the exact length of the shorter retention intervals used by Ebbinghaus. He mentions both 15 min (in 1880 [ 8]) and 19 min (in 1885 [ 9]) for the shortest interval, and 63 min and 8.75 hours (525 min) for the longer intervals. He also states that relearning took place "after about one third of an hour, after 1 hour, after 9 hours, one day, two days, six days, or 31 days." ([ 20], p. 66). Heller et al. [ 21] followed the latter intervals, using 20 min, 60 min and 9 hours, etc. We have also used these, given that this seems to have been the intended lengths of Ebbinghaus' retention intervals. The deviations these values by Ebbinghaus are based on corrections after the experiment.

Ebbinghaus' shortest interval ('20 minutes') is based on almost immediately relearning a list of eight rows and hence the interval depends on how long it took to learn the eight lists. When relearning the lists so soon it takes much less time to relearning them, than the original learning, so that the intervals between learning and relearning of lists is not constant, with List 8 being relearned earliest (e.g., 20 min) and List 1 latest (e.g., 10 min). Ebbinghaus [ 8] (p. 50) states that the average time is about 15 min and argues that that whereas he does not know exactly how to correct for these variable learning times, the error will be small. We recalculated the average of the times stated on page 51 of the 1880 text and find it to be 1010 s or 16.8 min. Ebbinghaus keeps using the value of 15 min throughout his text from 1880, including for fitting his 'power function' equation (see below). The learning and relearning times given in his 1885/1913 volume [ 20] are the same as in 1880 [ 8], but to each interval he has now added 88 s for reasons that are not made clear. The average of these learning intervals then becomes 18.3 min. Given that he also remarks that for the shortest interval "relearning of the first series of a test followed almost immediately or after an interval of one or two minutes upon the learning of the last series of the same test" ([ 20], p. 66) may explain rounding 18.3 min to 19 min, the value used throughout the text from 1885. In general, it seems he made more or less intuitive corrections for the variable learning times and changed his mind from 1880 to 1885 about the most appropriate method to approach this. We have used 19 min, 63 min, and 8.75 hours in the graphs and tables (and fits), for Ebbinghaus' data. For the other data sets, we use 20 min, 1 hour, and 9 hours.

Measurement of repetitions and time.

The main measurement was the number of repetitions needed to correctly reproduce the syllables in a row in the correct order.

For the forgetting curve experiment, Ebbinghaus [ 8] learned until twice correct, but in later experiments switched to once correct [ 9], because he found there to be no essential difference in the outcomes. We chose to also learn to once correct. Heller et al. [ 21] (p. 8) seem to be using the once-correct criterion as well but this is not made entirely clear.

Ebbinghaus [ 8] uses elapsed time to calculate the number of repetitions, because he finds keeping count too distracting. Heller et al. [ 21] use a chain with wooden, colored beads, much like a rosary to keep count. We found that word processing software (Microsoft Word) was handy to keep track of the number of repetitions. During learning, each single repetition of a row was counted by pressing the button '1' on the keyboard at the beginning of every single repetition of a row. At the end of learning a list, the total number of 1's for each row was counted and entered into the database.

We also measured the time in seconds needed to learn a list. A clock was shown on the computer screen during the task and we recorded the begin times and end times of learning a list. Subtracted from the time were the pauses of 15 s between two rows (cf. [ 8], p. 19). When relearning a list, the extra time (15 s) introduced by the voice recording of the first retrieval attempt was subtracted from the total relearning time.

The practice phase and experimental phase.

Following Ebbinghaus, we preceded the experimental phase of the experiment with a practice phase to prevent as much as possible general learning effects due to growing experience with the task and materials. The practice phase took place between 08-11-2011 and 29-11-2011. A total of 14 lists was learned and relearned after 20 minutes (Heller et al. [ 21] relearned lists after one hour). After these, a further 19 lists were learned only (i.e., not relearned later) for additional familiarization with the task.

In the experimental phase, which took place between 01-12-2011 and 13-02-2012, a total of 69 lists was learned and relearned (9 for the 9 hour interval). The total time spent on data collection in the experimental phase amounted to about 70 hours. We distributed the ten lists for each time interval as much as possible over the whole experimental period. Due to the limited time available to run the whole experiment, we were not able to achieve this for the 31 days time interval condition, so that we decided to learn these lists near the beginning of this experimental period.

List learning phase.

All lists were printed on paper (black ink, font 'Calibri', 11 points) (eight rows per page; a row was actually printed in a column format for easier studying). The non-studied rows were covered by sheets of paper. The subject was seated behind a desk in a quiet room. The main goal was to learn a list as quickly as possible, to learn each row until it could be reproduced correctly once.

Following Ebbinghaus [ 8] (p.18), the syllables were softly spoken from the first syllable to the 13 th syllable at a constant speed of 150 beats per minute. The repetition of a row took 5.2 s on average. Our subject preferred to speak the syllables in a jambus-like manner, where syllables were paired so that the emphasis always was on the second syllable (i.e. wes-hóm, niem-hág, etc...). The last syllable (13 th syllable) was not paired to another syllable and was not spoken with an emphasis. Here, we use an approach similar to Heller et al. [ 21] and not to Ebbinghaus, who prefers a ¾ rhythm, stressing the first syllable in each group of three (this is in fact the reason he gives for his preference for rows of length 10, 13, or 16 syllables, see Ebbinghaus [ 8], p. 19).

During the learning phase, the subject had a continuous choice to either read or reproduce the syllables. Towards the end of the learning process, occasional attempts were made to produce an entire row by heart. When there was a moment of hesitation during such 'blind' reproduction, the rest of the list was read (i.e., not blind) to the end. Blind reproduction always started with the first syllable of a row. Rows were not learned in parts. Each time the 13 th syllable had been reached and the row still contained errors, it was read again from the beginning. After having learned one row and before starting the next one, there was an interval of 15 sec. The interval served as a moment of rest and pause. All of this was aimed at following Ebbinghaus as closely as possible. Each row was thus learned to a 100% correct criterion before moving to learning the next row.

During the pre-pretraining phase, a metronome was used at first to achieve a recitation rate of 150 beats, but this was found to be too intrusive and distracting. Eventually, the rhythm was internalized and the metronome was only used for occasional rate checks. During the experimental phase, it was not used. After each rehearsal of a row, there was a little transition-pause of about 3 beats to take a breath before the next repetition of the row.

Learning of a list was considered complete, if all rows had thus been learned in order. The retention interval was started at the time a list had been learned. On most days two or three lists were learned or relearned with a maximum of four. The full learning schedule is given in Fig 1.

Relearning phase.

We added one additional measurement to Ebbinghaus' procedure: the number of correct syllables at first reproduction of a row at relearning, not necessarily at the right location. We recorded the recall of the first time a row was relearned with an Olympus WS-450s voice-recorder. After the last relearning session of the experimental phase, the sound files were transcribed and scored.

Rows were relearned in the same order as during original learning. During relearning, the subject was seated at a desk with a computer. A word processing program was opened on the computer and a clock was visible on the screen. A sheet of paper with a list printed on it was laid in front of the subject with only the syllables from the row to be learned visible. The other rows were covered by a piece of paper. During learning, the subject used the '1' button on the keyboard to count the number of repetitions. Following Ebbinghaus [ 8] (p. 19), after successful relearning there was a 15 second pause. Then, the row learned last was covered, a next row uncovered and the procedure was repeated.

Relearning of a row started with turning on the voice recorder. Then the row was read once as described above. Twenty seconds after turning the voice recorder on, the subject stopped recall attempts and turned the voice recorder off. After that, relearning continued in the usual fashion. This procedure was repeated for every row in the list. After a list had been relearned, the audio file of the recording was saved on a computer.

Results

The forgetting curve

The main objective was to replicate Ebbinghaus' famous forgetting curve. The average number of repetitions is given in Table 1 and the number of seconds spent on learning and relearning each list, with the calculated savings scores, is given in Table 2. The raw data of this experiment are freely available online at the website of the Open Science Foundation (URL: https://osf.io/6kfrp/). To see whether there were differences between the time-intervals in the average number of repetitions at first learning, a one-way independent ANOVA with the average number of repetitions per list as the dependent variable and the time-interval as the independent variable. There was no significant effect for the time-interval, F(6, 69) = 0.691, p = 0.658. This means that the average number of repetitions per list did not differ significantly per time interval, indicating there were no randomization confounds.

Savings scores (based on time in s) are compared with those of Ebbinghaus [ 8], and Mack and Seitz [ 21] in Table 3 and plotted with error bars in Fig 2 using loglog coordinates and in Fig 3 using log coordinates only for the time axis. Despite the fact that the original experiment dates from 1880 and replications were done over a century later, and despite the fact that our replication was carried out in a Dutch language context, the four forgetting curves share many characteristics. To facilitate a direct comparison we have overlaid the four curves in Fig 4 where we have normalized the savings scores such that the first data point (at 20 min) was always equal to 1.0. Given expected individual differences, we find the resemblance of the four graphs remarkable. The greatest deviation is by Dros at 31 days; his savings score is much lower that any of the other three. We can only speculate at the reason for this. It may be that this subject simply has more long-term forgetting. Another explanation bears on the fact that the lists for the 31 day interval were all learned early in the experiment (see Fig 1) and learning times were shorter at the beginning, due to greater initial enthusiasm, less pro-active interference, or yet another reason. It is also possible that the low savings score on the 31 day point is an effect of the relatively short time period in which initial learning took place for the 31-day data point (i.e., massed learning); learning for other intervals was more widely spaced. We were forced to place the learning sessions at the beginning because of the limited time available by the subject for data collection. Ebbinghaus could spread his sessions of a seven-month interval, though we do not know the exact schedule for the intervals past 9 hours, nor do we know anything about the schedule followed by Mack and Seitz. Finally, we considered where the number of intervening lists had influenced retention at the 31-day-point. For this time point, the number of intervening lists varied from 23 to 33. Our analysis, however, revealed virtually no relationship with savings score as a function of intervening lists ( R ² was about 8.4%). Our data did not allow us to further disentangle the effect of number of intervening lists versus time because list number and time were too strongly confounded in our design.

We found a gradual increase in learning time throughout the course of the experiment as can be seen in Fig 5, where averaged learning time in s has been plotted for consecutive ten-day periods ('bins'). In the course of the 75 days of the experimental phase there was an average increase in learning time of 2.67 s per day for a list (this linear regression explained 56.18% of the variance). If we correct for this steady increase, which mostly affects the 31 day interval, the corrected savings measure would be 0.137 for the 31 day interval instead of 0.0410. This, however, is still well below the values for the three others, which are in the 0.20 range. This steady increase in learning time may be due to pro-active interference or fatigue. Ebbinghaus [ 8] and Heller et al. [ 21] do not report or analyze this.

We also analyzed the false alarms and correct answers measured on first relearning. We did a one-way independent ANOVA with the number of false alarms per row as the dependent variable and the retention interval as the independent variable. There was no significant effect for the time interval, F(6, 512) = 0. 753, p > 0. 608, indicating that the number of false alarms was not significantly different for the time-intervals. A one-way independent ANOVA with the number of hits per row as the dependent variable and retention interval as the independent variable, however, yielded a significant effect for the time-interval, F(6, 512) = 3. 85, p < 0. 01, which we will analyze further in the next section.

Serial position effects

In Fig 6, we have plotted the average serial position curves for each retention interval and for the grand average. Even if a correct syllable was not mentioned at the correct position, it was still scored as correct for its intended position (this was rare and had only a small effect on the data). Fig 6 shows clear serial position curves for all retention intervals.

In Fig 6, it also seems that there is more forgetting with time in the middle positions. In Fig 7A, overall forgetting is shown, where the average of all positions is shown for each retention interval. Though there is forgetting, these curves are much shallower than the savings curves, which are shown as well for comparison. In Fig 7B, forgetting is shown for four groups of serial positions, indicating that indeed there is virtually no forgetting in the final positions 11 to 13. A regression line is nearly horizontal for positions 11-13 (slope -0.000183) and 1-2 (slope is -0.000112) with the largest decrease over time found in positions 3-8 (slope is -0.0114) and 9-10 (slope is -0.0119). The averaged curve has a slope of -0.00930, with all slopes calculated over the untransformed scores (i.e., not on a logarithmic scale).

(a) Proportion correct, averaged over all serial positions, shown with Dros' savings scores for comparison. (b) Proportion correct curves for different groups of serial positions and for the average over all 13 positions.

Discussion

We believe that we may conclude that our attempt to replicate Ebbinghaus' classic forgetting was successful. We were able to follow his method quite closely and the resulting curve is very similar to both that of Ebbinghaus and that of the two subjects in an earlier German replication [ 21], with the exception of the savings value at 31 days, which in our case is much lower than the others. The latter difference remains even if we correct for increased learning time over the course of the experiment. It is possible that with Ebbinghaus and Heller et al. there were far fewer intermediary lists learned between learning and relearning and hence much less interference. Unfortunately, this information is not available, so this must remain speculation.

Effects of serial position on forgetting

Ebbinghaus does not say anything about serial position curves or indeed about the order in which he acquired the syllables. Our data allow us to say a little more about this. When interpreting the serial position scores in Fig 6, one has to bear in mind the nature of the savings methods with lists of nonsense syllables. With Ebbinghaus-type relearning, a row is always first studied before it is relearned. Savings experiments are very different from normal memory retention experiments where the subject learns something at after some time interval is tested for retention. With savings, the retention measurement itself consists of relearning the original material in repeated recall trials each of which is preceded by prior exposure to the stimulus materials. In theory it is, therefore, possible in extreme cases that there are subjects who can learn a row of 13 syllables in a single learn-recall trial but who subsequently always forget the learned materials in say 10 min. By conventional (non-savings) retention tests, these subjects would be reported as have 0% retention past 10 min, but these same subjects would show 100% savings for all retention intervals past 10 min because when testing their savings performance they would go through the exposure-learn sequence, always (re)learning the materials immediately: at t = 0, they take 1 trial to learn the material and at t = 1 day they also take 1 trial, showing perfect savings (i.e., seemingly no forgetting). Thus, when stimuli are very easy to learn true forgetting becomes impossible to measure with the savings method, because original learning will be at ceiling (one-trial learning) as will be relearning after a time-interval (again: one-trial learning), no matter how long the interval.

Here, it seems that the first two and the last three syllables were very easily learned (and relearned), probably because of primacy and recency effects. This effect was also noticed by other early researchers who adapted Ebbinghaus' method (e.g., [ 17]). Relearning the harder parts of a list, in particular the middle syllables, benefits most from recently having learned these sometime before (i.e., before the relearning phase of the savings experiment), as is evident from the savings scores. The discrepancy between savings and recall or recognition has also been found by other authors (e.g.,[ 24, 31, 32, 33]) and appears here as a function of serial position.

The fairly evident primacy and recency effects also suggest that the despite Ebbinghaus' efforts to construct equivalent stimuli, there is a great variation in how well they eventually were learned: During the first phase (t = 0), stimuli in the first and last positions were very easily and hence very well learned, while those in the middle were learned much less. The effect seems quite constant, however, so that it need not affect the validity of the shape of the forgetting curve, but we should be aware that its shape is based on a combination of very well learned items and just barely learned items. In that sense, the forgetting curve by Ebbinghaus is an average over different forgetting curves of items in various serial positions, which have been learned to varying degrees. This in itself may explain part of the characteristic shape of the curve, which we will explore in the next section.

Curve fitting

Hermann Ebbinghaus [ 8] was the first to try to find a mathematical equation that describes the shape of forgetting. Many researchers who used his method have followed suit, also trying to summarize the forgetting curve in a concise equation. In his first manuscript, from 1880, Ebbinghaus proposes the equation x = [1 − (2/ t) 0.099] 0.51, where x equals 1 minus savings at time t (in min). It is of some interest that Ebbinghaus [ 8] (p. 57-63) puts the entire section in which he fits this equation to the data between square brackets, making it an aside: something that is also interesting but not belonging to the main text. Nonetheless, he gives a well-motivated derivation based on a differential equation of a gradually slowing forgetting process. Interesting is that in the write-up of the experiment in 1885 [ 9], the equation has been changed to the very different one, which has been become generally known as the 'Ebbinghaus Forgetting Equation', rather than the first one, namely Q( t) = 1.84 / ((log t) 1.25 + 1.84), where the log is taken with base 10. This equation is lacking a derivation and Ebbinghaus remarks on it: "Of course this statement and the formula upon which it rests have here no other value than that of a shorthand statement of the above results which have been found but once and under the circumstances described. Whether they possess a more general significance so that, under other circumstances or with other individuals, they might find expression in other constants I cannot at the present time say." [ 20]

We are now in a better position to verify Ebbinghaus' question about the general significance of his equation by fitting his equations to the other data. The results are given in Table 4 and were obtained with a nonlinear fitting procedure in Mathematica 9 (the Mathematica code is available from the author). We calculated several goodness-of-fit measures including the Akaike Information Criterion or AIC [ 49, 58], which contrary to, for example, variance explained ( R ²) or sum of squared differences ( SSD), takes into account (and penalizes for) the number of free parameters. It also allows a comparison of the goodness-of-fit of different models, even if they have different numbers of parameters. Lower values indicate better fit, where a difference of more than 2 is seen as a meaningful difference in goodness-of-fit [ 49].

See text for the meaning of the parameters. SSD is the sum of squared differences between data and fitted curve, R ² is proportion variance explained, and AIC is the Akaike Information Criterion. To stay close to Ebbinghaus' own estimates, the parameters are fitted for time expressed in minutes.

In the table, we see that in the case of his power function from 1880 [ 8], Ebbinghaus' calculations, carried out by hand, were quite close the computer-optimized parameter values: he found values 0.51 and 0.099 for the parameters, whereas we found 0.523 and 0.101, respectively. For the logarithmic function from 1885 [ 9], we also found similar parameters to those parameters Ebbinghaus reported: 1.8 and 1.21 for his values of 1.85 and 1.25 respectively.

The goodness-of-fit of his functions is quite good, in both cases explaining 98.8% of the variance ( R ²) for his own data. The equation from 1885 has a slightly smaller SSD value (i.e., fits better), which in fact is the lowest value for an individually fitted curve we obtained (also see below and Table 5). Though the equations found by Ebbinghaus fit his own data very well, they do not always fit the other curves well, with especially Mack and Dros showing a relatively bad fit on these 'classic' equations. This is perhaps not concluded from the variance explained ( R ²), which is very high for all studies, but if we base our judgment on the AIC we observe large differences where the AIC for the Ebbinghaus data is almost twice as low as on the Dros data. This suggests that the general applicability of Ebbinghaus equations may be lacking. We further investigate this by comparing Ebbinghaus' functions with some other functions that have been proposed in the literature.

See text for the meaning of the parameters. SSD is the sum of squared differences between data and fitted curve, R ² is proportion variance explained, and AIC is the Akaike Information Criterion. The parameters are fitted for time expressed in seconds.

Ebbinghaus function from 1880 is a type of double power function. A normal power function is described by the equation , where Q( t) is savings at time t and μ ₁ and a ₁ are parameters. The latter equation has been proposed by several authors to describe the time-course of forgetting (e.g., [ 1, 3, 4, 34]). The forgetting mechanism typically associated with a power function is a constant slowing down of the forgetting rate with time (cf. Ebbinghaus' account from 1880 mentioned above). Whereas this is certainly a viable mechanism of forgetting, it can be proven mathematically that (spurious) power functions may emerge from averaging over different subjects or items [ 35, 36]. This has also been shown in simulations considering a wide range of circumstances [ 37, 38]. As argued in the previous section, the forgetting curve (also) averages over items that have been learned to various degrees, due to their serial position. There are, therefore, several reasons to expect and consider the power function.

The goodness-of-fit of the simple power function to our data is given in Table 5. As can be seen, the fit to Ebbinghaus' data is still impressive, though somewhat less good than either of his own equations. The goodness-of-fit of the power function, as expressed by the SSD, averaged over all four subjects' is comparable to the Ebbinghaus 1885 'logarithmic' function and it is somewhat worse than his 1880 'power' function. The AIC is slightly worse, but probably not meaningfully so as the difference in AIC measures is only 1.

Heller et al. [ 21] also fitted the Ebbinghaus 1885 'logarithmic' equation to the Mack and Seitz data and noticed that it did not fit the Mack and Seitz data well. They therefore proposed a different equation, the sum of two exponentials: A similar function has independently been proposed by Rubin, Hinton and Wenzel [ 39] to successfully fit a forgetting curve with very large numbers of observations per data point, which could not be fitted satisfactorily with any of the more than hundred functions studied in Rubin and Wenzel [ 2]. None of these authors gave a forgetting mechanism associated with these functions.

Though providing us with a superior fit, a disadvantage of the summed exponential is that there are no memory models that explain why forgetting might have this shape. As remarked by several authors investigating the shape of learning and forgetting [ 35, 38, 40], simply fitting sums of exponentials is expected to yield progressively better fits for the simple reasons that any function may be approximated by such a sum, which is related to the Laplace transformation.

A model of forgetting and amnesia developed by our group also yields a summed exponential function, but with a different parameterization [ 41]. This so called Memory Chain Model assumes that a memory passes through several neural processes or stores, from short-term to very long-term memory. While a memory is (exponentially) declining in intensity in Store 1 (e.g., the hippocampus), its contents is steadily transferred to a Store 2 (e.g., the neocortex) from which it will decline at a lower rate. We still have two exponentially declining stores, as in the summed exponential function above, but they are linked by a memory consolidation process. The decay rates in Store 1 and Store 2 are given by a ₁ and a ₂, respectively. The initial strength of the memory traces in Store 1 are given by μ ₁ and the rate of consolidating the contents of Store 1 to Store 2 is given by μ ₂. In experiments with dementia patients and experimental animals, Store 1 may typically be identified with the hippocampus and Store 2 with the neocortex. Lesioning Store 1, will produce a retrograde amnesia gradient that can be modeled by the Memory Chain Model simultaneously with the forgetting gradient of healthy controls [ 42].

The Memory Chain Model (MCM) equation for type of savings studied here is given by The MCM function has the same number of parameters but they are arranged differently. The proof that this equation is a mathematical formalization of the memory consolidation process can be found elsewhere [ 42]. As can be seen in Table 5, the summed exponential and the MCM function give exactly the same fits, though the parameters differ. The gain of using the MCM function lies primarily in the fact that its parameters can be interpreted more clearly, that it is associated with a type of consolidation mechanism, and that also explains other types of data than the savings function [ 41- 43]. The MCM function assumes a neural system consolidation mechanism [ 44, 45] that has been dubbed the 'Standard Consolidation Theory' [ 46, 47], where the latter authors propose a different theory, the so called Multiple Trace Theory of consolidation. It is here not our goal to evaluate the merits of these theories; we have reviewed these and other theories of consolidation elsewhere [ 48]. We merely want to apply the MCM equation to these four savings curves and evaluate the goodness-of-fit, viewing it as a conceptual improvement of the summed exponential.

If we compare the MCM equation or summed exponential function to the other functions, this only makes sense if we rely on the AIC, which takes into account the varying number of parameters. The MCM function (or double exponential function) fits two of the four curves (Mack and Dros) better than Ebbinghaus' own equations from 1880 and 1885, it gives about the same fit on the Seitz data, and it does much worse on Ebbinghaus' own data. The average AIC is 0.5 less than the average AIC for the classic Ebbinghaus functions, which-though it indicates a better fit-may not be considered a meaningful difference; a difference of 2 is considered 'meaningful' [ 49]. We also fitted a single exponential with only two parameters but this fared far much worse on all data sets, including Ebbinghaus' data (also see [ 3]).

Summarizing, Ebbinghaus' data fit his own equations and the power function best. The AIC indicates that on average the MCM equation (or summed exponential function) is on average better than all equations considered thus far, where the difference with the power function is 1.5. The difference with Ebbinghaus' own equations is only 0.5 but this is partially because his own data have an exceptionally good fit on his own equations, with a very low AIC of about -30.5 (and an extremely high 99.8% variance explained). It is likely, however, that Ebbinghaus actively searched for an equation that achieves such an exceptional fit, which in his eyes was no more than a 'summary' of the forgetting curve (see citation above). This also explains why he has no problems substituting a 'logarithmic' equations for the earlier 'power' equation: it shows a slightly better fit.

Fitting data is always done with a purpose. Ebbinghaus achieved a concise summary of his forgetting data, the power function is a parsimonious description of the forgetting function that shows a good or at least adequate fit in many types of forgetting experiments, and the MCM equation attempts to capture the shape of a hypothetical consolidation process in the brain albeit at the expense of additional parameters. Taking into account these extra parameters, however, does not give a worse fit on the AIC and approaches a meaningful improvement over the power function.

The 24-hour point in Ebbinghaus' forgetting curve

When looking at the shapes of the four curves in Fig 2, savings after 1 day (or 2 days) seems higher than expected. Ebbinghaus [ 8] notices this as well but merely writes it off as a discrepancy from his fitted curve (see above) that still falls within the error bars ([ 8], p. 62). He clearly did not trust this data point because in his text from 1885 [ 9] he reports that he later had replicated this 24 hour data point. The replicated data for this point gave a very similar score, so we must consider it a valid measurement. Jenkins and Dallenbach [ 50], however, interpreted the discrepancy as an effect of sleep, which motivated them to investigate this closer in an experiment on the effect of sleep on forgetting. They also refer to the forgetting curve by Radossawljewitsch [ 16], who also found higher savings after both 1 and 2 days (0.689 and 0.609, resp.) compared with after 8 hours (0.474). To them, this is suggestive of a very strong effect of sleep, but Finkenbinder [ 17] points out that Radossawljewitsch's 8-hour data point may not be reliable, because these lists were all relearned during the afternoon, when there was less rapid learning resulting in fewer savings. He, therefore, suggests using a corrected savings score at 8 hours of 0.66, which is not unreasonable given that Ebbinghaus also corrected his savings scores for time-of-day effects, in some cases up to 13%. Even if savings would be 0.66 at 8 hours, however, the 1 day savings score is still higher than the 8 hour score and the 2 day savings is still higher than what one would expect.

Using free recall and retention up to 8 hours, the seminal study by Jenkins and Dallenbach [ 50] yielded a positive effect of sleep on recall. This effect has since been replicated many times, for example in recent studies on the effects of different sleep stages on both procedural and declarative memory (e.g., [ 51, 52- 56]). Whereas the older studies from the 1970s and before typically confound the sleep manipulation with time-of-day effects or fatigue, this is no longer the case in the recent studies, so that there is now very strong evidence that sleep does indeed have an effect on memory independent of the effects of, say, rest or lack of interference. In some of the sleep-memory experiments cited above, we even see a temporary increase in the forgetting curve, where subjects score better than after learning in the days following sleep, but not if they skipped the night of sleep after learning (e.g., [ 53]). This result-and other studies-suggests that the first night of sleep after learning has a particularly important effect on memory that may continue to evolve for several days afterwards. Such an effect may also be observed in savings curve by Mack and to some extent in the Seitz curve, both show a tendency to increase in savings score for two days following learning.

Given that we can trace the history of research on the effects of sleep on memory to the 24 hour point of Ebbinghaus' forgetting curve, we think it is interesting to evaluate this data point more formally. If we can establish the jump in the curve more formally, it will make a stronger case that the 'true shape' of the long-term forgetting curve has a jump in at 24 hours (or perhaps right after the subject has slept), although we may not conclude from this that the local increase is due to sleep per se, which would require more research is necessary for that.

If we first informally inspect the data points shown in Fig 2 and compare them with the fitted power function, we see relatively less forgetting at either day 1, day 2, or both. In all four panels, at least one of these points is above the fitted power function curve at a distance of at least one standard error. The same is true for the fitted curves of the summed exponential, Ebbinghaus' 1880 'power' function and his 1885 'logarithmic' function (not shown here). We also see this effect for the Memory Chain Model curve in Fig 3, though somewhat less pronounced (in the Seitz panel, the fitted curve crosses the error bars at 1 and 2 days). The reason for this is that the Memory Chain Model already incorporates the effects of a hypothetical consolidation process. In short, we observe that there is seems to be a memory 'boost' in the classic Ebbinghaus' forgetting curve and its replications. The current body of research on sleep and memory would predict such a boost after one or two nights and attribute it to sleep, though for this particular type of experiments this has to established more firmly in further experiments. Whatever its cause, we can better quantify the visually observed boost by including it in the equations fitted. We, therefore, made a variant of the power function that differs only in the addition of a constant boost factor to the savings of the retention intervals of 1 day and higher. This power function with boost is also plotted in Fig 2.

The results are mixed, though on average they suggest a trend towards improvement with a boost parameter. The original power function had a average AIC of -22, a sum-of-squared-differences ( SSD) of 0.00932 and explained 98.7% of the variance (see Table 5), whereas adding the boost reduces the AIC to -23.6, the SSD to 0.00628 and increases the average variance explained to 99.1%, putting its goodness-of-fit on a par with the MCM (or summed exponential) function. A difference in average AIC of 1.6 may perhaps be called a trend towards a meaningful difference, though there were large differences between the individual subjects. The boost parameter in Table 5 shows the size of the upward jump after 24 hours. We see that for Ebbinghaus, this jump is small (0.030), whereas for Mack it is quite substantial (0.131). The Dros data show no evidence for a boost but these fits are probably influenced strongly by the very low 31 day data point.

Concluding Remark

In 1880, Ebbinghaus [ 8] set new standards for psychology experiments, already incorporating such 'modern' concepts as controlled stimulus materials, counter-balancing of time-of-day effects, guarding against optional stopping, statistical data analysis, and modeling to find a concise mathematical description and further verify his results. The result was a high-quality forgetting curve that has rightfully remained a classic in the field. Replications, including ours, testify to the soundness of his results.

His method can also be seen as a precursor to implicit memory tests in that certain inaccessible representations, seemingly forgotten, can still be relearned faster compared with others that do not show such an advantage. This is evidence of implicit memory because the subjects may not be consciously aware they still possess traces of the memory representations, which cannot be recalled or recognized but that do show savings. The savings method is still used today as a sensitive method to study the decline of foreign languages in order to assess the true extent of linguistic knowledge retained over a long time [ 57].

Ebbinghaus [ 8] also emphasizes the importance of sleep for memory, but these remarks are limited to how low-quality or insufficient sleep may have inflated his own learning times at certain dates ([ 8], p. 66) and as an explanation for the observed time-of-day effects; he learns faster in the morning than at other times. In other words, he acknowledges the effects of previous sleep on current learning, but he does not admit to the role of sleep in slowing down long-term forgetting. The formal analysis above suggests that the classic forgetting curve is not completely smooth but does show a jump at the 1 day retention interval. Current research on the effects of sleep on memory would predict such a jump, but for this particular type of experiment this remains to be established.

Acknowledgments

We would like to thank Annette de Groot and Jeroen Raaijmakers for helpful suggestions when writing this.

Author Contributions

Conceived and designed the experiments: JMJM JD. Performed the experiments: JD. Analyzed the data: JMJM JD. Contributed reagents/materials/analysis tools: JMJM JD. Wrote the paper: JMJM JD.

# data[].attributes.attachment_link.title
Replication and Analysis of Ebbinghaus' Forgetting Curve

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We present a successful replication of Ebbinghaus' classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data.

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Tips about the facilitator role: setting up a democratic space, making ground rules, actively listening, interventions facing challenges with participant engagement, actions steps for concrete outcomes, tools (for icebreaking, team building, energy boosting, brainstorming, decision making, maintaining momentum, randomizing) evaluation methods, responding to teams trust, fear, commitment, accountability & focus disfunction
link for other ressource

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Plantnet: Plant biodiversity
Foldit: Protein folding puzzle video game to solve computationnaly-limited problems (Cooper et al. 2010. Nature)
eBird: Bird sighting for science & conservation
Citizen science app: various app for collecting field data
Zooniverse: citizen science web portal

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Petit recueil de 25 moisissures argumentatives par le Collectif de recherche transdisciplinaire esprit critique et sciences (CORTECS)

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As a teacher/facilitator, identifying errors in reasoning is crucial to support reflexivity in learners.

Argumentative mold = formulation of arguments resulting from voluntary / involuntary errors of reasoning, having the appearance of logic and intended to support, invalidate or discredit a statement, an argument, a belief or a hypothesis. Concept developed in zetetics or scientific skepticism. 3 distinct categories: logical errors, attacks, disguises

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Petit recueil de non plus 18, ni 20 mais 24 moisissures argumentatives à utiliser sans modération lors des concours de mauvaise foi.

Nous avons découpé ces moisissures argumentatives en trois grandes catégories : les erreurs logiques, les attaques, et les travestissements.

[TOC]
Télécharger en pdf la mise en page magnifique du graphiste Francois-b.
Télécharger le document élaboré avec le collectif INDICE en pdf ( 25 pages avec pages blanches pour noter ou 16 pages, plus dense avec seulement les descriptions) ou en html pour copier plus facilement le texte.

Méthode : prendre un échantillon trop petit et en tirer une conclusion générale.

Mon voisin est un imbécile moustachu, donc tous les moustachus sont des imbéciles.

Les Chinois sont vachement sympas. J'en connais deux, ils sont trop cools.

Donald Trump est anti-mexicain, comme tous les habitants des États-Unis.

Exemples aggravés (menant au racisme ordinaire) :

Le Chinois est vachement sympa. Le Juif est roublard. L'Arabe est voleur. Le Picard est pédophile.

Méthode : raisonner à rebours, vers une cause possible parmi d'autres, vers un scénario préconçu ou vers la position que l'on souhaite prouver.

C'est fou, le melon est déjà prédécoupé pour être mangé en famille.

Le monde est trop bien foutu, c'est une preuve de l'existence d'une volonté divine.

Exemple aggravé :
3. Le Non sequitur (" qui ne suit pas les prémisses ")

Méthode : tirer une conclusion ne suivant pas logiquement les prémisses. Deux types d'argumentaires :

Si A est vraie, alors B est vraie. Or, B est vraie. Donc A est vraie.

Si A est vraie, alors B est vraie. Or, A est fausse. Donc B est fausse.

Attention : la conclusion peut être finalement juste, mais le raisonnement est faux.

Tous les consommateurs d'héroïne ont commencé par le haschisch. Tu fumes du haschisch, donc tu vas finir héroïnomane.

Française des Jeux : 100% des gagnants auront tenté leur chance (décomposé, cela donne : tous ceux qui ont gagné ont joué. Donc si tu joues, tu gagnes).

On m'a dit " Si tu ne manges pas ta soupe, tu finiras au bagne ", or je mange ma soupe, donc je n'irai pas au bagne.

4. L'analogie douteuse

Méthode : discréditer une situation en utilisant une situation de référence lui ressemblant de manière lointaine.

Le marché, c'est une jungle : manger ou être mangé. Être tigre ou être chèvre.

Le capitalisme est naturel et vouloir se battre contre lui, c'est comme vouloir empêcher la pluie de tomber.

Vous refusez de débattre avec les créationnistes, vous êtes anti-démocratique.

L'Obama Care c'est le Communisme !

Guillaume Meurice a déniché une analogie exemplairement douteuse lors d'une réunion de banquiers à Opera (Paris) en janvier 2018.
L'extrait :

https://cortecs.org/wp-content/uploads/2016/10/CorteX_analogie_decouvert_lion.mp3

Télécharger l'extrait
La chronique entière d'où il a été extrait : Le moment Meurice, France Inter, 17 janvier 2018.

https://cortecs.org/wp-content/uploads/2016/10/CorteX_Banquiers_Meurice_FrInter_17.01.2018.mp3
Télécharger la chronique : un banquier, un câlin Variante : le syndrome Galilée
Méthode : prétendre que quelque chose est vrai seulement parce qu'il n'a pas été démontré que c'était faux, ou que c'est faux parce qu'il n'a pas été démontré que c'était vrai.

Il est impossible de prouver que je n'ai pas été enlevé par des extraterrestres. Donc j'ai été enlevé par des extraterrestres (argument de Raël).

Il n'est pas démontré que les ondes wi-fi ne sont pas nocives. Donc elles le sont.

Méthode : après cela, donc à cause de cela. Confondre conséquence et postériorité.

B est arrivé après A

donc B a été causée par A.

J'ai bu une tisane, puis mon rhume a disparu ; donc c'est grâce à la tisane.

J'ai éternué, et hop, il a plu !

7. L'attaque personnelle (ou argumentum ad hominem)

Méthode : attaquer la personne (sur sa moralité, son caractère, sa nationalité, sa religion...) et non ses arguments.

Impossible de donner du crédit à Heidegger, vu ses affinités nazies.

Comment peut-on adhérer aux positions de Rousseau sur l'éducation, alors qu'il a abandonné ses propres enfants ?

Variante 1 : l'empoisonnement du puits

Méthode : sous-entendre qu'il y a un lien entre les traits de caractère d'une personne et les idées ou les arguments qu'elle met en avant.

critiquer les positions mystiques, ça ne m'étonne pas de vous, vous avez toujours été sans cœur

Méthode : jeter l'opprobre sur la personne en raison de choses qu'elle a faites ou dites par le passé, en révélant une incohérence de ses actes ou propositions antérieures avec les arguments qu'elle défend.

Comment Voltaire peut-il prétendre parler de l'égalité des Hommes alors qu'il avait investi dans le commerce des esclaves ?

Comment lire les romans de Günter Grass, alors qu'à 17 ans il a été enrolé dans les Waffen-SS ?

Vous vous dites concerné par la justice animale mais vous n'avez pas participé aux manifestation contre les chasses aux loups !

8. Le déshonneur par association (et son cas particulier : le reductio ad hitlerum)

Méthode : comparer l'interlocuteur ou ses positions à une situation ou à un personnage servant de repoussoir.

Voyons, si tu adhères à la théorie de Darwin, alors tu cautionnes la " sélection " des espèces, donc le darwinisme social et l'eugénisme, ce qui mène droit aux nazis.

Tu critiques la psychanalyse ? Comme Jean-Marie Le Pen !

Tu donnes la parole à quelqu'un qui défend la liberté d'expression, même pour des négationnistes ? Alors il est négationniste. Et toi, tu es cryptonégationniste !

Méthode : faire croire que si on adopte la position de l'interlocuteur, les pires conséquences, les pires menaces sont à craindre.

10. L'homme de paille (dite technique de l'épouvantail, ou strawman)

Méthode : travestir la position de l'interlocuteur en une autre, plus facile à réfuter ou à ridiculiser.

les théoriciens de l'évolution disent que la vie sur Terre est apparue par hasard. N'importe quoi ! Comment un être humain ou un éléphant pourraient apparaître de rien, comme ça ?

Les adversaires de l'astrologie prétendent que les astres n'ont pas d'influence sur nous. Allez donc demander aux marins si la Lune n'a pas d'influence sur les marées !

Vous critiquez l'acupuncture ? Mais qu'est-ce que vous avez contre la merveilleuse culture asiatique ?

Vous êtes communiste ? Pour ma part, j'exècre les goulags et les purges staliniennes !

11. L'argument du silence (ou argumentum a silentio)

Méthode : accuser l'interlocuteur d'ignorance d'un sujet parce qu'il ne dit rien dessus.

Je vois que vous ne connaissez pas bien la philosophie politique puisque vous passez sous silence les travaux de John Rawls, c'est inadmissible !

Avez-vous lu Rič o Mianajbaro ? Non ? Alors je n'ai que faire de votre avis.

Méthode : demander à l'interlocuteur de prouver que ce qu'on avance est faux.

Mais prouvez-moi donc que la politique migratoire actuelle est inefficace..

À vous de me démontrer que le monstre du Loch Ness n'existe pas.

13. L'enfumage

Utiliser des termes compliqués ou des faits méconnus pour que l'interlocuteur ne les comprenne pas, en espérant qu'il n'osera pas questionner pour ne pas passer pour un inculte.
Exemples :

Cette situation n'est pas sans rappeler la désastreuse confédération de Sénégambie.

Je ne reviendrai évidement pas sur les catégories nouménales de Kant, que tout élève de terminale connaît.

14. Le mille-feuille argumentatif

Empiler un foisonnement d'arguments faibles dans un maillage si serré qu'ils se renforcent réciproquement sans qu'on puisse les confronter entre eux.
Exemple :

Et le drapeau qui flotte ? Et les ombres non parallèles ? Et les croix de cadrage ? Et les ceintures de Van Allen ? Et Stanley Kubrick ? Tout ça prouve bien qu'on est jamais allé sur la Lune !

Variante : Le tsunami péremptoire
Submerger l'interlocuteur de conclusions ou de questions pour ne pas lui laisser le temps de répondre ou donner l'impression d'être plus convainquant, alors qu'aucun argument n'a été exposé.
Exemples :

Le tabac provoque des cancers, favorise des entreprises étasuniennes et les mégots polluent la planète !

Vos actions ont été néfastes pour les enseignants, pour les ouvriers, pour les paysans. Que comptez-vous faire pour réduire le chômage ?

15. L'attaque sur la forme

Relever une moisissure argumentative ou une faute de grammaire plutôt que d'argumenter avec de nouveaux arguments. La critique n'est pas suffisante, même si elle est nécessaire.
Exemples :

C'est un pathétique appel à la pitié alors je ne vais pas aider ces gens.

Comparer l'anarchisme à une religion, c'est une analogie douteuse donc je refuse d'écouter tes critiques de l'anarchisme.

16. Le plurium, ou effet gigogne

Méthode : poser une question (plurium interrogationum) ou une affirmation (plurium affirmatum) qui présupposent une prémisse qui n'a ni été prouvée ni acceptée par la personne qui doit répondre ou faire face.

Exemples :

Avez-vous arrêté de battre votre femme ?

La planète est-elle réellement malade ?

Les djihadistes sont-ils si fous que ça ?

Méthode : réduire abusivement le problème à deux choix pour conduire à une conclusion forcée.

Ceux qui ne sont pas avec nous sont contre nous (l'argument dit de George W. Bush).

Le sol sous-marin de Bimini a été fait soit par des humains, soit par des gens de l'Atlantide. Mais des humains n'auraient pas pu faire ça, donc c'est forcément des gens de l'Atlantide.

La crise : mythe ou réalité ?

Vous avez deux solutions, le suicide collectif, c'est à dire le repli iden-
titaire ou la voie européenne. (Vincent Peillon, 18 janvier 2017 sur BFM)

Méthode : faire une démonstration contenant déjà l'acceptation de sa conclusion.

Les recherches bactériologiques de l'Armée sont nécessaires, sinon comment pourrait-elle nous soigner en cas d'attaque militaire bactériologique ?

Jésus est né d'une vierge. Comment cela serait-il possible sans l'intervention divine ?

Méthode : déplacer le débat vers une position intenable par l'interlocuteur.

Remettre en cause le lobbying industriel sur les nanotechnologies ? Autant revenir à la lampe à huile et à la marine à voile.

Et tous ces gens qui font de la réflexologie, ce sont des imbéciles, peut-être ?

20. L'argument d'autorité (ou argumentum ad verecundiam)

Méthode : invoquer une personnalité faisant ou semblant faire autorité dans le domaine concerné.

Isaac Newton était un génie, et il croyait en Dieu, donc Dieu existe.

Si Samuel Umtiti voit un marabout, c'est bien que ça marche.

Méthode : Invoquer le grand nombre de personnes qui adhèrent à une idée.

Des millions de personnes regardent TF1, ça ne peut donc pas être si nul.

Des milliers de gens se servent de l'homéopathie, ça prouve bien que ça marche.

Des variantes peuvent être discrètes : " tout le monde sait bien que... ", " on sait tous ici que... "

22. L'appel à la pitié (ou argumentum ad misericordiam)

Méthode : plaider des circonstances atténuantes ou particulières qui suscitent de la sympathie et donc cherchent à endormir les critères d'évaluation de l'interlocuteur.

Roman Polanski, il faut le défendre, il a beaucoup souffert. On ne peut pas accuser aussi gravement quelqu'un qui a autant de talent (suite au procès pour viol sur mineure)

Bien sûr, le tordeur de métal Uri Geller a triché, mais sous la pression que lui mettaient les scientifiques, on comprend qu'il en soit venu là.

Sarkozy a été pris en grippe par tous les médias alors qu'il aurait dû bénéficier de la présomption d'innocence !

Projeter des attentes ou volontés sur une entité fictive qui soutient l'argumentation sans que l'opinion de celle-ci ne puisse être consultée, comme la Patrie, Dieu, la Planète, les enfants, la survie de l'espèce,...

Je me bats contre le mariage homosexuel pour protéger nos enfants avant tout.

Si j'ai tué ces terroristes, ce n'était pas par haine, mais pour défendre les valeurs de ma patrie.

Ce travail a été initié par Richard Monvoisin, vice-champion mondial de mauvaise foi 2008, et Stanislas Antczak, champion intersidéral 2007. Il a été testé plusieurs fois lors des concours de mauvaise foi d'Ultimate Z, l'université d'été de l'Observatoire Zététique. Le modèle a été ensuite remanié, et utilisé par différents collectifs dans des débats féministes et lors d'un atelier d'économie populaire à Antigone, Grenoble. Il a été distribué au public dans la disputatio N°1. Il a été ensuite agrémenté et retravaillé avec le collectif INDICE. C'est ce qui est génial avec l'outillage critique : non seulement c'est compossible (offrir un savoir ne prive pas celui qui offre), mais c'est forcément assainissant, surtout dans un contexte de campagne électorale. Merci à Franck Villard, Nicolas Vivant, François Blaire, Raël, les frères Bogdanoff, Bernardin de Saint-Pierre, Jules César et tant d'autres pour leur contribution.

Pour aller plus loin : certains sophismes sont davantage détaillés dans l'article Logique - Le monde de sophisme.

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Petit recueil de 25 moisissures argumentatives pour concours de mauvaise foi

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Petit recueil de non plus 18, ni 20 mais 24 moisissures argumentatives à utiliser sans modération lors des concours de mauvaise foi. Nous avons découpé ces moisissures argumentatives en trois grandes catégories : les erreurs logiques, les attaques, et les travestissements. [TOC] Télécharger en pdf la mise en page magnifique du graphiste Francois-b.

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- Ancestors: The Humankind Odyssey: Survival open-world game about human evolution link
- Eco: simulation game for environnemental consciousness surrounding ecosystems  link
- Sandbrook et al. 2014: research article about pros & cons of digital games for biodiversity conservation
- Plague inc: strategy simulation game about pathogen spread and pandemics link link
- Papers, please: game about immigration & ethics link link
- This war of mine: strategy survival game about civilian life in times of war link link
- Sid Meier's Civilization: Strategy game for history & geography link link

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The art of questioning is crucial for teachers / facilitators to support learners' reflexivity.

Questions for teaching: Clarifying; Probing; Thinking Over Time; Extending; Deepening; Transfer; Contextualizing; Perspective-Based; Concrete; Metaphorical & Abstract; Compare & Contrast; Claim/Critique & Defend; Cause & Effect; Open-Ended; Closed; Leading; Loaded; Dichotomous; Display; Referential; Rhetorical; Epistemic; Divergent; Inductive; Deductive; 5 Ws Questions. link

Teachers questions - types and significance in science education. link

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link

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- Neoliberal meritocracy, racialization & transnationalism. link
- How Do Young Children Explain Differences in the Classroom? Implications for Achievement, Motivation, and Educational Equity. link
--> Recommendations for preventing inherence bias:
1) Provide extrinsic explanations for differences; 2) Block the demotivating implications of inherent explanations; 3) Minimize the salience of differences in the classroom

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- The guide above provides tools & guidelines about self-awareness, empathy, classroom climate, pedagogical choices, leveraging networks.
- Paper about implicit stereotypes and the predictive brain: cognition and culture in "biased" perception. link

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Promoting Engagement &Self-Efficacy Fostering Sense of Belonging Developing Empathy Philosophy, Scope, and Definitions Reference List Inclusive Teaching DevelopingSelf-Awareness Classroom Climate Pedagogical Choices Leveraging Networks Checklist

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Allows for: opensource equivalent of google drive

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What is it?

A pad is an online collaborative text editor. The contributions of each user are indicated by a colour code, appear on the screen in real time and are recorded as they are typed.
Want a demonstration?

How does it work?

Create a pad.

Start to write your text

and invite your collaborators.

Each participant is distinguished by a color

and can chat with the group.

If necessary, restore an old version from history

and when your work is done, export it.

Private Pads

You have the possibility to create a password protected account and having a space dedicated to the management of your pads and their access rights.

Limitations

Public pads have a limited lifespan to avoid growing our databases indefinitely.
The expiry date is defined according to the date of last modification of the pad.
To keep your documents, consider using the Import/Export menu.

This service is offered subject to acceptance of our terms and conditions and may be moderated according to our moderation policy.

Framapad, one of several projects

Framapad, an emancipatory tool

The economy of your attention

The Framapad is free, without ads or click traps. Our goal is to host a tool that serves you and does not use you.

Contrary to the adage, here, you are not the product.

Controlling your data

Framasoft collects as little data as possible.

You have complete autonomy over it.

We have no interest in tracking your behaviour, nor in exploiting it.

Respecting your freedoms

Framapad is based a Free-libre software¹. Its code is transparent, publicly auditable and there are no hidden features.

It is built by the community, and everyone is free to fork it and lead the project in new directions.
¹ Etherpad - License Apache 2.0

Want to go further in coding and autonomy?

Framapad is an installation of the free software Etherpad on the Framasoft servers. Its free license allows you to study its code, contribute to it and install it on your own server.

Why offer Framapad?

Using Framapad is the first step on the road to your digital autonomy.

Entrusting your data to a host implies having reasons to to trust it. Framasoft commits to clear conditions¹ to host services with respect to your integrity.
¹ see our Terms of Use and our charter

Creating a resilient ecosystem

Framasoft is one of many actors offering online services hosted with a strong ethic, with the objective of decentralisation.

To take a step further in your digital emancipation, find the same service hosted near you by one of the members of the Collective of independant, transparent, open, neutral and ethical hosters: the CHATONS!

Contributing to the digital commons

Behind a service like Framapad there is a political proposal: exploring an alternative to surveillance capitalism by experimenting with the contribution society.

By offering uses, feedback and contributions to Etherpad, we participate in maintaining the vibrancy of a common resource.

Who is behind Framapad?

Framapad is proposed by a French not-for-profit association: Framasoft

Created in 2004, the association is now dedicated to popular education on the challenges of the digital age and the cultural commons. Our small structure (38 including 10) is known the De-google-ify Internet project (16 ethical and alternative online tools) and for the development of PeerTube, Mobilizon, etc. Recognized as being of general interest Framasoft is funded at 93% by your donations, which are tax deductible for French taxpayers.

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Racism. no way! a guide for Australians schools

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- Systemic racism in higher education link
- Anti-racism curriculum case study link
- Anti-racism in U.S early childhood education link
- From Laissez-Faire to Anti-Discrimination link
- Who believes in me? The effect of student–teacher demographic match on teacher expectations link

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- Gender stereotypes. link
- Cortecs. Ecriture inclusive: Raisons scientifiques et morales. link
- Can gender-fair language reduce gender stereotyping and discrimination? link
- web docu l'école du genre link
- outil pour l'école link

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Differences between the social classes from thesociologyguy.com

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- Unequal Childhoods: A. Lareau link
- La fabrique des inégalités : évolutions, contradictions, paradoxes » Épisode 6/10 : Enfances de classe link
- Negotiating opportunities: how the middle-class secures advantages in school link
- The psychology of social class: How socioeconomic status impacts thought, feelings, and behaviour link

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Magana. 2022. Journal of Engineering Education. The role of frameworks in engineering education research

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Frameworks can be combined as a tool to ground and guide research and design. They communicate knowledge about engineering education and can make philosophical, epistemological and theoretical presuppositions explicit. link

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The empathy map is a human-centered tool designed to help develop shared understanding and empathy for other

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UX research methods can help generating insights about your users, their behavior, motivations, and needs. They can be used by pedagogical engineers to develop learner-centered experiences and foster reflexivity for the teachers they work with.

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Allows for: multimedia learning, teaching about systems, mindmap, conference video call, non-linear asynchrone learning, prototyping, decision making & voting, collaborative & contributive work

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Plus de 20 millions d'utilisateurs nous font confiance

Que vos équipes soient au même endroit, distribuées ou en télétravail , Miro offre une expérience intuitive et motivante, avec plusieurs options pour un travail en équipe en temps réel ou asynchrone sur un tableau en ligne.

Comment utiliser le
tableau en ligne Miro

Miro est plus qu'un simple tableau blanc intéractif, c'est un canvas infini qui vous aide à concevoir des idées, à élaborer des stratégies, à vous organiser et à travailler avec votre équipe.

Collaboration avec votre équipe

Invitez des utilisateurs à rejoindre votre tableau pour une session de brainstorming, prenez des notes et suivez vos projets. Utilisez la visioconférence, le partage d'écran et le mode présentation pour organiser des réunions animées. Miro est ultra-rapide, permettant ainsi à de nombreuses personnes de travailler en même temps.

Passez vos post-it au format numérique

Parfois, vous avez besoin de lancer des idées sur un tableau blanc physique. Mais quand vous devez partager ces idées avec vos collègues en ligne, tout transcrire au format numérique devient compliqué et chronophage. Avec la capture de post-it, vous pouvez instantanément prendre une photo de votre tableau et le transformer en post-it numériques modifiables sur Miro.

Tout ce dont vous avez besoin pour collaborer en ligne

Le canvas infini de Miro vous donne la liberté de choisir comment vous l'utilisez pour travailler avec votre équipe. Que ce soit pour organiser un brainstorming digital, rédiger le compte rendu d'une réunion, faire cours à distance ou gérer un workflow agile, les possibilités de collaboration sont infinies.

Commencez avec un modèle

Vous ne savez pas comment organiser votre tableau blanc en ligne ? Parcourez les modèles prédéfinis de Miro. Vous pouvez choisir parmi des cartes, diagrammes, outils de projet, tableaux Kanban et activités prêts à l'emploi, ou créer un modèle personnalisé et l'enregistrer pour le réutiliser plus tard.

Add ideas, digitize sticky notes, and leave comments on the go with Miro mobile app

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Tableau en ligne | Tableau blanc intéractif en ligne collaboratif gratuit | Miro

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Que vos équipes soient au même endroit, distribuées ou en télétravail , Miro offre une expérience intuitive et motivante, avec plusieurs options pour un travail en équipe en temps réel ou asynchrone sur un tableau en ligne.

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Allows for: interactive polls, quizzes, word clouds
link

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Your browser either doesn't support Javascript, or you've turned it off. To use Mentimeter, please make sure to turn Javascript on. How do I enable Javascript?

Gain valuable insights from everyone around you with Word Clouds, Polls, and Quizzes.

Unlocking success with Mentimeter

For work

Maximize the value of your work-related meetings. Gather valuable input, feedback, and insights from your colleagues to make better decisions together!
Read more

For education

Enhance learning outcomes and engage every student in the classroom. Experiment with modern teaching methods using Mentimeter's features!
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Works with your favorite apps

Turn tough crowds into engaged audiences

Build an immediate connection with your audience and make them a part of your presentation.

Create an interactive experience letting everyone vote, ask questions, and interact throughout.

Create truly unique presentations

Use a wide range of interactive slides to pose questions, gather feedback, and connect with your audience.

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"Mentimeter allows everyone to ask questions, to get clarification or a clearer understanding on subjects resulting in a more fulfilling learning experience." Teresa Delfin, California State Polytechnic University Explore more stories

Want to become a better presenter?

Impress with Interactive Presentations

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Interact with your audience using real-time voting. No installations or downloads required and it's free!

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Free app for design: presentation, videos, social networks

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Allows for: graphic design

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Allows for: recording both your screen & camera

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Free screen recorder for Mac and PC | Loom

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Best free online screen recording tool with advanced video editing and video storage trusted by over 21 million people and easy sharing from Loom.com.

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Allows for: audio recording, editing & sharing

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Allows for: recording, editing, sharing audio & video, community development

Research lit about - TikTok for science communication link; Incorporating TikTok in higher education link; macro- and nano-learning link

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Log in or sign up for an account on TikTok. Start watching to discover real people and real videos that will make your day.

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Library of apps for teaching: free; simple to use; answer pedagogical needs for exposing, practicing, exchanging, evaluating; consider to some extents accessibility criteria WAI for sensorially handicapped students

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Le Cnam - Living Lab Sofa - Le Laboratoire des usages sociaux éducatifs du numerique

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Nous conduisons une veille sur Internet afin d'identifier un certain nombre d'applications du Web répondant initialement le plus souvent à des usages de communication. Nous en imaginons un "détournement" à des fins pédagogiques. L'objectif des "outils du Web" de la boîte à outils, est de favoriser l'autonomie des enseignant-e-s dans la production de contenus pédagogiques servant à l'animation de leurs cours. La variété des usages, des situations d'apprentissage, des modalités pédagogiques (Foad, hybride, présentiel), des contenus et des publics d'apprenants est prise en compte.

Les applications retenues répondent à des critères spécifiques :

gratuites (version freemium),

simples d'usage,

répondant à des besoins pédagogiques selon 4 critères : exposer, faire pratiquer, échanger, évaluer.

répondant dans la mesure du possible aux critères d'accessibilité WAI pour en faciliter l'accès à des élèves en situation de handicap sensoriel.

Les outils du Web

Les outils pour l'escape game

Bibliothèques d'images et de pictos

Moteurs de recherche éco-responsables

Les posters de facilitation visuelle

Le Cnam Pays de la Loire, pilote du Living Lab Sofa du Cnam :

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La boîte à outils de la formation - Le Cnam - Living Lab Sofa

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Nous conduisons une veille sur Internet afin d'identifier un certain nombre d'applications du Web répondant initialement le plus souvent à des usages de communication. Nous en imaginons un "détournement" à des fins pédagogiques.

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Designing good PBL problems link
Designing assessments in PBL link link
Programmatic assessment link link link

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Online Project-Based Learning and Formative Assessment link
Assessing participation: rubrics, peer evaluation, self-evaluations & reflections link
More resources for assessment in project-based learning link link

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The research literature on evaluation indicates the importance of “assessment for learning” rather than “assessment of learning” (link).  Some research also defends that formative and summative assessment need to work in harmony and should not be seen as contrary to each other (link link).

Different forms of evaluations link link

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Self-assessment supports learners in exploring their own strengths and weaknesses. It is a comparative process (to specific standards / previous performances / the performance of others). It is a key component of “assessment as learning,” where students apply self-regulatory processes in their learning. link

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ABSTRACT
In recent years, research and practice focused on academics and students working in partnership to co-design learning and teaching in higher education has increased [Deeley and Bovill 2017. 'Staff student partnership in assessment: enhancing assessment literacy through democratic practices.' Assessment & Evaluation in Higher Education 42 (3): 463-477. doi: 10.1080/02602938.2015.1126551]. Student generation of content has been advocated as a means of fostering deep learning and high levels of students engagement, leading to enhanced conceptual understanding [Draper 2009. 'Catalytic Assessment: Understanding how MCQs and EVS can Foster Deep Learning.' British Journal of Educational Technology 40 (2): 285-293.]. However, within the discourse exploring staff-student co-creation, some concerns have been raised about whether students ought to, or indeed can, meaningfully act as partners in assessment [Cook-Sather, Bovill, and Felten 2014. Engaging Students as Partners in Learning and Teaching. San Francisco, CA: Jossey Bass.]. This paper describes the introduction of a co-creation assignment into an undergraduate tax module. Students' and instructors' perceptions of several aspects of the assignment were examined, facilitating the cataloguing and elucidation of the tensions involved in assessment co-creation. The paper suggests techniques to manage these challenges effectively, thereby providing a case study for others with an interest in implementing assessment co-creation within their own disciplinary context.

Disclosure statement

No potential conflict of interest was reported by the authors.

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Assessment co-creation: an exploratory analysis of opportunities and challenges based on student and instructor perspectives

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ABSTRACT In recent years, research and practice focused on academics and students working in partnership to co-design learning and teaching in higher education has increased [Deeley and Bovill 2017. 'Staff student partnership in assessment: enhancing assessment literacy through democratic practices.' Assessment & Evaluation in Higher Education 42 (3): 463-477. doi: 10.1080/02602938.2015.1126551].

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- Piaget's Cognitive Developmental Theory: Critical Review link
- Generative learning: which strategies for what age? link
- Preferring "generational humility" over the myth of the Millennial learner link
- Challenges faced by adult learners in online distance education: A literature review link

- Instructional methods used to help elder learners link

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Evaluate your teaching link link
Student satisfaction link
About student evaluation of teaching link link