DOCSLIB.ORG

Enhancing Human Learning Via Spaced Repetition Optimization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Enhancing Human Learning Via Spaced Repetition Optimization Enhancing human learning via spaced repetition optimization Behzad Tabibiana,b,1, Utkarsh Upadhyaya, Abir Dea, Ali Zarezadea, Bernhard Scholkopf¨ b, and Manuel Gomez-Rodrigueza aNetworks Learning Group, Max Planck Institute for Software Systems, 67663 Kaiserslautern, Germany; and bEmpirical Inference Department, Max Planck Institute for Intelligent Systems, 72076 Tubingen,¨ Germany Edited by Richard M. Shiffrin, Indiana University, Bloomington, IN, and approved December 14, 2018 (received for review September 3, 2018) Spaced repetition is a technique for efficient memorization which tion algorithms. However, most of the above spaced repetition uses repeated review of content following a schedule determined algorithms are simple rule-based heuristics with a few hard- by a spaced repetition algorithm to improve long-term reten- coded parameters (8), which are unable to fulfill this promise— tion. However, current spaced repetition algorithms are simple adaptive, data-driven algorithms with provable guarantees have rule-based heuristics with a few hard-coded parameters. Here, been largely missing until very recently (14, 15). Among these we introduce a flexible representation of spaced repetition using recent notable exceptions, the work most closely related to ours the framework of marked temporal point processes and then is by Reddy et al. (15), who proposed a queueing network model address the design of spaced repetition algorithms with prov- for a particular spaced repetition method—the Leitner system able guarantees as an optimal control problem for stochastic (9) for reviewing flashcards—and then developed a heuristic differential equations with jumps. For two well-known human approximation algorithm for scheduling reviews. However, their memory models, we show that, if the learner aims to maximize heuristic does not have provable guarantees, it does not adapt recall probability of the content to be learned subject to a cost to the learner’s performance over time, and it is specifically on the reviewing frequency, the optimal reviewing schedule is designed for the Leitner systems. given by the recall probability itself. As a result, we can then In this work, we develop a computational framework to derive develop a simple, scalable online spaced repetition algorithm, optimal spaced repetition algorithms, specially designed to adapt MEMORIZE, to determine the optimal reviewing times. We per- to the learner’s performance, as continuously monitored by mod- APPLIED form a large-scale natural experiment using data from Duolingo, a ern spaced repetition software and online learning platforms. MATHEMATICS popular language-learning online platform, and show that learn- More specifically, we first introduce a flexible representation ers who follow a reviewing schedule determined by our algorithm of spaced repetition using the framework of marked temporal memorize more effectively than learners who follow alternative point processes (16). For several well-known human memory schedules determined by several heuristics. models (1, 12, 17–19), we use this presentation to express the dynamics of a learner’s forgetting rates and recall probabilities for the content to be learned by means of a set of stochastic memorization j spaced repetition j human learning j marked differential equations (SDEs) with jumps. Then, we can find the temporal point processes j stochastic optimal control Significance Our ability to remember a piece of information depends crit- ically on the number of times we have reviewed it, the temporal Understanding human memory has been a long-standing distribution of the reviews, and the time elapsed since the last problem in various scientific disciplines. Early works focused review, as first shown by a seminal study by Ebbinghaus (1). The on characterizing human memory using small-scale controlled effect of these two factors has been extensively investigated in the experiments and these empirical studies later motivated the experimental psychology literature (2, 3), particularly in second design of spaced repetition algorithms for efficient memo- language acquisition research (4–7). Moreover, these empirical rization. However, current spaced repetition algorithms are studies have motivated the use of flashcards, small pieces of rule-based heuristics with hard-coded parameters, which do information a learner repeatedly reviews following a schedule not leverage the automated fine-grained monitoring and determined by a spaced repetition algorithm (8), whose goal is to greater degree of control offered by modern online learning ensure that learners spend more (less) time working on forgotten platforms. In this work, we develop a computational frame- (recalled) information. work to derive optimal spaced repetition algorithms, specially The task of designing spaced repetition algorithms has a rich designed to adapt to the learners’ performance. A large-scale history, starting with the Leitner system (9). More recently, natural experiment using data from a popular language- several works (10, 11) have proposed heuristic algorithms that learning online platform provides empirical evidence that the schedule reviews just as the learner is about to forget an item, spaced repetition algorithms derived using our framework are i.e., when the probability of recall, as given by a memory model significantly superior to alternatives. of choice (1, 12), falls below a threshold. An orthogonal line of research (7, 13) has pursued locally optimal scheduling by iden- Author contributions: B.T., U.U., B.S., and M.G.-R. designed research; B.T., U.U., A.D., A.Z., tifying which item would benefit the most from a review given and M.G.-R. performed research; B.T. and U.U. analyzed data; and B.T., U.U., and M.G.-R. a fixed reviewing time. In doing so, the researchers have also wrote the paper.y proposed heuristic algorithms that decide which item to review The authors declare no conflict of interest.y by greedily selecting the item which is closest to its maximum This article is a PNAS Direct Submission.y learning rate. This open access article is distributed under Creative Commons Attribution License 4.0 In recent years, spaced repetition software and online plat- (CC BY).y forms such as Mnemosyne (mnemosyne-proj.org), Synap (www. Data deposition: The MEMORIZE algorithm has been deposited on GitHub (https:// synap.ac), and Duolingo (www.duolingo.com) have become github.com/Networks-Learning/memorize).y increasingly popular, often replacing the use of physical flash- 1 To whom correspondence should be addressed. Email: [email protected] cards. The promise of these pieces of software and online This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. platforms is that automated fine-grained monitoring and greater 1073/pnas.1815156116/-/DCSupplemental.y degree of control will result in more effective spaced repeti- www.pnas.org/cgi/doi/10.1073/pnas.1815156116 PNAS Latest Articles j 1 of 6 Downloaded by guest on September 29, 2021 optimal reviewing schedule for spaced repetition by solving a i.e., E[dN(t)] = u(t)dt, and think of the recall r as their binary stochastic optimal control problem for SDEs with jumps (20–23). marks. Moreover, every time that a learner reviews an item, the In doing so, we need to introduce a proof technique to find a recall r has been experimentally shown to have an effect on solution to the so-called Hamilton–Jacobi–Bellman (HJB) equa- the forgetting rate of the item (3, 15, 25). Here, we estimate tion (SI Appendix, sections 3 and 4), which is of independent such an effect using half-life regression (25), which implicitly interest. assumes that recalls of an item i during a review have a multi- For two well-known memory models, we show that, if the plicative effect on the forgetting rate ni (t)—a successful recall learner aims to maximize recall probability of the content to be at time tr changes the forgetting rate by (1 − αi ), i.e., ni (t) = learned subject to a cost on the reviewing frequency, the solu- (1 − αi )ni (tr ), αi ≤ 1, while an unsuccessful recall changes the tion uncovers a linear relationship with a negative slope between forgetting rate by (1 + βi ), i.e., ni (t) = (1 + βi )ni (tr ), βi ≥ 0. In the optimal rate of reviewing, or reviewing intensity, and the this context, the initial forgetting rate, ni (0), captures the diffi- recall probability of the content to be learned. As a consequence, culty of the item, with more difficult items having higher initial we can develop a simple, scalable online spaced repetition algo- forgetting rates compared with easier items, and the parame- rithm, which we name MEMORIZE, to determine the optimal ters αi , βi , and ni (0) are estimated using real data (refer to SI reviewing times. Finally, we perform a large-scale natural exper- Appendix, section 8 for more details). iment using data from Duolingo, a popular language-learning Before we proceed farther, we acknowledge that several lab- online platform, and show that learners who follow a reviewing oratory studies (6, 27) have provided empirical evidence that schedule determined by our algorithm memorize more effec- the retention rate follows an inverted U shape, i.e., mass prac- tively than learners who follow alternative schedules determined tice does not improve the forgetting rate—if an item is in by several heuristics. To facilitate research in this area, we are a learner’s short-term memory when the review happens, the releasing an open-source implementation of our algorithm (24). long-term retention does not improve. Thus, one could argue for time-varying parameters αi (t) and βi (t) in our framework. Modeling Framework of Spaced Repetition. Our framework is However, there are several reasons that prevent us from that: agnostic to the particular choice of memory model—it provides (i) The derivation of an optimal reviewing schedule under a set of techniques to find reviewing schedules that are opti- time-varying parameters becomes very challenging; (ii) for the mal under a memory model.
Load more

Recommended publications
  • A Queueing-Theoretic Foundation for Optimal Spaced Repetition
    A Queueing-Theoretic Foundation for Optimal Spaced Repetition Siddharth Reddy [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14850 Igor Labutov [email protected] Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850 Siddhartha Banerjee [email protected] School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14850 Thorsten Joachims [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14850 1. Extended Abstract way back to 1885 and the pioneering work of Ebbinghaus (Ebbinghaus, 1913), identify two critical variables that de- In the study of human learning, there is broad evidence that termine the probability of recalling an item: reinforcement, our ability to retain a piece of information improves with i.e., repeated exposure to the item, and delay, i.e., time repeated exposure, and that it decays with delay since the since the item was last reviewed. Accordingly, scientists last exposure. This plays a crucial role in the design of ed- have long been proponents of the spacing effect for learn- ucational software, leading to a trade-off between teaching ing: the phenomenon in which periodic, spaced review of new material and reviewing what has already been taught. content improves long-term retention. A common way to balance this trade-off is spaced repe- tition, which uses periodic review of content to improve A significant development in recent years has been a grow- long-term retention. Though spaced repetition is widely ing body of work that attempts to ‘engineer’ the process used in practice, e.g., in electronic flashcard software, there of human learning, creating tools that enhance the learning is little formal understanding of the design of these sys- process by building on the scientific understanding of hu- tems.
    [Show full text]
  • The Spaced Interval Repetition Technique
    The Spaced Interval Repetition Technique What is it? The Spaced Interval Repetition (SIR) technique is a memorization technique largely developed in the 1960’s and is a phenomenal way of learning information very efficiently that almost nobody knows about. It is based on the landmark research in memory conducted by famous psychologist, Hermann Ebbinghaus in the late 19th century. Ebbinghaus discovered that when we learn new information, we actually forget it very quickly (within a matter of minutes and hours) and the more time that passes, the more likely we are to forget it (unless it is presented to us again). This is why “cramming” for the test does not allow you to learn/remember everything for the test (and why you don’t remember any of it a week later). SIR software, notably first developed by Piotr A. Woźniak is available to help you learn information like a superhero and even retain it for the rest of your life. The SIR technique can be applied to any kind of learning, but arguably works best when learning discrete pieces of information like dates, definitions, vocabulary, formulas, etc. Why does it work? Memory is a fickle creature. For most people, to really learn something, we need to rehearse it several times; this is how information gets from short‐term memory to long‐ term memory. Research tells us that the best time to rehearse something (like those formulas for your statistics class) is right before you are about to forget it. Obviously, it is very difficult for us to know when we are about to forget an important piece of information.
    [Show full text]
  • Arichardson-Klavehn Rbjork 2002
    1096 Memory, Development of Schneider W and Bjorklund DF (1998) Memory. In: Kuhn Schneider W and Pressley M (1997) Memory Development D and Siegler RS (eds) Handbook of Child Psychology, between 2 and 20, 2nd edn. Mahwah, NJ: Lawrence vol. 2, Cognition, Perception, and Language, 5th edn, Erlbaum. pp. 467±521. New York, NY: John Wiley. Memory, Long-term Introductory article Alan Richardson-Klavehn, Goldsmiths College, University of London, London, UK Robert ABjork, University of California, Los Angeles, USA CONTENTS Definition and classification of long-term memory The constructive character of long-term memory The dynamic character of long-term memory Conclusion Long-term memory is central to cognitive function- interconnected and cannot be understood in ing. Taking a wide variety of forms, from skills to isolation from each other. (See Information Pro- general knowledge to memory for personal experi- cessing) ences, it is characterized by dynamic interactions between encoding and retrieval processes and by constructive processes, and thus differs fundamen- Distinguishing between Short-term and tally from current human-made information storage Long-term Memory systems. In everyday discourse, long-term memory is usu- ally distinguished from short-term memory in DEFINITION AND CLASSIFICATION OF terms of the time that has elapsed since information LONG-TERM MEMORY was encoded. Moreover, it is not unusual to find memory that persists over days or weeks being The ability to retain information over long periods described as short-term memory. In psychology, is fundamental to intelligent thought and behavior. however, the terms long-term and short-term Memory is the `glue', in effect, that holds our intel- memory have come to have specialized meanings lectual processes together, from perception, atten- that stem from a distinction made by William tion, and language, to reasoning, decision-making, James in 1890.
    [Show full text]
  • Teaching the Science of Learning Yana Weinstein1* , Christopher R
    Weinstein et al. Cognitive Research: Principles and Implications (2018) 3:2 Cognitive Research: Principles DOI 10.1186/s41235-017-0087-y and Implications TUTORIALREVIEW Open Access Teaching the science of learning Yana Weinstein1* , Christopher R. Madan2,3 and Megan A. Sumeracki4 Abstract The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies. Keywords: Education, Learning, Memory, Teaching Significance educators who might be interested in integrating the Education does not currently adhere to the medical strategies into their teaching practice, (b) science of learn- model of evidence-based practice (Roediger, 2013). ing researchers who are looking for open questions to help However, over the past few decades, our field has made determine future research priorities, and (c) researchers in significant advances in applying cognitive processes to other subfields who are interested in the ways that princi- education. From this work, specific recommendations ples from cognitive psychology have been applied to can be made for students to maximize their learning effi- education. ciency (Dunlosky, Rawson, Marsh, Nathan, & Willing- While the typical teacher may not be exposed to this ham, 2013; Roediger, Finn, & Weinstein, 2012).
    [Show full text]
  • COPYRIGHTED MATERIAL Hack 1: Remember to Remember
    HaleEvans c01.indd V4 - 07/21/2011 Page 1 CHAPTER 1 Memory In classical Greece, Mnemosyne — usually translated as “Memory” — was considered to be the mother of History, Music, Astronomy, and all the other Muses. Memory is fundamental to learning and knowledge, so we’ve placed our chapter of memory-enhancement techniques fi rst. They include how to build memory on previously memorized environments (Hack 2, “Build a Memory Dungeon”), how to use technology to remember most effi ciently (Hack 4, “Space Your Repetitions”), and how to draw old memories that you thought you had lost back to the surface (Hack 5, “Recall Long-Ago Events”). Boosting your memory will help you both gather new information and track where you’ve been in your life, bringing your past with you into the future. Our goal is to help you hold onto all the intangible treasures your academic pursuits and life experience bring you. COPYRIGHTED MATERIAL Hack 1: Remember to Remember Ever use a fancy mnemonic only to forget that you memorized anything at all? Prospective memory is remembering to do something in the future. Learn to cue your prospective memory in ways that go far beyond a string around your fi nger. The traditional method to remind yourself that you need to remember some- thing (and a staple of clip art collections) is a string tied around your fi nger, but 1 cc01.indd01.indd 1 88/9/2011/9/2011 66:26:53:26:53 PPMM HaleEvans c01.indd V4 - 08/05/2011 Page 2 2 Chapter 1 n Memory there are many ways you can improve your prospective memory – or remember- ing to remember.
    [Show full text]
  • Learning with Smartphones
    LEARNING WITH SMARTPHONES Case study of learning drug prescribing using the PharmaFrog app Inauguraldissertation zur Erlangung des Akademischen Grades eines Dr. phil., vorgelegt dem Fachbereich 02 Sozialwissenschaften, Medien und Sport der Johannes Gutenberg-Universität Mainz von Dativa Tibyampansha aus Tansania Mainz, Germany Tag des Prüfungskolloquiums: July 15, 2020 PRELIMINARY REMARKS I would like acknowledge the indispensable contributions of the following individuals and institutions: 1) This thesis’ advisors 2) Programmers 3) PharmaFrog app concept and content developers 4) The participants of the surveys and of the PharmaFrog app evaluation presented in this thesis 5) My family and friends for moral support and encouragement. The PharmaFrog app can be downloaded using the following QR codes. Android iOS i TABLE OF CONTENTS LIST OF FIGURES ................................................................................................................. vi LIST OF TABLES ................................................................................................................. viii ABBREVIATIONS .................................................................................................................. ix SUMMARY ............................................................................................................................... 1 INTRODUCTION..................................................................................................................... 3 Deficiencies in drug prescribing ....................................................................................................................
    [Show full text]
  • Senior Students
    Digital Ambition Parent Discussion Guide This sheet is intended to help you have a discussion with your child about how to study in the most effective way. Below, we have a number of useful study applications and suggested questions that you and your child can ask each other about how you both learn to do new things and manage your time. By sharing your experiences with your child, we hope that you both improve your productivity in both study and work. Each question should be answered by your child AND yourself. Applications that help you create Applications that reduce/block distractions flashcards/quizzes and study schedules One of the biggest problems for modern students is the number of online and mobile distractions that hinder effective study. Research has shown that retrieval practice is one of the most effective ways of studying. Retrieval practice is testing Many of these websites and programs have hundreds of yourself regularly about the key ideas in anything new you’re engineers designing them to figure out the best way to get learning. One common way of this is creating flashcards on people to use them and keep using them, so it’s not a surprise the key ideas as you’re reading the material for the first time that many students give into the temptation to check their and then testing yourself as a way of studying the material. phone or surf the web for a minute and then find out that they’ve wasted most of their study time. Research also shows that if you spread out what you are studying so that you revise material just before you’re about Rescuetime: to forget it, your long-term learning will be much better.
    [Show full text]
  • Visualizing Words and Knowledge: Arts of Memory from the Agora to the Computer
    Syracuse University SURFACE Dissertations - ALL SURFACE May 2015 Visualizing Words and Knowledge: Arts of Memory from the Agora to the Computer Seth D. Long Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Arts and Humanities Commons Recommended Citation Long, Seth D., "Visualizing Words and Knowledge: Arts of Memory from the Agora to the Computer" (2015). Dissertations - ALL. 222. https://surface.syr.edu/etd/222 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE. It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE. For more information, please contact [email protected]. ABSTRACT This dissertation examines rhetoric’s fourth canon—the art of memory—tracing its development through the classical, medieval, and early modern periods. It argues that for most of its history, the fourth canon was an art by which words and knowledge were remediated into visual, spatial forms, either in the mind or on the page. And it was this technique of visualization, I argue, that linked the canons of memory and invention throughout history. In contemporary rhetorical theory, however, memory palaces and mnemonic imagery have been replaced with a conception of memory grounded in psychology and critique. I argue that this move away from memory as an artificial practice has obscured the classical art’s visual precepts, consequently severing the ancient link between memory and invention. I suggest that contemporary rhetorical theorists should return to visualization to revitalize the fourth canon in the twenty-first century. Today, digital tools that visualize words and knowledge are ubiquitous.
    [Show full text]
  • Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
    Unbounded Human Learning: Optimal Scheduling for Spaced Repetition Siddharth Reddy Igor Labutov Siddhartha Banerjee Department of Computer Electrical and Computer Operations Research and Science Engineering Information Engineering Cornell University Cornell University Cornell University [email protected] [email protected] [email protected] Thorsten Joachims Department of Computer Science Cornell University [email protected] ABSTRACT 1. INTRODUCTION In the study of human learning, there is broad evidence that The ability to learn and retain a large number of new our ability to retain information improves with repeated ex- pieces of information is an essential component of human posure and decays with delay since last exposure. This plays learning. Scientific theories of human memory, going all the a crucial role in the design of educational software, leading way back to 1885 and the pioneering work of Ebbinghaus [9], to a trade-off between teaching new material and reviewing identify two critical variables that determine the probability what has already been taught. A common way to balance of recalling an item: reinforcement, i.e., repeated exposure this trade-off is spaced repetition, which uses periodic review to the item, and delay, i.e., time since the item was last re- of content to improve long-term retention. Though spaced viewed. Accordingly, scientists have long been proponents repetition is widely used in practice, e.g., in electronic flash- of the spacing effect for learning: the phenomenon in which card software, there is little formal understanding of the periodic, spaced review of content improves long-term reten- design of these systems. Our paper addresses this gap in tion.
    [Show full text]
  • Using Spaced Repetition Software with a TOEIC Wordlist: Preliminary Results
    広島文教女子大学紀要 49，2014 【原著】 Using Spaced Repetition Software with a TOEIC Wordlist: Preliminary Results Arthur Rutson-Griff iths and Jack Bower 間隔反復ソフトを用いた TOEIC 単語の学習に関する予備調査について Arthur Rutson-Griffiths and Jack Bower Abstract This paper presents the preliminary results from the second phase of a study in which participants are studying a TOEIC wordlist using spaced repetition software (SRS). The overall aim of this study is to investigate whether increased usage of the SRS leads to statistically significant gains in TOEIC overall and reading scores. The aim of the second phase of the study is to replicate the first stage of the study, which used the SRS Anki and yielded disappointing results, with the more feature-rich and user-friendly SRS Cooori, and to compare participant attitudes to them. Participants were given a 640 item TOEIC wordlist, and access to this list in Cooori, and asked to study it over one academic year. Participants are tested on their knowledge of the items via 20 item multiple-choice quizzes every two weeks in order to encourage regular study. TOEIC scores at the start of the project will be compared to those at the end, and attitudes are obtained through administration of a survey. This working paper presents preliminary attitudinal data, which suggests participants favor Cooori over Anki, and preliminary usage data, which shows greatly increased usage in the second phase of the study compared to the first. As final TOEIC scores are not yet available, SRS usage is correlated with quiz scores using a non-parametric test. The results suggest that TOEIC scores predict quiz scores, and that while SRS usage does not yet strongly predict quiz scores, usage of the ‘quiz’ feature of Cooori does predict scores.
    [Show full text]
  • A Game to Learn Grammatical Gender in Portuguese Information Systems
    Fishing for Words: A game to learn grammatical gender in Portuguese Pedro Bertrand Cabral Thesis to obtain the Master of Science Degree in Information Systems and Computer Engineering Supervisors: Prof. Carlos Antonio´ Roque Martinho Prof. Nelia´ Maria Pedro Alexandre Examination Committee Chairperson: Prof. Miguel Nuno Dias Alves Pupo Correia Supervisor: Prof. Carlos Antonio´ Roque Martinho Member of the Committee: Prof. Maria Lu´ısa Torres Ribeiro Marques da Silva Coheur June 2019 “And by 1983, I had my dream. I could see the Dragon clearly in my mind’s eye. Let me tell you about my dream. I dreamed of the day when computer games would be a viable medium of artistic expression. An art form!” Chris Crawford, “The Dragon Speech” at GDC 1992 ii Acknowledgments Though I could hardly hope to fit such quantities of gratitude in a simple sheet of paper, one cannot do better than to do their best. Faculdade de Letras da Universidade de Lisboa, Instituto de Cultura e L´ıngua Portuguesa and the professors there whose support and time made data collection for this project possible. My alma mater, Instituto Superior Tecnico´ , which imparted quality education & experience and facil- itated these verdant years of my youth. My professors and advisors, who with wisdom and friendship guided me through this odd journey, teaching me the way and enduring my inexperience. My family, whose caring and nurturing education raised me to this position where I am able to write a full thesis and finish a Master’s degree, who taught me to believe and persevere even when the prospect of failure looms, overshadowing.
    [Show full text]
  • Teaching the Science of Learning
    Cogn Res Princ Implic. 2018 Dec; 3: 2. PMCID: PMC5780548 Published online 2018 Jan 24. doi: 10.1186/s41235-017-0087-y PMID: 29399621 Teaching the science of learning Yana Weinstein, 1 Christopher R. Madan,2,3 and Megan A. Sumeracki4 1Department of Psychology, University of Massachusetts Lowell, Lowell, MA USA 2Department of Psychology, Boston College, Chestnut Hill, MA USA 3School of Psychology, University of Nottingham, Nottingham, UK 4Department of Psychology, Rhode Island College, Providence, RI USA Yana Weinstein, Email: [email protected]. Contributor Information. Corresponding author. Received 2016 Dec 20; Accepted 2017 Dec 2. Copyright © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Abstract The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies. Keywords: Education, Learning, Memory, Teaching Significance Education does not currently adhere to the medical model of evidence-based practice (Roediger, 2013).
    [Show full text]