Week 1

Week 1 part 1

Key Concepts, References, and Readings

NOTE: All these references and readings are optional.

Video 1: Introduction to Teaching Online

Key Concepts

Research has shown that students can learn even better online than they can in the traditional face-to-face classroom. Teaching online does NOT have to mean reduced effectiveness.
Online teaching that emphasizes active practice while tightening up lecture content appears to be highly efficient.

References

General references

Oakley B, Rogowsky B, Sejnowski T. Uncommon Sense Teaching: Penguin Random House; 2021. See in particular Chapter 9: Online Teaching with Personality and Flair.
Mayer, R and Fiorella, L. The Cambridge Handbook of Multimedia Learning. 3rd ed: Cambridge University Press, 2021.
Prince, M, et al. “Active Student Engagement in Online STEM Classes: Approaches and Recommendations.” Advances in Engineering Education 8, (2020).
Shachar, M., & Neumann, Y. (2010). “Twenty years of research on the academic performance differences between traditional and distance learning: Summative meta–analysis and trend examination.” MERLOT Journal of Online Learning and Teaching, 6(2).

Terry and Barb, writing, researching, and thinking about world of teaching and learning online

Chen K-Z, Oakley B. “Redeveloping a global MOOC to be more locally relevant: design-based research.” International Journal of Educational Technology in Higher Education. 2020;17(1):1-22.
Oakley, B. A., & Sejnowski, T. J. (2019). “What we learned from creating one of the world’s most popular MOOCs.” npj Science of Learning, 4, 1-7.
Oakley B. Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential: Penguin-Random House; 2017. See in particular chapters 11 and 12 on MOOCs and creating online courses.
Oakley B, Poole D, Nestor M. “Creating a sticky MOOC.” OLC Journal of Online Learning. 2016;20(1):1-12.

Students can learn as well or even better online

See pages 6-7 of Joyner, D and Isbell, C. The Distributed Classroom: MIT Press, 2021 for a contextualized discussion, citing the research literature, of how students learn better online. We quote here:

“To illustrate the range of possible differences [between online and traditional education], we take the example of an online undergraduate class we launched in January 2017 at Georgia Tech. In developing this class, we paid close attention to the research showing that learning outcomes often tend to lag in online compared to traditional classes. We wanted to ensure that the online class—CS1301: Introduction to Computing—could promise comparable learning gains to the traditional version of the same curriculum before rolling it out to a larger audience. The class we produced ended up turning out students who learned as much as or more than students in a traditional version of the class.8 Other experiments at MIT and Carnegie Mellon have found similar results.9 The class has been offered every semester (fifteen terms total) since, totaling over three thousand course completers for credit, and has also been launched as a MOOC; over ten thousand students have completed a MOOC version of the course. This scale was possible only because of the favorable learning gains we observed in our experiments over the first couple of years of delivering the course: without evidence of the learning outcomes, we would have been reluctant to expand the course so heavily.

“These results run counter to an influential thread of research about online education, where the finding has been that outcomes suffer in online environments compared to traditional environments. In response to this result, some have argued that students in selective and prestigious research institutions like MIT, Georgia Tech, and Carnegie Mellon are themselves better prepared to succeed in online classes; they possess the discipline and self-regulation skills necessary to monitor their own progress with limited external structures ensuring their continued engagement.10 Much of the research finding poorer outcomes in online classes comes from community colleges and MOOC providers, and so some argue that the achievement difference is due to differences in the students. Online classes, then, could contribute to a widening of the achievement gap as they allow already well-educated students to move forward even faster based on their ability to succeed in more flexibly-available online courses.

“Others—ourselves included—pose a different explanation. These large research institutions have and are devoting significant resources to developing online initiatives. When David developed our online Introduction to Computing class, we spent a full year writing the textbook, filming the lectures, and developing the initial assessments. We had a team of nearly a dozen people supporting David, including video producers, textbook copyeditors, project managers, technologists, and teaching assistants; in many ways, we had far more support than even traditional face-to-face classes have, before we even consider David’s own prior experience teaching online. Our online master’s-level courses are similarly developed by teams of professors, teaching assistants, instructional technologists, and project managers using world-class facilities. It is perhaps unsurprising that such a large investment of resources creates an educational experience leading to superior learning outcomes.”

For a full exploration of the learning outcomes of the two versions of the class, see David Joyner, “Toward CS1 at Scale: Building and Testing a MOOC-for-Credit Candidate,” in Proceedings of the Fifth Annual ACM Conference on Learning at Scale (New York: ACM, 2018); and David Joyner and Melinda McDaniel, “Replicating and Unraveling Performance and Behavioral Differences between an Online and a Traditional CS Course,” in Proceedings of the ACM Conference on Global Computing Education, 157–163 (New York: ACM, 2019).
For information on Carnegie Mellon’s results, see: M. Lovett, O. Meyer, and C. Thille, “The Open Learning Initiative: Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning,” Journal of Interactive Media in Education (2008). Results from MIT’s similar exploration can be found at Piotr F. Mitros, Khurram K. Afridi, Gerald J. Sussman, Chris J. Terman, Jacob K. White, Lyla Fischer, and Anant Agarwal, “Teaching Electronic Circuits Online: Lessons from MITx’s 6.002 x on edX,” in Proceedings of the 2013 IEEE International Symposium on Circuits and Systems, 2763–2766 (Piscataway, NJ: IEEE, 2013).
For more on the relationship between self-regulation and success in online classes, see Richard Lynch and Myron Dembo, “The Relationship between Self-Regulation and Online Learning in a Blended Learning Context,” International Review of Research in Open and Distributed Learning 5, no. 2 (2004); Rachel L. Bradley, Blaine L. Browne, and Heather M. Kelley, “Examining the Influence of Self-Efficacy and Self-Regulation in Online Learning,” College Student Journal 51, no. 4 (2017): 518–530; and Heather Kauffman, “A Review of Predictive Factors of Student Success in and Satisfaction with Online Learning,” Research in Learning Technology 23 (2015).

Shachar, M., & Neumann, Y. (2010). “Twenty years of research on the academic performance differences between traditional and distance learning: Summative meta–analysis and trend examination.” MERLOT Journal of Online Learning and Teaching, 6(2).
Sun, A., & Chen, X. (2016). “Online education and its effective practice: A research review.” Journal of Information Technology Education: Research, 15, 157–190.

Video 2: Be Wary of Checkbox Advice in Creating Online Courses—The Expertise Reversal Effect and Schemas

Key Concepts

The expertise reversal effect is observed when certain teaching methods can impede the learning of already-skilled learners.
Learning involves making connections between neurons in long-term memory.
A set of connected neurons involving our knowledge on a topic or in a subject area form what’s called a “schema.”
Wading through a lot of material just to make a tiny adjustment in a schema is something that students (as well as we teachers) prefer to avoid.

References

Schemas

Babichev, A and Dabaghian, YA. “Topological schemas of memory spaces.” Frontiers in Computational Neuroscience 12, (2018): 27.
Frankland, PW, et al. “The neurobiological foundation of memory retrieval.” Nat Neurosci 22, no. 10 (2019): 1576-1585.
Ghosh, VE and Gilboa, A. “What is a memory schema? A historical perspective on current neuroscience literature.” Neuropsychologia 53, (2014): 104-114.
Gilboa, A and Marlatte, H. “Neurobiology of schemas and schema-mediated memory.” Trends Cogn Sci 21, no. 8 (2017): 618-631.
Josselyn, SA and Tonegawa, S. “Memory engrams: Recalling the past and imagining the future.” Science 367, no. 6473 (2020): eaaw4325.
Richards, BA, et al. “Patterns across multiple memories are identified over time.” Nat Neurosci 17, no. 7 (2014): 981-986.
Tse, D, et al. “Schema-dependent gene activation and memory encoding in neocortex.” Science 333, no. 6044 (2011): 891-895.

Expertise reversal effect

Armougum, A, et al. “Expertise reversal effect: Cost of generating new schemas.” _Computers in Human Behavio_r 111, (2020): 106406.
Kalyuga, S and Sweller, J. “Cognitive load and expertise reversal.” In The Cambridge Handbook of Expertise and Expert Performance, edited by K. A. Ericsson, R. R. Hoffman, A. Kozbelt and A. M. Williams: Cambridge University Press, 2018.
Sweller, J, et al. “The expertise reversal effect.” Educational Psychologist 38, no. 1 (2003): 23-31.

Video 3: Low-stakes Onboarding Quizzes and Avoiding the Horrors of Goodhart’s Law

Key Concepts

A low stakes on-boarding quiz is a great way to help students learn the most important aspects of your course and your teaching style.
If you are teaching a “flipped” class, you may wish to reduce the average scores for the online quizzes to the average scores for the in-person quizzes, since it can be easier to control for cheating in a traditional, in-person classroom setting.
Goodhart’s Law states that “When a measure becomes a target, it ceases to be a good measure.” This law reminds us that it can be all-too-easy to fall into a “checkbox” approach that seems to satisfy all the requirements, but in reality, produces a poor online learning experience for students.

References

Expertise reversal effect

Armougum, A, et al. “Expertise reversal effect: Cost of generating new schemas.” _Computers in Human Behavio_r 111, (2020): 106406.
Kalyuga, S and Sweller, J. “Cognitive load and expertise reversal.” In The Cambridge Handbook of Expertise and Expert Performance, edited by K. A. Ericsson, R. R. Hoffman, A. Kozbelt and A. M. Williams: Cambridge University Press, 2018.
Sweller, J, et al. “The expertise reversal effect.” Educational Psychologist 38, no. 1 (2003): 23-31.

The value of practice tests

Adesope, OO, et al. “Rethinking the use of tests: A meta-analysis of practice testing.” Review of Educational Research 87, no. 3 (2017): 659-701.

Goodhart’s Law

The Wikipedia article on Goodhart’s Law provides a good general overview and further references.

Video 4: Create a Quick Walkthrough of Your Course

Key Concepts

Creating a screencast walkthrough can help onboard your students more quickly and easily into your course.

References

Berdahl, L. (2021, August 27). “How to get students to read your syllabus.” University Affairs.
Dachner, AM and Saxton, BM. “If you don’t care, then why should I? The influence of instructor commitment on student satisfaction and commitment.” Journal of Management Education 39, no. 5 (2015): 549-571.
Fitton, T. “Touring the online learning environment: An exploration using screencast-o-matic.” European Journal of Open, Distance and E-learning, (2018).
Hartmann, T, et al. “Spatial presence theory: State of the art and challenges ahead.” In Immersed in Media: Telepresence Theory, Measurement & Technology, edited by Matthew Lombard, Frank Biocca, Jonathan Freeman, Wijnand Ijsselsteijn and Rachel J. Schaevitz, 115-135. Cham: Springer International Publishing, 2015.

Week 1 part 2

Key Concepts, References, and Readings

NOTE: All these references and readings are optional

Video 5: Schemas of Identity Can Provide Motivation—or Demotivation

Key Concepts

To get students to engage with one another, you must first get them to engage with you.
Taken together, all of our schemas help provide an overarching sense involving who we are—our identity. Our identity provides fundamental motivations for our actions.
Our job as teachers is to help students develop as well as change their schemas.

References

Costa, K. 99 Tips for Creating Simple and Sustainable Educational Videos: A Guide for Online Teachers and Flipped Classes: Stylus Publishing, LLC, 2020.

Students focus (first) on videos

Breslow, L, et al. “Studying learning in the worldwide classroom research into edX’s first MOOC.” RPA Journal 8, (2013): 13-25.
Ekstrom, A. “Navigation in Virtual Space: Psychological and Neural Aspects,” In: Koob G.F., Le Moal M. and Thompson R.F. (eds.) Encyclopedia of Behavioral Neuroscience, volume 2, pp. 286–293 Oxford: Academic Press
Hsin, W-J and Cigas, J. “Short videos improve student learning in online education.” Journal of Computing Sciences in Colleges 28, no. 5 (2013): 253-259.
Kizilcec, RF, et al. “Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses.” In Proceedings of the Third International Conference on Learning Analytics and Knowledge, edited by D Suthers, K Verbert, E Duval and X Ochoa, 170-179. Leuven, Belgium, 2013.
Oakley, BA and Sejnowski, TJ. “What we learned from creating one of the world’s most popular MOOCs.” npj Science of Learning, 4, Article 7 (2019): 1-7.
Seaton, DT, et al. “Who does what in a massive open online course?” Commun ACM 57, no. 4 (2014): 58-65.

Identity (versus goal-based) habit formation

Melnikoff, DE, et al. “Editorial: On the nature and scope of habits and model-free control.” Frontiers in Psychology 12, (2021): 760841.
Oyserman, D, et al. “Identity-based motivation and health.” Journal of Personality and Social Psychology 93, no. 6 (2007): 1011. This paper is a classic with relation to identity-based motivation.
Oyserman, D, et al. “An identity-based motivation framework for self-regulation.” Psychological Inquiry 28, no. 2-3 (2017): 139-147.
Oyserman, D. Pathways to Success through Identity-Based Motivation: Oxford University Press, USA, 2015.
Williamson, LZ and Wilkowski, BM. “What we repeatedly do: Evaluating the determinants and consequences of habit enactment during daily goal‐pursuit.” British Journal of Psychology 113, no. 1 (2022): 1-24. This paper emphasizes the importance of tiny habitual actions that support long-term goals.

Video 6: The Imposter Syndrome in Online Teaching—Meet Television Star Audrey Lawrence

Key Concepts

Students can change their innermost core beliefs about themselves—the schemas underlying their identity—through online learning.

References

Audrey Lawrence: https://www.audreylawrence.org/
Woolley, K and Fishbach, A. “Motivating personal growth by seeking discomfort.” Psychological Science 33, no. 4 (2022): 510-523.

Week 2 part 1

Key Concepts, References, and Readings

NOTE: All these references and readings are optional

Chapter 6 of Uncommon Sense Teaching is especially helpful in providing helpful information related to procedural system, habit-based learning.

Video 1: On (and Off) Camera Habits and the Declarative-Procedural Learning Systems: Why Knowing and Doing are Not the Same Thing

Key Concepts

Two main pathways are used to deposit or tweak links in our neural schemas: The declarative and the procedural pathways.
The declarative pathway takes our conscious thoughts from the front of the brain through the hippocampus into long-term memory.
The procedural pathway of learning flows through the basal ganglia. We are not conscious of what we are doing when we are using the procedural sets of links. Still, these types of links underpin our ability to do highly sophisticated activities, like solve a Rubik’s Cube or speak our native language.
Procedurally-laid links underpin our habits. They can be hard to change.

References

For general overview of the declarative (Chapter 3) and procedural (Chapter 6) systems and how they relate to long-term memory, see Oakley, B, Rogowski, B, Sejnowski, T, Uncommon Sense Teaching: Penguin Random House, 2021, and the references therein. Chapter 9: Online teaching with personality and flair.
For references related to schemas, see Week 1, Video 2.

Transactional Distance

Shearer, RL and Park, E. “The theory of transactional distance.” In Open and Distance Education Theory Revisited, 31-38: Springer, 2019.
Stein, DS, et al. “Bridging the transactional distance gap in online learning environments.” The American Journal of Distance Education 19, no. 2 (2005): 105-118.
Moore, MG. “Theory of transactional distance.” Theoretical Principles of Distance Education 1, (1993): 22-38.

Habit

We will get more into habit later this week, so look there for many more references on this topic. In the meantime, two good popular books about habit include:

Duhigg, C. The Power of Habit. NY: Random House, 2012.
Wood, W. Good Habits, Bad Habits: The Science of Making Positive Changes that Stick: Farrar, Straus and Giroux, 2019. Dr. Wood is an important researcher in this area, so her book is particularly worthwhile.

The Difficulties of “Unlearning”

Allaire-Duquette, G, et al. “An fMRI study of scientists with a Ph. D. in physics confronted with naive ideas in science.” npj Science of Learning 6, no. 1 (2021): 1-12. This paper observes: “Results suggest that naive ideas are likely to persist, even after completing a Ph.D. Advanced experts may still rely on high order executive functions like inhibitory control to overcome naive ideas when the context requires it.”
Dunbar, K, et al. “Do naïve theories ever go away? Using brain and behavior to understand changes in concepts.” In Carnegie Mellon Symposia on Cognition. Thinking with Data, edited by M. C. Lovett and P. Shah, 193-206: Lawrence Erlbaum Associates Publishers, 2007.
Halloun, IA and Hestenes, D. “The initial knowledge state of college physics students.” American Journal of Physics 53, no. 11 (1985): 1043-1055.
NOVA. “The science of smart: How to unlearn mistaken ideas.” PBS (2013).
Scherr, RE, et al. “The challenge of changing deeply held student beliefs about the relativity of simultaneity.” American Journal of Physics 70, no. 12 (2002): 1238-1248.
Shtulman, A and Valcarcel, J. “Scientific knowledge suppresses but does not supplant earlier intuitions.” Cognition 124, no. 2 (2012): 209-215.

Video 2: Lights, Camera, Action—Oops!

Key Concepts

Your microphone and ambient environment, camera, camera positioning, and lights have an important effect on your ability to teach well online. The equipment you choose to use is probably the place where the smallest decisions can have the biggest impact.

References

Coursera has put together an awesome listing of lighting, microphones, cameras, recording and editing software and the like that you can find here: bit.ly/Coursera-Home-Production-Guidance.
Schneider, S., et al. (2016). “Decorative pictures and emotional design in multimedia learning.” Learning and Instruction 44: 65-73.

Video 3: “You’re Ready for your Close-Up… Wait, Too Close!”

Key Concepts

Your audio quality is much more important than your video quality when you are teaching online.
Your visual background can either be simple or can provide hints for your students about your likes and interests.
Just before going on camera, recheck your:
- Lighting
- Camera framing (common errors are the Frankenstein and gopher effects, or having the head too close to the camera)
- Sound (eliminate the noise of fans if possible)

References

Coursera’s listing of lighting, microphones, cameras, recording and editing software: bit.ly/Coursera-Home-Production-Guidance.
“Here’s Why Movie Dialogue Has Gotten More Difficult To Understand (And Three Ways To Fix It),” by Ben Pearson, Slashfilm, 2022.

Video 4: Breaking Bad Habits When It Comes to Teaching Online—and in Everyday Life

Key Concepts

The best way to change a bad habit—like how you position yourself on camera—is to bring the change you want to conscious awareness, for example, using reminder post-it notes.
Retrieval practice (“recall”) and spaced repetition help to lay new declarative sets of links and, over time, turn those declarative links into procedural links.

References

See the references for Week 2, Video 1

Week 2 part 2

NOTE: All these references and readings are optional

Video 5: Talk to the Hand—the Power of Gesture to Help Form Mental Models

Key Concepts

Mental models are the thoughts we are holding in working memory. We develop mental models related to concepts, ideas or events we are trying to follow or understand.
Our gestures and movements can help us develop mental models because they activate a rich network of neurons—our schemas—of remembered experiences that allows us to think more deeply about the ideas we are considering.
When people watch another person move or gesture, their neural networks and schemas are subtly activated to mirror the person they are watching. This is called the “mirror rule.” These mirrored activations help with both mimicking and the development of a mental model.
Whenever you have a success, your brain learns from it and programs that learning into your neural networks to help you repeat that success. This has been termed the “success rule.”

General References

Al-Diban, S. “Mental models.” In Encyclopedia of the Sciences of Learning, edited by Norbert M. Seel, 2200-2204. Boston, MA: Springer US, 2012.
Beaubien, R and Parrish, S. The Great Mental Models, Latticework Publishing Inc., 2018.
Bhalwankar, R and Treur, J. “In control of your instructor: Modeling learner-controlled mental model learning.” In _Mental Models and Their Dynamics, Adaptation, and Contro_l, 209-253: Springer, 2022.
Chartrand, TL, et al. “Beyond the perception-behavior link: The ubiquitous utility and motivational moderators of nonconscious mimicry.” In The New Unconscious, edited by Ran R. Hassin, James S. Uleman and John A. Bargh, 334-361: Oxford University Press, 2005.
Hickok, G. The Myth of Mirror Neurons: The Real Neuroscience of Communication and Cognition: WW Norton & Company, 2014.
Churchland, PS. Braintrust: What Neuroscience Tells Us About Morality: Princeton University Press, 2011. See in particular Chapters 5 and 5. As Churchland notes: “A neuron, though computationally complex, is just a neuron. It is not an intelligent homunculus. If a neural network represents something complex, such as an intention [to insult], it must have the right input and be in the right place in the neural circuitry to do that.” Chapter 6, page 142.
Guo, PJ, et al. “How video production affects student engagement: an empirical study of MOOC videos.” In Proceedings of the First ACM Conference on Learning@Scale Conference, 41-50. Atlanta, GA USA, 2014.
Jones, NA, et al. “Mental models: an interdisciplinary synthesis of theory and methods.” Ecology and Society 16, no. 1 (2011). [This is a major explanatory paper.]
Mayer, RE. “Evidence-based principles for how to design effective instructional videos.” Journal of Applied Research in Memory and Cognition 10, no. 2 (2021): 229-240. See in particular his discussion of voice (use appealing human voice) and embodiment (display gesturing instructor),
Mayer, R and Fiorella, L. The Cambridge Handbook of Multimedia Learning. 3rd ed: Cambridge University Press, 2021.
Noah, T, et al. “When both the original study and Its failed replication are correct: Feeling observed eliminates the facial-feedback effect.” Journal of Personality and Social Psychology 114, no. 5 (2018): 657.
Paley, Christopher, “Smiling does make you happier – under carefully controlled conditions: The idea that smiling changes the way we perceive things seemed like another casualty of social psychology’s replication crisis – but something more interesting was going on,” The Guardian, 2018. This easy-to-read popular article does a good job summarizing the ebbs and flows of research in this area.
Pontis, Shiela, “Making sense of mental models in information design,” blog post, July 25, 2021. This post gives a nice general overview of how information designers think about mental models and schemas, and just how differently people can view these terms, depending on their background and training.
Prince, M, et al. “Active Student Engagement in Online STEM Classes: Approaches and Recommendations.” Advances in Engineering Education 8, (2020).
Rau, MA and Herder, T. “Under which conditions are physical versus virtual representations effective? Contrasting conceptual and embodied mechanisms of learning.” Journal of Educational Psychology, (2021).
Radvansky, GA and Zacks, JM. “Event perception.” Wiley Interdiscip Rev Cogn Sci 2, no. 6 (2011): 608-620. This interesting paper describes how mental models encompass both “system” and “event” models. In our MOOC, we often describe the “event model” aspect of mental models, in line with the descriptions in this paper. But do keep in mind that other neuroscientists can and do explain mental models differently than what Radvansky and Zacks have explained in their paper.
Treur, J. “Mental models in the brain: On context-dependent neural correlates of mental models.” Cognitive Systems Research 69, (2021): 83-90.
Treur, J. “How do mental models actually exist in the brain: On context-dependent neural correlates of mental models.” In Mental Models and Their Dynamics, Adaptation, and Control, 409-426: Springer, 2022.
Varga, N, et al. “Schema, inference, and memory.” In Oxford Handbook of Human Memory, edited by Michael J. Kahana and Anthony D. Wagner: Oxford University Press, 2022.
van Ments, L and Treur, J. “Reflections on dynamics, adaptation and control: A cognitive architecture for mental models.” Cognitive Systems Research 70, (2021): 1-9.
Wade, L. “Experimental evidence for expectation-driven linguistic convergence.” Language, (2022).
Zacks, Jeffrey. Flicker: Your Brain on Movies. Oxford University Press. 2014. (See especially Chapter 1.)
Zajonc, Robert. “Attitudinal effects of mere exposure.” Journal of Personality and Social Psychology 9 (1968): 1-27.
Zwaan RA. “Effect of genre expectations on text comprehension.” J Exp Psychol Learn Mem Cogn 1994, 20:920–933. (As noted in Radvansky and Zacks, 2011.)

Gesture

Dargue, N, et al. “When our hands help us understand: A meta-analysis into the effects of gesture on comprehension.” Psychol Bull 145, no. 8 (2019): 765-784.
Davis, RO, et al. “Does a pedagogical agent’s gesture frequency assist advanced foreign language users with learning declarative knowledge?” International Journal of Educational Technology in Higher Education 18, no. 1 (2021).
Davis, RO, et al. “The effects of virtual human gesture frequency and reduced video speed on satisfaction and learning outcomes.” Educational Technology Research and Development 69, no. 5 (2021): 2331-2352.
Halvorson, KM, et al. “The role of motor context in the beneficial effects of hand gesture on memory.” Atten Percept Psychophys 81, no. 7 (2019): 2354-2364.
Kendon, A. Gesture: Visible Action as Utterance: Cambridge University Press, 2004. (This book is a classic that helped launch the field of gesture studies.)
Kita, S, et al. “How do gestures influence thinking and speaking? The gesture-for-conceptualization hypothesis.” Psychological Review 124, no. 3 (2017): 245.
Pan, Y, et al. “Instructor-learner body coupling reflects instruction and learning.” NPJ Sci Learn 7, no. 1 (2022): 15.
Ping, R, et al. “Teaching Stereoisomers Through Gesture, Action, and Mental Imagery.” Chemistry Education Research and Practice, (2022). This intriguing paper also rightly notes that “gesture is not necessarily the superior instructional choice for all concepts and training paradigms.”
Schneider, S., et al. (2022). “The impact of video lecturers’ nonverbal communication on learning–An experiment on gestures and facial expressions of pedagogical agents.” Computers & Education 176: 104350.
Turella, L and Lingnau, A. “Neural correlates of grasping.” Frontiers in Human Neuroscience 8, (2014).

Video 6: Diving Deeper into Mental Models

Key Concepts

Mental models are like a flock of neural birds continually rearranging themselves in the air as you attempt to grasp a concept or understand a situation.
If you develop a mental model that is important enough to you, and you gain enough experience with it, your mental model can gradually be integrated into your neural schemas.

References: How birds (not to mention neurons) “flock” together

Chen, D, et al. “Inferring causal relationship in coordinated flight of pigeon flocks.” Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 11 (2019): 113118.
Chung, S and Abbott, L. “Neural population geometry: An approach for understanding biological and artificial neural networks.” Current Opinion in Neurobiology 70, (2021): 137-144.
Cordova, NI, et al. “Focusing on what matters: Modulation of the human hippocampus by relational attention.” Hippocampus, (2019).
Franklin, NT, et al. “Structured event memory: A neuro-symbolic model of event cognition.” Psychological Review 127, no. 3 (2020): 327.
Friederici, P. “How a flock of birds can fly and move together.” Audubon Magazine (2009).
Gilboa, A and Marlatte, H. “Neurobiology of schemas and schema-mediated memory.” Trends Cogn Sci 21, no. 8 (2017): 618-631.
Kurby, CA and Zacks, JM. “Priming of movie content is modulated by event boundaries.” Journal of Experimental Psychology: Learning, Memory, and Cognition, (2021).
Musall, S, et al. “Single-trial neural dynamics are dominated by richly varied movements.” Nature Neuroscience 22, no. 10 (2019): 1677-1686.
Nieh, EH, et al. “Geometry of abstract learned knowledge in the hippocampus.” Nature 595, no. 7865 (2021): 80-84. This especially-intriguing paper found that the hippocampus seems to perform “a general computation—the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.” In other words, the hippocampus can be thought of as where the neural “flock” flies around as it is attempting to grasp an event model.
Pao, GM, et al. “Experimentally testable whole brain manifolds that recapitulate behavior.” arXiv preprint arXiv:2106.10627, (2021).
Sarma, AA, et al. “Internal feedback in biological control: Architectures and examples.” arXiv preprint arXiv:2110.05029, (2021).
Sekeres, MJ, et al. “The hippocampus and related neocortical structures in memory transformation.” Neurosci Lett 680, (2018): 39-53. The nomenclature of what we call “event models” can vary substantially, depending on what theories are being followed and what varying factors are involved. As Sekeres et all note with respect to the concept of episodic memory: “Episodic memories are multifaceted and malleable, capable of being transformed with time and experience at both the neural level and psychological level. At the neural level, episodic memories are transformed from being dependent on the hippocampus to becoming represented in neocortical structures, such as the medial prefrontal cortex (mPFC), and back again, while at the psychological level, detailed, perceptually rich memories, are transformed to ones retaining only the gist of an experience or a schema related to it. Trace Transformation Theory (TTT) initially proposed that neural and psychological transformations are linked and proceed in tandem… At the heart of the updated TTT lies the long axis of the hippocampus whose functional differentiation and connectivity to neocortex make it a hub for memory formation and transformation. The posterior hippocampus, connected to perceptual and spatial representational systems in posterior neocortex, supports fine, perceptually rich, local details of memories; the anterior hippocampus, connected to conceptual systems in anterior neocortex, supports coarse, global representations that constitute the gist of a memory. Notable among the anterior neocortical structures is the medial prefrontal cortex (mPFC) which supports representation of schemas that code for common aspects of memories across different episodes. Linking the aHPC with mPFC is the entorhinal cortex (EC) which conveys information needed for the interaction/translation between gist and schemas. Thus, the long axis of the hippocampus, mPFC and EC provide the representational gradient, from fine to coarse and from perceptual to conceptual, that can implement processes implicated in memory transformation. Each of these representations of an episodic memory can co-exist and be in dynamic flux as they interact with one another throughout the memory’s lifetime, going from detailed to schematic and possibly back again, all mediated by corresponding changes in neural representation.”
Steinmetz, NA, et al. “Distributed coding of choice, action and engagement across the mouse brain.” Nature 576, no. 7786 (2019): 266-273.
Sugihara, G, et al. “Detecting causality in complex ecosystems.” Science 338, no. 6106 (2012): 496-500.
Puiu, T. “How do birds flock together? Birds of a feather flock together… but how do they decide where to go and who to follow?” ZME Science (2019).
Vyas, S, et al. “Computation through neural population dynamics.” Annual Review of Neuroscience 43, (2020): 249.

Video 7: Catching Continuity Errors at the Movies—How Mental Models Arise

Key Concepts

Mental models involve what is being seen, heard, or otherwise experienced. But they also make predictions even as they draw on memories (schemas) of previous experiences.
Virtually everything we do as teachers is done to help students build mental models and schemas in STUDENT’S brains that are similar to the mental models and schemas in OUR brains.

References

See references for Week 2, videos 4 and 5. For the inspiration behind this video, we are particularly indebted to Dr. Jeffrey Zacks’ discussions in his book Flicker: Your Brain on Movies. Oxford University Press. 2014.

Video 8: Predicting Effective Online Strategies—Insight from Mental Models & More

Key Concepts

In general, it can be helpful to ask students to make predictions in order to activate prior knowledge within their schemas and help them to begin the process of making a mental model.
Theoretical techniques that can work well in traditional classrooms don’t necessarily transfer to the online world.

References

Brod, G. “Predicting as a learning strategy.” Psychonomic Bulletin & Review, (2021): 1-9.
Lang, JM. Small Teaching: Everyday Lessons from the Science of Learning: John Wiley & Sons, 2021.
Seabrooke, T, et al. “The benefits of impossible tests: Assessing the role of error-correction in the pretesting effect.” Memory & Cognition 50, no. 2 (2022): 296-311.

Video 9: How Long Should Online Videos Be? More Insight from Mental Models

Key Concepts:

Although shorter videos are in general better, the maximum length of good online videos is thought to be in the range of 12-20 minutes, not 6 minutes.
Inserting indicators that help frame key concepts can improve memory for that key concept.

References

Guo, PJ, et al. “How video production affects student engagement: an empirical study of MOOC videos.” In Proceedings of the First ACM Conference on Learning@Scale Conference, 41-50. Atlanta, GA USA, 2014.
Franklin, NT, et al. “Structured event memory: A neuro-symbolic model of event cognition.” Psychological Review 127, no. 3 (2020): 327. If you’re interested in more detail about what an event might actually consist of, in this paper, Franklin and his colleagues, including the Zelig-like Dr. Zacks, note: “Humans spontaneously organize a continuous experience into discrete events and use the learned structure of these events to generalize and organize memory. We introduce the Structured Event Memory (SEM) model of event cognition, which accounts for human abilities in event segmentation, memory, and generalization. SEM is derived from a probabilistic generative model of event dynamics defined over structured symbolic scenes. By embedding symbolic scene representations in a vector space and parametrizing the scene dynamics in this continuous space, SEM combines the advantages of structured and neural network approaches to high-level cognition. Using probabilistic reasoning over this generative model, SEM can infer event boundaries, learn event schemata, and use event knowledge to reconstruct past experience. We show that SEM can scale up to high dimensional input spaces, producing human-like event segmentation for naturalistic video data, and accounts for a wide array of memory phenomena.”
Kay, J. S., Lambe, C., Nolan, T. J., Grello, T. M., & Breitzman, A. (2019, June). “Apples to apples: Differences in viewer retention when longer content is chopped into smaller bites.” In Proceedings of the Sixth (2019) ACM Conference on Learning@ Scale (pp. 1-7).
Kurby, CA and Zacks, JM. “Priming of movie content is modulated by event boundaries.” Journal of Experimental Psychology: Learning, Memory, and Cognition, (2021).
Lagerstrom, L, et al. “The myth of the six-minute rule: Student engagement with online videos.” In 122nd ASEE Annual Conference, pp. 14-17. Seattle, WA, 2015.
Mayer, RE. “Evidence-based principles for how to design effective instructional videos.” Journal of Applied Research in Memory and Cognition 10, no. 2 (2021): 229-240.
Sargent, JQ, et al. “Event segmentation ability uniquely predicts event memory.” Cognition 129, no. 2 (2013): 241-255.
Slemmons, K., Anyanwu, K., Hames, J., Grabski, D., Mlsna, J., Simkins, E., & Cook, P. (2018). “The impact of video length on learning in a middle-level flipped science setting: Implications for diversity inclusion.” Journal of Science Education and Technology, 27(5), 469-479.
Sweller, J, et al. Cognitive Load Theory. New York: Springer-Verlag, 2011.
Sweller, J, et al. “Cognitive architecture and instructional design: 20 years later.” Educational Psychology Review 31, no. 2 (2019): 261-292.

week 3

week 3 Part 1

NOTE: All these references and readings are optional

Chapters 1 and 9 of Uncommon Sense Teaching are especially helpful in providing helpful information related to this material.

Week 3, Lesson 1: Retrieval Practice, Mental Models, and Schemas

Video 1: Today’s Online Learners Typically Have No Time to Waste

Key Concepts

Today’s online students often expect and benefit from tightly planned teaching that focuses on quickly conveying the key ideas.
We can help our students by teaching them about the value of retrieval practice and expecting them to use retrieval practice in their private study sessions between classes.
Retrieval practice is good, not just for relatively simple facts, but to help students gain a deeper conceptual understanding of the materials.

References

Agarwal, P. K. and P. Bain. Powerful Teaching: Unleash the Science of Learning: Jossey-Bass, 2019. This book is a wonderful, highly readable resource for incorporating retrieval practice into your teaching.
Agarwal, P. K., J. R. Finley, N. S. Rose and H. L. Roediger, 3rd. “Benefits from retrieval practice are greater for students with lower working memory capacity.” Memory 25, no. 6 (2017): 764-771.
Antony, JW, et al. “Retrieval as a fast route to memory consolidation.” Trends Cogn Sci 21, no. 8 (2017): 573-576.
Bjork, Robert A. “Being suspicious of the sense of ease and undeterred by the sense of difficulty: Looking back at Schmidt and Bjork (1992).” Perspectives on Psychological Science 13, no. 2 (2018): 146-148.
Bjork, EL and Bjork, RA. “Chapter 5: Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning.” Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society, 2011.
Carvalho, PF, et al. “Self-regulated spacing in a massive open online course is related to better learning.” Npj Science of Learning 5, no. 2 (2020). This important study observes: “We found that, overall, distributing study across multiple sessions—increasing spacing—was related to increased performance on end-of-unit quizzes… Spacing benefits … were largest for the lower-ability students and for those students who were less likely to complete activities. These results suggest that spaced study might work as a buffer, improving performance for low ability students and those who do not engage in active practices.”
Charo, R, et al. “Self-regulation of learning and MOOC retention.” Computers in Human Behavior, (2020): 106423.
Ericsson, KA, et al. The Cambridge Handbook of Expertise and Expert Performance. 2nd ed.: Cambridge University Press, 2018.
Lyle, Keith B, Campbell R Bego, Robin F Hopkins, Jeffrey L Hieb and Patricia AS Ralston. “How the amount and spacing of retrieval practice affect the short-and long-term retention of mathematics knowledge.” Educational Psychology Review, (2019): 1-19.
Karpicke, J. D. and J. R. Blunt. “Retrieval practice produces more learning than elaborative studying with concept mapping.” Science 331, no. 6018 (2011): 772-775.
Karpicke, Jeffrey D and Phillip J Grimaldi. “Retrieval-based learning: A perspective for enhancing meaningful learning.” Educational Psychology Review 24, no. 3 (2012): 401-418.
Koriat, Asher and Robert A Bjork. “Illusions of competence in monitoring one’s knowledge during study.” Journal of Experimental Psychology: Learning, Memory, and Cognition 31, no. 2 (2005): 187–194.
Nielsen, M. “Using spaced repetition systems to see through a piece of mathematics.” Cognitive Medium (2019).
O’Day, Garrett M. and Jeffrey D. Karpicke. “Comparing and combining retrieval practice and concept mapping.” Journal of Educational Psychology Advance online publication. https://doi.org/10.1037/edu0000486, (2021), 113 (5), 986–997.
Oxford Handbook of Human Memory, Volume 1 & Volume 2. (2022; partially complete and available online).
Pan, SC and Bjork, RA. “Chapter 11.3 Acquiring an accurate mental model of human learning: Towards an owner’s manual.” In Oxford Handbook of Memory, Vol. II: Applications, in press.
Rawson, Katherine A and John Dunlosky. “Optimizing schedules of retrieval practice for durable and efficient learning: How much is enough?” Journal of Experimental Psychology: General 140, no. 3 (2011): 283.
Roediger, Henry L and Andrew C Butler. “The critical role of retrieval practice in long-term retention.” Trends in Cognitive Sciences 15, no. 1 (2011): 20-27.
Smith, Amy M., Victoria A. Floerke and Ayanna K. Thomas. “Retrieval practice protects memory against acute stress.” Science 354, no. 6315 (2016).
Upadhyay, U, et al. “Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively.” NPJ Sci Learn 6, no. 1 (2021): 26.

Video 2: What on Earth Do Retrieval Practice and Spaced Repetition Have to Do with Mental Models? Or Good Online Teaching?

Key Concepts

Retrieval practice with spaced repetition creates and recreates mental models that help strengthen neural connections in schemas in long-term memory.
An EVENT is what is happening OUTSIDE the student. The MENTAL MODEL is what students construct IN their brain to model—in some sense, understand—the external event with their conscious working memory.
Online teaching can give us a great platform to encapsulate key concepts by turning them into tightly scripted events that are easier to retrieve.

References

See references for Video 3C-1 and below:
Babichev, A and Dabaghian, YA. “Topological schemas of memory spaces.” Frontiers in computational neuroscience 12, (2018): 27.
Franklin, NT, et al. “Structured event memory: A neuro-symbolic model of event cognition.” Psychological Review 127, no. 3 (2020): 327.
Ghosh, VE and Gilboa, A. “What is a memory schema? A historical perspective on current neuroscience literature.” Neuropsychologia 53, (2014): 104-114.
Gilboa, A and Marlatte, H. “Neurobiology of schemas and schema-mediated memory.” Trends Cogn Sci 21, no. 8 (2017): 618-631.
Kurby, CA and Zacks, JM. “Priming of movie content is modulated by event boundaries.” Journal of Experimental Psychology: Learning, Memory, and Cognition, (2021).

Video 3: “If I told you, I’d have to…” Skirting Around the Complexity of Working Memory and Mental Models

Key Concepts

The mental model being held in working memory is extremely complex, even for the seeming simplest of events. It can be difficult even for experienced neuroscientists to explain to non-neuroscientists. But thinking in terms of metaphors—the mental model is a flock of birds, while working memory is an octopus, can help.

References

See references for Video 3C-1 and 3C-2.

Video 4: Driving Home the Idea that Retrieval Practice Helps Solidify Both Simple and Complex Events in Schemas

Key Concepts

Even seemingly simple ideas, like a vocabulary word in a foreign language, can involve extraordinary complexities. “Simple” retrieval practice isn’t necessarily so simple!
Retrieval practice is useful even for complicated topics—as long as the topics are “graspable” by working memory.

References

See references for Video 3C-1 and 3C-2, and
Nielsen, M. “Using spaced repetition systems to see through a piece of mathematics.” Cognitive Medium (2019).

Video 5: Cognitive Load—If the Event is Too Complicated, Watch Out!

Key Concepts

If an event is too complex, it becomes difficult for working memory to grasp. It is better to break the ideas into smaller “flocks” of thoughts or concepts. This is called “scaffolding” or “chunking.”
Declarative information passes through the hippocampus. Procedural information passes through the basal ganglia. The basic process of retrieval practice helps with both declarative and procedural learning.
Once information is in long-term memory, it can meld together—consolidate—into larger, cohesive chunks of information that are easy to draw into mind even though they involve complicated ideas.

References

See references for Video 3C-1 and 3C-2.
To get started on the copious literature related to the importance of autonomy in learning and related issues, see the discussions on autonomy (which extend beyond motor learning) in Wulf, G and Lewthwaite, R. “Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.” Psychon Bull Rev 23, no. 5 (2016): 1382-1414.

Week 3 part 2

Week 3, Lesson 2: Using & Encouraging Retrieval Practice Apps in Your Courses

Video 6: How to Use Retrieval Practice Apps to Encourage Collaboration

References

See references for Video 3C-1

Video 7: How to Use a Specific Retrieval Practice App (iDoRecall) in Your Coursera Courses

References

See references for Video 3C-1.

Video 8: How to Solicit Live, Monitored Retrieval Practice from Every Student in a Class Simultaneously (PearDeck™)

PearDeck.com
Edwards, Luke, “What is Pear Deck and How Does It Work?” 2021, Tech & Learning.com.

Video 9: Working Memory, Non-Native Speakers, and Barb’s Personal Experience with Retrieval Practice

Key Concepts

A typical working memory can hold roughly four pieces of information in mind. But the size of a piece of information depends on the learner’s background expertise in the area—their underlying schema.
Be aware that non-native speakers also have the added cognitive load of trying to grasp the information being taught via what is to them a foreign language. Reduce the processing burden of foreign language speakers by enunciating clearly and avoiding slang. (We realize, however, that a little slang can sometimes make such a big difference on native speakers of English that it’s worth the tradeoff.)
Retrieval practice is particularly valuable for students with a lesser working memory capacity—it can allow them to excel even in difficult subject areas.

References

See references for Video 3C-1 and 3C-2 and:
Agarwal, PK, et al. “Benefits from retrieval practice are greater for students with lower working memory capacity.” Memory 25, no. 6 (2017): 764-771.
Sali, AW and Egner, T. “Declarative and procedural working memory updating processes are mutually facilitative.” Atten Percept Psychophys 82, no. 4 (2020): 1858-1871.

Week 4

Week 4 part 1

Week 4, Lesson 1: Focus, Suspense, and Creativity