week 1

week 1 part 1

NOTE: All these references and readings are optional.

Chapters 1 and 2 of Uncommon Sense Teaching are especially helpful in providing helpful information related to this material.

Video 1: The Essence of How We Learn

Neurons

To get a better sense of more anatomically correct neurons, see the beautiful images drawn by the pioneering father of modern neuroscience, Santiago Ramon y Cajal, .
A quick overview video of the neuron can be found at 2-Minute Neuroscience. (As one viewer comments: “One hour in a textbook, still confused. 2 minutes on YouTube and I get it. What a world we live in lol.”) The main 2-Minute Neuroscience website, with many more videos about how the brain works, is here.

Hebbian learning

Sejnowski, TJ. “The book of Hebb.” Neuron 24, no. 4 (1999): 773-776.

Growth Mindset

Our statement to the effect that “it’s not enough to have a growth mindset,” is worthy of being unpacked. Carol Dweck, the originator of the theory surrounding a growth mindset, deserves credit for putting forth a “gold-standard” study of growth mindset. She and her colleagues pre-registered their plans beforehand so intentions couldn’t be altered once the data came in, and the study was massive, involving over 12,000 students in 65 public schools. Their findings showed a .03 improvement in GPA, which Dweck argues here is significant. The effect size, however, is only a 0.08 overall (a good explanation of effect sizes is in chapter 3 of e-Learning and the Science of Instruction, by Ruth Colvin Clark and Richard E. Mayer). A meta-analysis by a different author group found the effect of growth mindset interventions to be too small to be practically meaningful. Meta-analysis co-author Brooke McNamara responds to Dweck’s criticism of the meta-analysis here. (We also have to give credit to McNamara, an assistant professor, and her chutzpah in being willing to look critically at the work of a world-renowned Stanford researcher.) This related discussion in Wired also helps put growth mindset interventions into context.

Scott Alexander Siskind, (writing as Scott Alexander), goes deep into an analysis of unusual anomalies in Carol Dweck’s published research on growth mindset in: “No Clarity around Growth Mindset,” Slate Star Codex, April 8, 2015. See also “Does mindset affect children’s ability, school achievement, or response to challenge? Three failures to replicate,” by Yue Li & Timothy C. Bates, with the following conclusions: “Praise for intelligence failed to harm post- challenge cognitive performance. Children’s mindsets had no relationship to their IQ or to their school grades. Finally believing ability to be malleable had no association with improvement of grades across the year. We conclude that the belief that basic ability is fixed is harmless, and that implicit theories of intelligence play no significant role in development of cognitive ability, response to challenge, or educational attainment.”

Week 1 Part 2

NOTE: All these references and readings are optional

Chapters 1, 2, and 3 of Uncommon Sense Teaching are especially helpful in providing helpful information related to this material.

Video 6: Inclusivity, Differentiation, and Scaffolding

Incorporating Differentiation into Your Classroom

Heacox, Diane. Making Differentiation a Habit: How to Ensure Success in Academically Diverse Classrooms: Free Spirit Publishing, 2017.
Tomlinson, Carol Ann. How to Differentiate Instruction in Academically Diverse Classrooms. 3rd ed.: ASCD, 2017. (This includes information on “teaching up.”)

Working memory and creativity

DeCaro, M. S., C. A. Van Stockum and M. B. Wieth. “When higher working memory capacity hinders insight.” Journal of Experimental Psychology: Learning, Memory, and Cognition 42, no. 1 (2015): 39-49.
DeCaro et al., 2015; Takeuchi, H., Y. Taki, Y. Sassa, H. Hashizume, A. Sekiguchi, A. Fukushima and R. Kawashima. “Working memory training using mental calculation impacts regional gray matter of the frontal and parietal regions.” PLoS ONE 6, no. 8 (2011): e23175.
DeCaro, Marci S. “Chapter 4: “When does higher working memory capacity help or hinder insight problem solving?” In Insight: On the Origins of New Ideas, edited by Frédéric Vallée-Tourangeau, 79-104: Routledge, 2018.
Wieth and Zacks, 2011. Wieth, Mareike B and Rose T Zacks. “Time of day effects on problem solving: When the non-optimal is optimal.” Thinking & Reasoning 17, no. 4 (2011): 387-401.
Szumski, Grzegorz, Joanna Smogorzewska and Maciej Karwowski. “Academic achievement of students without special educational needs in inclusive classrooms: A meta-analysis.” Educational Research Review 21, (2017): 33-54.

Video 7: Practical Insights Related to Working Memory

Highly recommended!

Agarwal, P. K. and P. Bain. Powerful Teaching: Unleash the Science of Learning: Jossey-Bass, 2019.

For instructions on how to use “handouts with gaps” and the value of worked examples:

Chang, Wan-Chen and Yu-Min Ku. “The effects of note-taking skills instruction on elementary students’ reading.” The Journal of Educational Research 108, no. 4 (2015): 278-291.
Chen, Ouhao, Slava Kalyuga and John Sweller. “The worked example effect, the generation effect, and element interactivity.” Journal of Educational Psychology 107, no. 3 (2015): 689–704.
Felder, Richard M and Rebecca Brent. Teaching and Learning STEM: A Practical Guide: John Wiley & Sons, 2016.
Gharravi, Anneh Mohammad. “Impact of instructor-provided notes on the learning and exam performance of medical students in an organ system-based medical curriculum.” Advances in Medical Education and Practice 9, (2018): 665-672. It’s common for students with lesser-capacity working memory to struggle with math:
Clark, Caron A. C., Verena E. Pritchard and Lianne J. Woodward. “Preschool executive functioning abilities predict early mathematics achievement.” Developmental Psychology 46, no. 5 (2010): 1176-1191.
Morgan, Paul L, George Farkas and Steve Maczuga. “Which instructional practices most help first-grade students with and without mathematics difficulties?” Educational Evaluation and Policy Analysis 37, no. 2 (2015): 184-205.
Raghubar, Kimberly P., Marcia A. Barnes and Steven A. Hecht. “Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches.” Learning and Individual Differences 20, no. 2 (2010): 110-122. The value of direct (scaffolded) instruction and retrieval practice for those with lesser-capacity working memory:
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.
Dehn, Milton J. Working Memory and Academic Learning: Assessment and Intervention: John Wiley & Sons, 2008.
Fuchs, Lynn S., David C. Geary, Donald L. Compton, Douglas Fuchs, Christopher Schatschneider, Carol L. Hamlett, Jacqueline DeSelms, Pamela M. Seethaler, Julie Wilson, Caitlin F. Craddock, Joan D. Bryant, Kurstin Luther and Paul Changas. “Effects of first-grade number knowledge tutoring with contrasting forms of practice.” Journal of Educational Psychology 105, no. 1 (2013): 58-77.
Klahr, David and Milena Nigam. “The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning.” Psychological Science 15, no. 10 (2004): 661-667.
Stockard, Jean, Timothy W. Wood, Cristy Coughlin and Caitlin Rasplica Khoury. “The effectiveness of direct instruction curricula: A meta-analysis of a half century of research.” Review of Educational Research 88, no. 4 (2018): 479-507.

Music and noise helps the studies of those with ADHD

Antonietti, Alessandro, Barbara Colombo and Braelyn R DeRocher. “Enhancing Self-Regulatory Skills in ADHD Through Music.” In Music Interventions for Neurodevelopmental Disorders, edited by Alessandro Antonietti, Barbara Colombo and Braelyn R DeRocher, 19-49: Springer, 2018.
Soderlund, G., S. Sikstrom and A. Smart. “Listen to the noise: Noise is beneficial for cognitive performance in ADHD.” J Child Psychol Psychiatry 48, no. 8 (2007): 840-7

Video 8: What Is Active Learning?

Active learning improves classroom performance:

Freeman, S., S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt and M. P. Wenderoth. “Active learning increases student performance in science, engineering, and mathematics.” Proceedings of the National Academy of Sciences 111, no. 23 (2014): 8410-8415.
Hora, Matthew T. “Limitations in experimental design mean that the jury is still out on lecturing.” Proceedings of the National Academy of Sciences 111, no. 30 (2014): E3024.
Freeman, Scott, Sarah L. Eddy, Hannah Jordt, Michelle K. Smith and Mary Pat Wenderoth. “Reply to Hora: Meta-analytic techniques are designed to accommodate variation in implementation.” Proceedings of the National Academy of Sciences 111, no. 30 (2014): E3025.

Finding the most effective balance between lecture versus active learning

Jaeger, B, et al. “Tipping the scales: Finding the most effective balance between lecture versus active learning across academic levels in engineering.” In American Society for Engineering Education 2008 Annual Conference & Exposition, 13.1290. 1291-1213.1290. 1218, 2008.

The value of direct instruction:

Adams, Gary L and Siegfried Engelmann. Research on Direct Instruction: 25 Years beyond DISTAR. Seattle, WA: Educational Achievement Systems, 1996.
Boxer, Adam, ed. The researchED Guide to Explicit & Direct Instruction: An Evidence-Informed Guide for Teachers: John Catt Educational, 2019.
Klahr, David and Milena Nigam. “The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning.” Psychological Science 15, no. 10 (2004): 661-667.
Przychodzin, Angela M, Nancy E Marchand-Martella, Ronald C Martella and Diane Azim. “Direct instruction mathematics programs: An overview and research summary.” Journal of Direct Instruction 4, no. 1 (2004): 53-84.
Stockard, Jean, Timothy W. Wood, Cristy Coughlin and Caitlin Rasplica Khoury. “The effectiveness of direct instruction curricula: A meta-analysis of a half century of research.” Review of Educational Research 88, no. 4 (2018): 479-507.
White, William AT. “A meta-analysis of the effects of direct instruction in special education.” Education and Treatment of Children 11, no. 4 (1988): 364-374.

“Grecian urn” projects:

Gonzalez, Jennifer. “Is Your Lesson a Grecian Urn?” In Cult of Pedagogy, 2016.

Learning involves retrieval practice:

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.
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, (2020).

Optional: Video Interview with Dr. Steiner.

Here is a terrific article in the Wall Street Journal about Dr. Steiner’s work. Sadly, it’s behind a paywall. So that’s why we went straight to Dr. Steiner himself and made an interview!

“HOW SCHOOLS ARE REWRITING THE RULES ON CLASS TIME FOR STUDENTS—AND EVEN DITCHING GRADE LEVELS,” by Yoree Koh, Wall Street Journal, AUG. 9, 2021.

Week 2

Week 2 part 1

NOTE: All these references and readings are optional

Chapter 3 of Uncommon Sense Teaching is especially helpful in providing helpful information related to declarative learning.

Video 1: Introduction to the Declarative Learning System (Hip Hip, Hooray!)

The hippocampus and neocortex as two major learning systems, with the hippocampus as an index:

McClelland, James L, Bruce L McNaughton and Randall C O’Reilly. “Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.” Psychological Review 102, no. 3 (1995): 419-457. (This is a classic paper in the field and proposed the theory of the hippocampus as an index.)
Mao, Dun, Adam R Neumann, Jianjun Sun, Vincent Bonin, Majid H Mohajerani and Bruce L McNaughton. “Hippocampus-dependent emergence of spatial sequence coding in retrosplenial cortex.” Proceedings of the National Academy of Sciences 115, no. 31 (2018): 8015-8018.

Video 2: I Do Declare, There’s a Hip Way to Get Info into Long-Term Memory!

General introduction to declarative learning, “Hip” and “Neo”

Oakley, B, Rogowsky, B, Sejnowski, T. Uncommon Sense Teaching: Penguin Random House, 2021. Chapter 3.

The hippocampus and neocortex as two major learning systems

McClelland, James L, et al. “Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.” Psychological Review 102, no. 3 (1995): 419-457. (This is a classic paper in the field and proposed the theory of the hippocampus as an index.)

The scattered (“scatter-brained!) nature of long-term memory

Josselyn, S. A. and S. Tonegawa. “Memory engrams: Recalling the past and imagining the future.” Science 367, no. 6473 (2020): 1-14, which notes: “Although initial engram studies focused on single brain regions, an emerging concept is that a given memory is supported by an engram complex, composed of functionally connected engram cell ensembles dispersed across multiple brain regions, with each ensemble supporting a component of the overall memory.”

The hippocampus as an index:

Mao, Dun, Adam R Neumann, Jianjun Sun, Vincent Bonin, Majid H Mohajerani and Bruce L McNaughton. “Hippocampus-dependent emergence of spatial sequence coding in retrosplenial cortex.” Proceedings of the National Academy of Sciences 115, no. 31 (2018): 8015-8018.

The hippocampus turns to repeat new learning to the neocortex:

Craig, M., et al. “Rest on it: Awake quiescence facilitates insight.” Cortex 109, (2018): 205-214.
Runyan, Jason D., et al. “Coordinating what we’ve learned about memory consolidation: Revisiting a unified theory.” Neuroscience & Biobehavioral Reviews 100, (2019): 77-84.
Wamsley, Erin J. “Memory consolidation during waking rest.” Trends in Cognitive Sciences 23, no. 3 (2019): 171-173.

Video 3: Concussion Dealt Me a Knockout Blow—The Value of Consolidation

Fast Forword

If you would like to learn more about Fast ForWord, go to https://www.scilearn.com/ It should be noted that Fast ForWord had the largest improvement index of interventions evaluated in the English language development category, describing the learning done by English language learners.

Consolidation:

Although not a rigorous source, an up-to-date, fairly readable description of memory consolidation processes can be found on Wikipedia.
Antony, James W and Ken A Paller. “Hippocampal contributions to declarative memory consolidation during sleep.” In The Hippocampus from Cells to Systems, edited by D. E. Hannula and M. C. Duff, 245-280: Springer, 2017.
De Vivo, Luisa, et al. “Ultrastructural evidence for synaptic scaling across the wake/sleep cycle.” Science 355, no. 6324 (2017): 507-510.
Dudai, Y., et al. “The consolidation and transformation of memory.” Neuron 88, no. 1 (2015): 20-32.
Himmer, L., et al. “Rehearsal initiates systems memory consolidation, sleep makes it last.” Science Advances 5, no. 4 (2019): 1-9.
Li, Wei, et al. “REM sleep selectively prunes and maintains new synapses in development and learning.” Nature Neuroscience 20, no. 3 (2017): 427-437.
Westermann, J., et al. “System consolidation during sleep - a common principle underlying psychological and immunological memory formation.” Trends Neurosci 38, no. 10 (2015): 585-597.
Yang, Guang, et al. “Sleep promotes branch-specific formation of dendritic spines after learning.” Science 344, no. 6188 (2014): 1173-1178.

But There Is Some Evidence That Hip Sticks Around…

Duff, M. C., et al. “Semantic memory and the hippocampus: Revisiting, reaffirming, and extending the reach of their critical relationship.” Front Hum Neurosci 13, (2019): 471.

Retrieval as a fast route to memory consolidation:

Antony, J. W., et al. “Retrieval as a fast route to memory consolidation.” Trends Cogn Sci 21, no. 8 (2017): 573-576.

Traumatic Brain Injury (TBI)

Azouvi, P, et al. “Neuropsychology of traumatic brain injury: An expert overview.” Revue Neurologique 173, no. 7-8 (2017): 461-472.
Cantarero, G., et al. “Repeated concussions impair behavioral and neurophysiological changes in the motor learning system.” Neurorehabil Neural Repair 34, no. 9 (2020): 804-813.
Lindsey, A. Enhancing Cognitive and Linguistic Processes of Individuals with a History of TBI. Doctoral Dissertations, (2019).
Vanderploeg, Rodney D, et al. “Verbal learning and memory deficits in traumatic brain injury: Encoding, consolidation, and retrieval.” Journal of Clinical and Experimental Neuropsychology 23, no. 2 (2001): 185-195.

Video 4: The Value of Metaphor General information on metaphors and teaching with metaphors

Brown, S and Salter, S. “Analogies in science and science teaching.” Adv Physiol Educ 34, no. 4 (2010): 167-169.
Duit, R. “On the role of analogies and metaphors in learning science.” Sci Educ 75, no. 6 (1991): 649-672.
Hendricks, R. “Do metaphors make learning a piece of cake?” Learning & the Brain Blog (2015).
Lakoff, G and Johnson, M. Metaphors We Live By. Chicago, IL USA: University of Chicago Press, 2008.
Rodriguez, X and Arroyo-Santos, A. “The function of scientific metaphors: An example of the creative power of metaphors in biological theories.” In The Paths of Creation. Creativity in Science and Art, 9, 81-96: Peter Lang Publishing Group Bern, 2011. Mathematical equations are metaphors
Derman, Emanuel. Models. Behaving. Badly. New York, NY USA: Free Press, 2011. As leading mathematical modeler Emanuel Derman notes: “Theories describe and deal with the world on its own terms and must stand on their own two feet. Models stand on someone else’s feet. They are metaphors that compare the object of their attention to something else that it resembles. Resemblance is always partial, and so models necessarily simplify things and reduce the dimensions of the world… . In a nutshell, theories tell you what something is; models tell you merely what something is like” (p. 6). Neural Reuse Theory
Anderson, Michael L. After Phrenology: Neural Reuse and the Interactive Brain. Cambridge, MA: MIT Press, 2014.

Pathological Altruism

Oakley, B, et al., eds. Pathological Altruism: Oxford University Press, 2012.
Oakley, BA. “Concepts and implications of altruism bias and pathological altruism.” Proceedings of the National Academy of Sciences 110, Supplement 2 (2013): 10408-10415.

Week 2 part 2

NOTE: All these references and readings are optional

Chapter 6 of Uncommon Sense Teaching is especially helpful in providing helpful information related to procedural learning. Exercise is covered in Chapter 3, and attention introduced in Chapter 2.

Video 5: Introduction to Procedural Learning

Excellent, easy-to-read general introductions to the habit-based procedural system:

Wood, W. Good Habits, Bad Habits: The Science of Making Positive Changes that Stick: Farrar, Straus and Giroux, 2019.
Duhigg, C. The Power of Habit. Random House, 2012.

The Procedural System (in contrast with the declarative system)

Ashby, FG and Valentin, VV. “Multiple systems of perceptual category learning: Theory and cognitive tests.” In the Handbook of Categorization in Cognitive Science, edited by Henri Cohen and Claire Lefebvre, 157-188: Elsevier Science, 2017.
Evans, TM and Ullman, MT. “An extension of the procedural deficit hypothesis from developmental language disorders to mathematical disability.” Frontiers in Psychology 7, Article 1318 (2016): 1-9.
Sali, AW and Egner, T. “Declarative and procedural working memory updating processes are mutually facilitative.” Atten Percept Psychophys 82, no. 4 (2020): 1858-1871.
Turner, BO, et al. “Hierarchical control of procedural and declarative category-learning systems.” NeuroImage 150, (2017): 150-161.
Ullman, MT. “The declarative/procedural model: A neurobiologically motivated theory of first and second language.” In Theories in Second Language Acquisition: An Introduction, edited by Bill VanPatten, Gregory D. Keating and Stefanie Wulff, 128-161: Routledge, 2020.
Ullman, MT, et al. “The neurocognition of developmental disorders of language.” Annu Rev Psychol 71, (2020): 389-417.
Ullman, MT and Lovelett, JT. “Implications of the declarative/procedural model for improving second language learning: The role of memory enhancement techniques.” Second Language Research 34, no. 1 (2016): 39-65.
Zwart, FS, et al. “Procedural learning across the lifespan: A systematic review with implications for atypical development.” J Neuropsychol 13, no. 2 (2019): 149-182.

Bidirectional Relations Between Procedural and Conceptual Knowledge of Mathematics

Rittle-Johnson, B, et al. “Not a one-way Street: Bidirectional relations between procedural and conceptual knowledge of mathematics.” Educational Psychology Review 27, no. 4 (2015): 587-597.

Video 6: Drill to SKILL

With appreciation to Kazuyuki Takayanagi and Manabu Watanabe for the Flash Anzan video. For more information, please contact:

Soroban Kyuoshitsu USA

1-1-8 Maeji Urawa-ku Saitama City Saitama Prefecture, 330-0053 Japan

TEL: 048-887-1788

FAX: 048-886-8301 Bidirectional Relations Between Procedural and Conceptual Knowledge of Mathematics

Rittle-Johnson, B, et al. “Not a one-way Street: Bidirectional relations between procedural and conceptual knowledge of mathematics.” Educational Psychology Review 27, no. 4 (2015): 587-597.

Additional Research Related to Procedural Learning

Evans, TM and Ullman, MT. “An extension of the procedural deficit hypothesis from developmental language disorders to mathematical disability.” Frontiers in Psychology 7, Article 1318 (2016): 1-9.
Sali, AW and Egner, T. “Declarative and procedural working memory updating processes are mutually facilitative.” Atten Percept Psychophys 82, no. 4 (2020): 1858-1871.
Ullman, MT. “The declarative/procedural model: A neurobiologically motivated theory of first and second language.” In Theories in Second Language Acquisition: An Introduction, edited by Bill VanPatten, Gregory D. Keating and Stefanie Wulff, 128-161: Routledge, 2020.
Ullman, MT, et al. “The neurocognition of developmental disorders of language.” Annu Rev Psychol 71, (2020): 389-417.
Zwart, FS, et al. “Procedural learning across the lifespan: A systematic review with implications for atypical development.” J Neuropsychol 13, no. 2 (2019): 149-182.

Interleaving

Brunmair, M and Richter, T. “Similarity matters: A meta-analysis of interleaved learning and its moderators.” Psychol Bull 145, no. 11 (2019): 1029-1052.
Carvalho, PF and Goldstone, RL. “When does interleaving practice improve learning?” In The Cambridge Handbook of Cognition and Education, edited by J. Dunlosky and K. A. Rawson, 2019.
Pan, SC, et al. “Does interleaved practice enhance foreign language learning? The effects of training schedule on Spanish verb conjugation skills.” Journal of Educational Psychology 111, no. 7 (2019): 1172-1188.
Ste-Marie, DM, et al. “High levels of contextual interference enhance handwriting skill acquisition.” Journal of Motor Behavior 36, no. 1 (2004): 115-126.
Soderstrom, NC and Bjork, RA. “Learning versus performance: An integrative review.” Perspect Psychol Sci 10, no. 2 (2015): 176-199.
Rohrer, D, et al. “Interleaved practice improves mathematics learning.” Journal of Educational Psychology 107, no. 3 (2015): 900.

Video 7: The Power of Exercise

Nice general overview of exercise

Reynolds, G. “Even a 20-second exercise ‘snack’ can improve fitness.” New York Times Jan 23 (2019).

Research publications on the value of exercise

Basso, JC and Suzuki, WA. “The effects of acute exercise on mood, cognition, neurophysiology, and neurochemical pathways: A review.” Brain Plasticity 2, no. 2 (2017): 127-152.
Chang, YK, et al. “The effects of acute exercise on cognitive performance: A meta-analysis.” Brain Res 1453, (2012): 87-101.
Erickson, KI, et al. “Physical activity, cognition, and brain outcomes: a review of the 2018 physical activity guidelines.” Medicine & Science in Sports & Exercise 51, no. 6 (2019): 1242-1251.
Jenkins, EM, et al. “Do stair climbing exercise ‘snacks’ improve cardiorespiratory fitness?” Appl Physiol Nutr Metab 44, no. 6 (2019): 681-684.
Lu, B, et al. “BDNF-based synaptic repair as a disease-modifying strategy for neurodegenerative diseases.” Nature Reviews: Neuroscience 14, no. 6 (2013): 401-416.
Snyder, JS and Drew, MR. “Functional neurogenesis over the years.” Behavioral Brain Research 382, 112470 (2020).
Szuhany, KL, et al. “A meta-analytic review of the effects of exercise on brain-derived neurotrophic factor.” Journal of Psychiatric Research 60, (2015): 56-64.
Wunsch, K, et al. “Acute psychosocial stress and working memory performance: the potential of physical activity to modulate cognitive functions in children.” BMC Pediatr 19, 271 (2019): 1-15.

Video 8: The Vital Importance of Focus

Multitasking

Adler, RF and Benbunan-Fich, R. “Self-interruptions in discretionary multitasking.” Computers in Human Behavior 29, no. 4 (2013): 1441-1449.
Benbunan-Fich, R, et al. “Measuring multitasking behavior with activity-based metrics.” ACM Transactions on Computer-Human Interaction 18, no. 2 (2011): 1-22.
Borst, JP, et al. “The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking.” PLoS ONE 5, no. 9 (2010): e12966.
Courage, ML, et al. “Growing up multitasking: The costs and benefits for cognitive development.” Developmental Review 35, (2015): 5-41.
Donohue, SE, et al. “Cognitive pitfall! Videogame players are not immune to dual-task costs.” Attention, Perception, & Psychophysics 74, no. 5 (2012): 803-809.
Dewan, P. “Reading in the age of continuous partial attention: Retail-inspired ideas for academic libraries.” Library Publications. 47, (2019).
Fox, EL and Houpt, JW. “Quantifying the Effects of Multi-Tasking on Processing Efficiency.” Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1717-1721.
Frein, ST, et al. “When it comes to Facebook there may be more to bad memory than just multitasking.” Computers in Human Behavior 29, no. 6 (2013): 2179-2182.
Kraushaar, J and Novak, D. “Examining the affects of student multitasking with laptops during the lecture.” Journal of Information Systems Education 21, no. 2 (2010): 241-251.
Loh, KK and Kanai, R. “How Has the Internet Reshaped Human Cognition?” Neuroscientist 22, no. 5 (2016): 506-520.
Mark, G, et al. “Neurotics can’t focus: An in situ study of online multitasking in the workplace.” In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 1739-1744: ACM, 2016.
Mark, G, et al. “Focused, aroused, but so distractible: Temporal perspectives on multitasking and communications.” In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 903-916: ACM, 2015.
Mokhtari, K, et al. “Connected yet distracted: Multitasking among college students.” Journal of College Reading and Learning 45, no. 2 (2015): 164-180.
Paul, AM. “You’ll never learn! Students can’t resist multitasking, and it’s impairing their memory.” Slate May 3 (2013).
Pollard, MA and Courage, ML. “Working memory capacity predicts effective multitasking.” Computers in Human Behavior 76, (2017): 450-462.
Redick, TS, et al. “Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence.” J Exp Psychol Gen 145, no. 11 (2016): 1473-1492.
Srna, S, et al. “The illusion of multitasking and its positive effect on performance.” Psychological Science 29, no. 12 (2018): 1942-1955.
van der Schuur, WA, et al. “The consequences of media multitasking for youth: A review.” Computers in Human Behavior 53, (2015): 204-215.
Wilmer, HH, et al. “Smartphones and Cognition: A Review of Research Exploring the Links between Mobile Technology Habits and Cognitive Functioning.” Frontiers in Psychology 8, (2017): 605.

Task switching reduces productivity

Rubinstein, JS, et al. “Executive control of cognitive processes in task switching.” Journal of Experimental Psychology: Human Perception and Performance 27, no. 4 (2001): 763-797.

Flash Anzan

Bellos, A. “Abacus adds up to number joy in Japan.” The Guardian Oct 25 (2012).
Bellos, A. “World’s fastest number game wows spectators and scientists.” The Guardian (2012).
Watanabe, M. “Training math athletes in Japanese jukus.” Juku (2015).

Week 3

week 3 part 1

NOTE: All these references and readings are optional

Chapters 3, 4, and 6 of Uncommon Sense Teaching are especially helpful in providing helpful information related to this material.

Video 1: Focused and Diffuse Modes

Christoff, K., Z. C. Irving, K. C. Fox, R. N. Spreng, and J. R. Andrews-Hanna. “Mind-wandering as spontaneous thought: A dynamic framework.” Nature Reviews. Neuroscience 17, no. 11 (Nov 2016): 718-31. https://dx.doi.org/10.1038/nrn.2016.113.
Di, X., and B. B. Biswal. “Modulatory interactions between the default mode network and task positive networks in resting-state.” PeerJ 2 (2014): e367. https://dx.doi.org/10.7717/peerj.367.
Kühn, Simone, Simone M. Ritter, Barbara C. N. Müller, Rick B. van Baaren, Marcel Brass, and Ap Dijksterhuis. “The importance of the default mode network in creativity—A structural MRI study.” The Journal of Creative Behavior 48, no. 2 (2014/06/01 2014): 152-63.
Pachai, Amy A, Anita Acai, Andrew B LoGiudice, and Joseph A Kim. “The mind that wanders: Challenges and potential benefits of mind wandering in education.” Scholarship of Teaching and Learning in Psychology 2, no. 2 (2016): 134.
Raichle, M. E., and Svend Davanger. “The brain’s default mode network–What does it mean to us? Marcus Raichle interviewed by Svend Davanger.” The Meditation Blog (Mar 9 2015). http://www.themeditationblog.com/the-brains-default-mode-network-what-does-it-mean-to-us/.
Raichle, Marcus E. “The brain’s default mode network.” Annu Rev Neurosci 38 (2015): 433-47.

Video 2: Procrastination and the Pomodoro Technique

Focus

Badre, D. On Task: How Our Brain Gets Things Done: Princeton University Press, 2020.

A Pain in the Brain When Thinking About Something You Don’t Like

Lyons, IM and Beilock, SL. “When math hurts: Math anxiety predicts pain network activation in anticipation of doing math.” PLoS ONE 7, no. 10 (2012): e48076.

Procrastination

Pychyl, TA. Solving the Procrastination Puzzle: A Concise Guide to Strategies for Change: TarcherPerigee, 2013.
Sirois, F and Pychyl, T. “Procrastination and the priority of short-term mood regulation: Consequences for future self.” Social and Personality Psychology Compass 7, no. 2 (2013): 115-127.
Steel, P. The Procrastination Equation. NY: Random House, 2010.
Steel, P. “The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure.” Psychol Bull 133, no. 1 (2007): 65-94.
van Eerde, W and Klingsieck, KB. “Overcoming procrastination? A meta-analysis of intervention studies.” Educational Research Review, (2018).

The Pomodoro Technique

Cirillo, F. The Pomodoro Technique: The Acclaimed Time-Management System That Has Transformed How We Work: Currency, 2018.

Multitasking

Fox, EL and Houpt, JW. “Quantifying the Effects of Multi-Tasking on Processing Efficiency.” Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1717-1721.
Worringer, B, et al. “Common and distinct neural correlates of dual-tasking and task-switching: a meta-analytic review and a neuro-cognitive processing model of human multitasking.” Brain Structure and Function 224, no. 5 (2019): 1845-1869.
Redick, TS, et al. “Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence.” J Exp Psychol Gen 145, no. 11 (2016): 1473-1492.

Video 3: Diving Deeper Into Procrastination

Pychyl, TA. Solving the Procrastination Puzzle: A Concise Guide to Strategies for Change: TarcherPerigee, 2013.
Sirois, F and Pychyl, T. “Procrastination and the priority of short-term mood regulation: Consequences for future self.” Social and Personality Psychology Compass 7, no. 2 (2013): 115-127.
Steel, P. The Procrastination Equation. NY: Random House, 2010.
Steel, P. “The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure.” Psychol Bull 133, no. 1 (2007): 65-94.
van Eerde, W and Klingsieck, KB. “Overcoming procrastination? A meta-analysis of intervention studies.” Educational Research Review, (2018).

Video 4: Don’t Just Follow Your Passions—Broaden Them!

Newport, C. “In choosing a job: Don’t ask “What are you good at?’, ask instead ‘What are you willing to get good at?’” In Study Hacks: Decoding Patterns of Success, 2013.
Newport, C. So Good They Can’t Ignore You. NY: Business Plus, 2012.
Oakley, BA. A Mind for Numbers: How to Excel at Math and Science. New York, NY: Penguin-Random House, 2014.
Oakley, B. Hair of the Dog: Tales from Aboard a Russian Trawler. Pullman, Washington: WSU Press, 1996.

Video 5: Practice—the Key to Remarkable Changes

Encouraging finger counting doesn’t seem to help with learning math:

Schild, U, et al. “A finger-based numerical training failed to improve arithmetic skills in kindergarten children beyond effects of an active non-numerical control training.” Frontiers in Psychology 11, (2020): 529-529.

Developmental differences between boys and girls

Hollier, Lauren P, Eugen Mattes, Murray T Maybery, Jeffrey A Keelan, Martha Hickey, and Andrew JO Whitehouse. “The Association between Perinatal Testosterone Concentration and Early Vocabulary Development: A Prospective Cohort Study.” Biological Psychology 92, no. 2 (2013): 212-15.
Kung, Karson TF, Wendy V Browne, Mihaela Constantinescu, Rebecca M Noorderhaven, and Melissa Hines. “Early Postnatal Testosterone Predicts Sex-Related Differences in Early Expressive Vocabulary.” _Psychoneuroendocrinolog_y 68 (2016): 111-16.
Quast, Anja, Volker Hesse, Johannes Hain, Peter Wermke, and Kathleen Wermke. “Baby Babbling at Five Months Linked to Sex Hormone Levels in Early Infancy.” Infant Behavior and Development 44 (2016): 1-10.
Schaadt, Gesa, Volker Hesse, and Angela D Friederici. “Sex Hormones in Early Infancy Seem to Predict Aspects of Later Language Development.” Brain and Language 141 (2015): 70-76.
Stoet, Gijsbert, and David C Geary. “Sex Differences in Mathematics and Reading Achievement Are Inversely Related: Within-and across-Nation Assessment of 10 Years of PISA Data.” PLoS ONE 8, no. 3 (2013): e57988.
Whitehouse, Andrew JO, Eugen Mattes, Murray T Maybery, Michael G Sawyer, Peter Jacoby, Jeffrey A Keelan, and Martha Hickey. “Sex‐Specific Associations between Umbilical Cord Blood Testosterone Levels and Language Delay in Early Childhood.” Journal of Child Psychology and Psychiatry 53, no. 7 (2012): 726-34.

Taxi drivers:

Maguire, EA, et al. “Navigation-related structural change in the hippocampi of taxi drivers.” Proceedings of the National Academy of Sciences 97, no. 8 (2000): 4398-4403.

Sheryl Sorby:

Oakley, BA. A Mind for Numbers: How to Excel at Math and Science. New York, NY: Penguin-Random House, 2014, pp. 166-167.

Kumon, Saxon, and Singapore approaches to teaching math

Agita, A. “The effect of application Kumon learning method in learning mathematics of ability troubleshooting mathematics of students.” In Journal of Physics: Conference Series, 1429, 012005: IOP Publishing, 2020.
Begum, J. “Experimental study to determine the effectiveness of Kumon Method in comparison with traditional lecture method for teaching of mathematics to Grade-5.” Foundation University, Islamabad, 2018.
Begum, J, et al. “Effectiveness of Kumon teaching method for academic achievement of children in mathematics.” Pakistan Journal of Education 35, no. 1 (2018).
Dancis, J. “Exceptional learning results from exceptionally good textbooks: Singapore yes! Finland no!” Nonpartisan Education Review 14, no. 4 (2018): 1-5.
Donovan, W and Wurman, Ze. “Axioms of excellence: Kumon and the Russian school of mathematics. White Paper No. 188.” Pioneer Institute for Public Policy Research, (2019).
Hook, W, et al. “A quality math curriculum in support of effective teaching for elementary schools.” Educational Studies in Mathematics 65, no. 2 (2007): 125-148.
Mendaje, JRC. “Performance of sophomore secondary students exposed in the Kumon Mathematics Program.” World Journal of Research and Review 6, no. 3 (2019): 262680.
Oakley, B, et al. “Improvements in statewide test results as a consequence of using a Japanese-based supplemental mathematics system, Kumon Mathematics, in an inner-urban school district.” In Proceedings of the ASEE Annual Conference. Portland, Oregon, 2005.
Oakley, B, et al. “Using the Kumon method to revitalize mathematics in an inner-urban school district.” In Proceedings of the ASEE. Nashville, TN, 2003.
Orcos, L, et al. “The Kumon method: its importance in the improvement on the teaching and learning of mathematics from the first levels of early childhood and primary education.” Mathematics 7, no. 1 (2019): 109.
Rohrer, D, et al. “The scarcity of interleaved practice in mathematics textbooks.” Educational Psychology Review 32, no. 3 (2020): 873-883. (This paper points out that Saxon is the rare exception to math textbooks in that it heavily employs interleaving.)
Sulasteri, S, et al. “The effect of Kumon learning model on mathematics learning outcomes in cognitive style view.” In Journal of Physics: Conference Series, 1581, 012052: IOP Publishing, 2020.
Watanabe, M. Juku: The Stealth Force of Education and the Deterioration of Schools in Japan. Charleston, SC USA: CreateSpace, 2013.
Watanabe, M. “Training math athletes in Japanese jukus.” Juku (2015).
Wood, T, et al. “The effect of peer-assisted mathematics learning opportunities in first grade classrooms: What works for whom?” Journal of Research on Educational Effectiveness 13, no. 4 (2020): 601-624. Interleaving and Spaced Repetition
Brunmair, M and Richter, T. “Similarity matters: A meta-analysis of interleaved learning and its moderators.” Psychol Bull 145, no. 11 (2019): 1029-1052.
Carpenter, SK. “Distributed practice or spacing effect.” In The Oxford Research Encyclopedia of Education, 2020.
Carvalho, PF and Goldstone, RL. “When does interleaving practice improve learning?” In The Cambridge Handbook of Cognition and Education, edited by J. Dunlosky and K. A. Rawson, 2019.
Feng, K, et al. “Spaced learning enhances episodic memory by increasing neural pattern similarity across repetitions.” The Journal of Neuroscience 39, no. 27 (2019): 5351-5360.
Foster, NL, et al. “Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice.” Mem Cognit 47, no. 6 (2019): 1088-1101.
Lyle, KB, et al. “How the amount and spacing of retrieval practice affect the short-and long-term retention of mathematics knowledge.” Educational Psychology Review, (2019): 1-19.
Nemeth, L, et al. “Interleaved learning in elementary school mathematics: Effects on the flexible and adaptive use of subtraction strategies.” Frontiers in Psychology 10, (2019): 86.
Oakley, B. “Make your daughter practice math. She’ll thank you later. The way we teach math in America hurts all students, but it may be hurting girls the most.” New York Times (2018).
Rohrer, D, et al. “The scarcity of interleaved practice in mathematics textbooks.” Educational Psychology Review, (2020): 1-11.

Week 3 part 2

Video 6: Motivation, Habit and Salt

Salt

Trinquart, L, et al. “Why do we think we know what we know? A metaknowledge analysis of the salt controversy.” Int J Epidemiol, (2016).

Habit

Wood, W. Good Habits, Bad Habits: The Science of Making Positive Changes that Stick: Farrar, Straus and Giroux, 2019.

Practice

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.
Bjork, RA. “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, RA and Bjork, EL. “The myth that blocking one’s study or practice by topic or skill enhances learning.” In Education Myths: An Evidence-Informed Guide for Teachers, edited by C. Barton, 57–70: John Catt Educational, 2019.
Brunmair, M and Richter, T. “Similarity matters: A meta-analysis of interleaved learning and its moderators.” Psychol Bull 145, no. 11 (2019): 1029-1052.
Carpenter, SK. “Distributed practice or spacing effect.” In Oxford Research Encyclopedia of Education, 2020.
Carvalho, PF and Goldstone, RL. “When does interleaving practice improve learning?” In The Cambridge Handbook of Cognition and Education, edited by J. Dunlosky and K. A. Rawson, 2019.
Eglington, LG and Pavlik, PI, Jr. “Optimizing practice scheduling requires quantitative tracking of individual item performance.” npj Sci Learn 5, (2020): 15.
Feng, K, et al. “Spaced learning enhances episodic memory by increasing neural pattern similarity across repetitions.” The Journal of Neuroscience 39, no. 27 (2019): 5351-5360.
Foster, NL, et al. “Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice.” Mem Cognit 47, no. 6 (2019): 1088-1101.
Lyle, KB, et al. “How the amount and spacing of retrieval practice affect the short-and long-term retention of mathematics knowledge.” Educational Psychology Review, (2019): 1-19.
Nemeth, L, et al. “Interleaved learning in elementary school mathematics: Effects on the flexible and adaptive use of subtraction strategies.” _Frontiers in Psycholog_y 10, (2019): 86.
Nakata, T and Suzuki, Y. “Mixing grammar exercises facilitates long‐term retention: Effects of blocking, interleaving, and increasing practice.” The Modern Language Journal, (2019).
Pili-Moss, D, et al. “Contributions of declarative and procedural memory to accuracy and automatization during second language practice.” Bilingualism: Language and Cognition 23, no. 3 (2019): 639-651.
Oakley, B. “Make your daughter practice math. She’ll thank you later. The way we teach math in America hurts all students, but it may be hurting girls the most.” New York Times (2018).
Rohrer, D, et al. “The scarcity of interleaved practice in mathematics textbooks.” Educational Psychology Review, (2020): 1-11 .
Suzuki, Y, et al. “The role of working memory in blocked and interleaved grammar practice: Proceduralization of L2 syntax.” Language Teaching Research, (2020).

Video 7: Rubrics

Rubrics

Brookhart, SM. “Appropriate criteria: Key to effective rubrics.” Frontiers in Education 3, Article 22 (2018).
Colvin, E, et al. “Exploring the way students use rubrics in the context of criterion referenced assessment.” In Research and Development in Higher Education: The Shape of Higher Education, edited by M. H. Davis and A. Goody, 42-52: HERDSA (Higher Education Research & Development Society of Australasia, Inc), 2016.
Bacchus, R, et al. “When rubrics aren’t enough: Exploring exemplars and student rubric co-construction.” Journal of Curriculum and Pedagogy 17, no. 1 (2019): 48-61.

Comparative judgment (Insight into what rubrics can miss):

Wheadon, C, et al. “A comparative judgement approach to the large-scale assessment of primary writing in England.” Assessment in Education: Principles, Policy & Practice 27, no. 1 (2020): 46-64.
Wheadon, C, et al. “The classification accuracy and consistency of comparative judgement of writing compared to rubric-based teacher assessment.” SocArXiv, (2020). https://osf.io/preprints/socarxiv/vzus4/download.

Video 8: Helping Students Succeed in Test-Taking

Testing and stress

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.
Charles, ST, et al. “The mixed benefits of a stressor-free life.” Emotion, pre-publication (2021). https://psycnet.apa.org/record/2021-21143-001
Rudland, JR, et al. “The stress paradox: How stress can be good for learning.” Med Educ 54, no. 1 (2020): 40-45.

Breathing techniques

Nestor, J. Breath: The New Science of a Lost Art, Riverhead Books, 2020. (This is one of our favorite books.)

The value of reframing

Oettingen, G. Rethinking Positive Thinking: Inside the New Science of Motivation: Current, 2015.

Hard Start Technique

Oakley, Barbara Ann. A Mind for Numbers: How to Excel at Math and Science. New York, NY: Penguin-Random House, 2014.

Video 9: Ensuring Equity, Fairness, and Inclusion in Your Testing

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.

Video 3A-8: Helping Students Succeed in Test-Taking

Week 4

week 4 part 1

NOTE: All these references and readings are optional

Chapter 5 of Uncommon Sense Teaching is especially helpful in providing helpful information related to this material.

Video 1: Children’s Changing Brains

How the Brain Matures

Cho, S, et al. “Hippocampal-prefrontal engagement and dynamic causal interactions in the maturation of children’s fact retrieval.” Journal of Cognitive Neuroscience 24, no. 9 (2012): 1849-1866.
Dehaene, Stanislas. How We Learn: Why Brains Learn Better Than Any Machine… For Now: Viking, 2020. This is a great general overview
Geary, DC and Berch, DB. Evolutionary Perspectives on Child Development and Education: Springer International Publishing, 2016.
Qin, S, et al. “Hippocampal-neocortical functional reorganization underlies children’s cognitive development.” Nat Neurosci 17, (2014): 1263-1269.

Dyscalculia

Geary, David C., Daniel B. Berch, and Kathleen Mann Koepke. “Introduction: Cognitive Foundations of Mathematical Interventions and Early Numeracy Influences.” In Mathematical Cognition and Learning, Vol 5: Elsevier, 2019.

Paradigm Shifts

Kuhn, Thomas. The Structure of Scientific Revolutions. Chicago, IL: University of Chicago Press, 50th Anniversary Edition, 2012.

That Pesky “R” Sound

Fox, Anthony. “To ‘r’ is Human? Intrusive Remarks on a Recent Controversy.” Journal of the International Phonetic Association 8, no. 1/2 (1978): 72-74.

Video 2: When It Comes to Learning, Some Stuff Is Easy and Some Is Hard

Toddlers Learn Incredibly Quickly

Borgstrom, K., J. von Koss Torkildsen, and M. Lindgren. “Substantial gains in word learning ability between 20 and 24 months: A longitudinal ERP study.” Brain Lang 149 (Oct 2015): 33-45. https://dx.doi.org/10.1016/j.bandl.2015.07.002.

Theory of biologically primary and secondary material

This theory was originally conceived by cognitive developmental and evolutionary psychologist David Geary in 1995 . Geary, D. C. “Reflections of evolution and culture in children’s cognition: Implications for mathematical development and instruction.” American Psychologist 50, no. 1 (1995): 24-37.
Geary, David C, and Daniel B Berch. Evolutionary Perspectives on Child Development and Education: Springer International Publishing, 2016.

Neuronal recycling hypothesis

Dehaene, Stanislas. “Evolution of human cortical circuits for reading and arithmetic: The ‘neuronal recycling’ hypothesis.” In From Monkey Brain to Human Brain. Edited by Stanislas Dehaene, Jean-René Duhamel, and Marc D. Hauser. Cambridge, MA: MIT Press, 2005.
Dehaene, S., and L. Cohen. “Cultural recycling of cortical maps.” Neuron 56, no. 2 (Oct 25 2007): 384-98. https://dx.doi.org/10.1016/j.neuron.2007.10.004.

The size of hunter-gatherer groups

Dunbar, Robin. “Neocortex size as a constraint on group size in primates.” Journal of Human Evolution 22, no. 6 (1992): 469-493.

Maya Angelou

Gillespie, MA, et al. Maya Angelou: A Glorious Celebration: Doubleday, 2008.

Math and science death march

Drew, C. “Why science majors change their minds (it’s just so darn hard).” New York Times (2011).

Precocity in Learning

Dark, VJ and Benbow, CP. “Differential enhancement of working memory with mathematical versus verbal precocity.” Journal of Educational Psychology 83, no. 1 (1991): 48.
Myers, T, et al. “Cognitive and neural correlates of mathematical giftedness in adults and children: A review.” Frontiers in Psychology 8, (2017): 1646.
Papadopoulos, TC, et al. “Precocious readers: a cognitive or a linguistic advantage?” European Journal of Psychology of Education, (2020): 1-28.

Math Anxiety in Observed in Those with Lower Working Memory Capacity

Ashcraft, MH and Kirk, EP. “The relationships among working memory, math anxiety, and performance.” Journal of Experimental Psychology: General 130, no. 2 (2001): 224.

week 4 part 2

NOTE: All these references and readings are optional

Chapter 5 of Uncommon Sense Teaching is especially helpful in providing helpful information related to this material.

Video 3: How Should Teaching Change as Students Grapple with More Difficult Material?

Boxer, A, ed. The researchED Guide to Explicit & Direct Instruction: An Evidence-Informed Guide for Teachers: John Catt Educational, 2019.
Engelmann, S and Carnine, D. Theory of Instruction: Principles and Applications: NIFDI Press, 1982. Reprint, Revised edition, 2016.
Estes, T and Mintz, SL. Instruction: A Models Approach. 7th ed.: Pearson, 2015.
Marshall, C. “Montessori education: A review of the evidence base.” npj Science of Learning 2, no. 1 (2017): 1-9.
Mourshed, M, et al. How to Improve Student Educational Outcomes: New Insights from Data Analytics. (Report) McKinsey & Company, 2017. This report observes: “We analyzed the PISA results to understand the relative impact of each of these practices. In all five regions, scores were generally higher when teachers took the lead. The more inquiry-based teaching was used, however, the lower the average PISA scores were. At first glance that looks like a damning verdict on inquiry-based teaching. When we dig deeper into the data, however, a more interesting story is revealed: the best results are achieved when the two styles work together. The “sweet spot” is to use teacher-directed instruction in most or almost all lessons, and inquiry-based teaching in some lessons. This pattern holds true across all five regions.” The report goes on to say “School systems need to tread carefully in selecting inquiry-based teaching practices, however. Our analysis shows that there is a set of practices that have a negative impact on average student scores across almost all regions—even when applied in only some lessons. These practices include having students design their own experiments, asking them to do investigations to test ideas, having a class debate about investigations, and requiring students to argue about science questions.”
National Institute for Direct Instruction. “Project Follow Through.”
Stockard, J, et al. “The effectiveness of direct instruction curricula: A meta-analysis of a half century of research.” Review of Educational Research 88, no. 4 (2018): 479-507.
Stokke, A. What to Do about Canada’s Declining Math Scores? (Report) CD Howe Institute Commentary 427, (2015). The report observes: “One way to redress the balance between instructional techniques that are effective and those that are less so would be to follow an 80/20 rule whereby at least 80 percent of instructional time is devoted to direct instructional techniques and 20 percent of instructional time (at most) favours discovery-based techniques. Although some individual teachers already might follow a roughly 80/20 rule, provincial curricula, teachers’ professional development sessions and provincially approved (or mandated) textbooks tend to favour discovery-based techniques. Thus, pedagogical directives that stress ineffective discovery techniques should be removed from the curricula, and texts that incorporate effective direct instructional techniques should be included in provincially recommended textbook lists.”

Video 4: Direct Instruction Versus Student-Directed Approaches

Boxer, A, ed. The researchED Guide to Explicit & Direct Instruction: An Evidence-Informed Guide for Teachers: John Catt Educational, 2019.
Engelmann, S and Carnine, D. Theory of Instruction: Principles and Applications: NIFDI Press, 1982. Reprint, Revised edition, 2016.
Eskelson, TC. “How and why formal education originated in the emergence of civilization.” Journal of Education and Learning 9, no. 2 (2020): 29-47.
Estes, T and Mintz, SL. Instruction: A Models Approach. 7th ed.: Pearson, 2015.
Freeman, S, et al. “Active learning increases student performance in science, engineering, and mathematics.” Proceedings of the National Academy of Sciences 111, no. 23 (2014): 8410-8415.
Geary, D.C., and D.B. Berch. “Chapter 9. Evolution and children’s cognitive and academic development.” In Evolutionary Perspectives on Child Development and Education, 217–49: Springer, 2016. The authors note: “We have suggested that structured, explicit, teacher-directed instruction should be most effective when acquiring secondary skills that are remote from supporting primary systems and that take place in a species atypical, classroom context where the goal is oriented toward acquiring knowledge for its own sake [italics in original].” Interestingly, 2012 PISA achievement versus teaching style reveals a pattern supportive of Geary and Berch’s findings. The better the PISA scores, the more likely the country is to use direct instruction. See Mourshed, M, et al. How to Improve Student Educational Outcomes: New Insights from Data Analytics. McKinsey & Company, 2017.
Howell, Robert, and Phoebe Park “How YouTube made Julius Yego an Olympic medalist,” CNN.
Kirschner, PA, et al. “Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching.” Educ Psychol 41, no. 2 (2006): 75-86.
Stockard, J, et al. “The effectiveness of direct instruction curricula: A meta-analysis of a half century of research.” Review of Educational Research 88, no. 4 (2018): 479-507.

Video 5: Driving Home the Main Ideas

Boxer, A, ed. The researchED Guide to Explicit & Direct Instruction: An Evidence-Informed Guide for Teachers: John Catt Educational, 2019.
Engelmann, S and Carnine, D. Theory of Instruction: Principles and Applications: NIFDI Press, 1982. Reprint, Revised edition, 2016.
Freireich, Abby and Brian Platzer, T_aking the Stress Out of Homework: Organizational, Content-Specific, and Test-Prep Strategies to Help Your Children Help Themselves_, Avery, 2021. This excellent book serves as a great guide for parents trying to help their children in school.
Geary, D.C., and D.B. Berch. “Chapter 9. Evolution and children’s cognitive and academic development.” In Evolutionary Perspectives on Child Development and Education, 217–49: Springer, 2016.
Lemov, D. Teach Like a Champion 2.0: John Wiley & Sons, 2015. This provides an excellent explanation of “I do, we do, you do,” along with other important facets of direct instruction.
Stockard, J, et al. “The effectiveness of direct instruction curricula: A meta-analysis of a half century of research.” Review of Educational Research 88, no. 4 (2018): 479-507.

Video 7: Wrap Up, Course 1

Bruer, JT. “Education and the brain: A bridge too far.” Educational Researcher 26, no. 8 (1997): 4-16.

🪴 PAR Brain

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Uncommon sense teaching notes

week 1

week 1 part 1

Week 1 Part 2

NOTE: All these references and readings are optional

Week 2

Week 2 part 1

NOTE: All these references and readings are optional

Week 2 part 2

NOTE: All these references and readings are optional

Week 3

week 3 part 1

Week 3 part 2

Week 4

week 4 part 1

NOTE: All these references and readings are optional

week 4 part 2

NOTE: All these references and readings are optional

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