Skip to main content

Adaptively Adding Cards to a Flashcard Deck Improves Learning Compared to Adaptively Dropping Cards Regardless of Cognitive Ability

  • Conference paper
  • First Online:
Adaptive Instructional Systems (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13332))

Included in the following conference series:

  • 665 Accesses

Abstract

Flashcards are a popular study tool, however learner decisions can lower their effectiveness. One such decision is whether or not to drop a concept from study. Using objective mastery criteria that adaptively determine when to add or drop an item from study based on performance may improve learning outcomes in flashcard-based tasks. The effectiveness of adaptive flashcard-based learning may also vary based on the cognitive ability of the learner. The current study examined the impact of adaptive mastery instructional strategies on learning butterfly species and whether or not the impact of adaptive mastery varies by cognitive ability. Three learning conditions were compared: a No Add/Drop group (all items remain in the deck throughout study), a Mastery Drop group (start with all items, then drop after an item is mastered), and a Mastery Add group (start with three items, add items once mastered). A pre-post-transfer test design was used both immediately after training and one week later. Participants also completed the symmetry span task and a change detection task to evaluate cognitive ability. Results show the worst overall immediate pre-post learning gains in the Mastery Drop condition compared to the Mastery Add and No Add/Drop conditions which showed similar learning gains. This pattern went away when looking at delayed pre-post learning gains. Cognitive ability did not have any impact on learning performance, suggesting that similar strategies work equally well across all levels of cognitive ability. These results suggest adaptively adding cards is better than dropping them, though if there are no time constraints, leaving all concepts in the deck leads to the best overall learning in the short term.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Golding, J.M., Wasarhaley, N.E., Fletcher, B.: The use of flashcards in an introduction to psychology class. Teach. Psychol. 39, 199–202 (2012). https://doi.org/10.1177/0098628312450436

    Article  Google Scholar 

  2. Hartwig, M.K., Dunlosky, J.: Study strategies of college students: are self-testing and scheduling related to achievement? Psychon. Bull. Rev. 19, 126–134 (2012). https://doi.org/10.3758/s13423-011-0181-y

    Article  Google Scholar 

  3. Karpicke, J.D., Butler, A.C., Roediger, H.L.: Metacognitive strategies in student learning: do students practise retrieval when they study on their own? Memory 17, 471–479 (2009). https://doi.org/10.1080/09658210802647009

    Article  Google Scholar 

  4. Kornell, N., Bjork, R.A.: The promise and perils of self-regulated study. Psychon. Bull. Rev. 14, 219–224 (2007). https://doi.org/10.3758/bf03194055

    Article  Google Scholar 

  5. Whitmer, D.E., Johnson, C.I., Marraffino, M.D., Pharmer, R.L., Blalock, L.D.: A Mastery Approach to Flashcard-Based Adaptive Training. In: Sottilare, R.A., Schwarz, J. (eds.) HCII 2020. LNCS, vol. 12214, pp. 555–568. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50788-6_41

    Chapter  Google Scholar 

  6. Bjork, R.A., Dunlosky, J., Kornell, N.: Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev. Psychol. 64, 417–444 (2013). https://doi.org/10.1146/annurev-psych-113011-143823

    Article  Google Scholar 

  7. Kornell, N., Bjork, R.A.: Learning concepts and categories: is spacing the “enemy of induction”? Psychol. Sci. 19, 585–592 (2008). https://doi.org/10.1111/j.1467-9280.2008.02127.x

    Article  Google Scholar 

  8. Senzaki, S., Hackathorn, J., Appleby, D.C., Gurung, R.A.R.: Reinventing flashcards to increase student learning. Psychol. Learn. Teach. 16, 353–368 (2017). https://doi.org/10.1177/1475725717719771

    Article  Google Scholar 

  9. Karpicke, J.D.: Metacognitive control and strategy selection: deciding to practice retrieval during learning. J. Exp. Psychol. Gen. 138, 469–486 (2009)

    Article  Google Scholar 

  10. Miyatsu, T., Nguyen, K., McDaniel, M.A.: Five popular study strategies: their pitfalls and optimal implementations. Perspect. Psychol. Sci. 13, 390–407 (2018). https://doi.org/10.1177/1745691617710510

    Article  Google Scholar 

  11. Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T., Rohrer, D.: Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychol. Bull. 132, 354–380 (2006). https://doi.org/10.1037/0033-2909.132.3.354

    Article  Google Scholar 

  12. Roediger, H.L., Karpicke, J.D.: The power of testing memory: basic research and implications for educational practice. Perspect. Psychol. Sci. 1, 181–210 (2006). https://doi.org/10.1111/j.1745-6916.2006.00012.x

    Article  Google Scholar 

  13. Cepeda, N.J., Coburn, N., Rohrer, D., Wixted, J.T., Mozer, M.C., Pashler, H.: Optimizing distributed practice: theoretical analysis and practical implications. Exp. Psychol. 56, 236–246 (2009). https://doi.org/10.1027/1618-3169.56.4.236

    Article  Google Scholar 

  14. Janiszewski, C., Noel, H., Sawyer, A.G.: A meta-analysis of the spacing effect in verbal learning: implications for research on advertising repetition and consumer memory. J. Consum. Res. 30, 138–149 (2003). https://doi.org/10.1086/374692

    Article  Google Scholar 

  15. Johnson, C.I., Mayer, R.E.: A testing effect with multimedia learning. J. Educ. Psychol. 101, 621–629 (2009). https://doi.org/10.1037/a0015183

  16. Karpicke, J.D., Roediger, H.L.: Repeated retrieval during learning is the key to long-term retention. J. Mem. Lang. 57, 151–162 (2007). https://doi.org/10.1016/j.jml.2006.09.004

    Article  Google Scholar 

  17. Rowland, C.A.: The effect of testing versus study on retention: a meta-analytic review of the testing effect. Psychol. Bull. 140, 1432–1463 (2014). https://doi.org/10.1037/a0037559

    Article  Google Scholar 

  18. Roediger, H.L., Karpicke, J.D.: Test-enhanced learning: taking memory tests improves long-term retention. Psychol. Sci. 17, 249–255 (2006). https://doi.org/10.1111/j.1467-9280.2006.01693.x

  19. Kornell, N.: Optimising learning using flashcards: spacing is more effective than cramming. Appl. Cog. Psychol. 23, 1297–1317 (2009). https://doi.org/10.1002/acp.1537

    Article  Google Scholar 

  20. Kornell, N., Bjork, R.A.: Optimising self-regulated study: the benefits—and costs—of dropping flashcards. Memory 16, 125–136 (2008). https://doi.org/10.1080/09658210701763899

    Article  Google Scholar 

  21. Wissman, K.T., Rawson, K.A., Pyc, M.A.: How and when do students use flashcards? Memory 20, 568–579 (2012). https://doi.org/10.1080/09658211.2012.687052

    Article  Google Scholar 

  22. Pyc, M.A., Rawson, K.A., Aschenbrenner, A.J.: Metacognitive monitoring during criterion learning: when and why are judgments accurate? Mem. Cognit. 42(6), 886–897 (2014). https://doi.org/10.3758/s13421-014-0403-4

    Article  Google Scholar 

  23. Pyc, M.A., Rawson, K.A.: Examining the efficiency of schedules of distributed retrieval practice. Mem. Cogn. 35, 1917–1927 (2007). https://doi.org/10.3758/bf03192925

    Article  Google Scholar 

  24. Pyc, M.A., Rawson, K.A.: Testing the retrieval effort hypothesis: does greater difficulty correctly recalling information lead to higher levels of memory? J. Mem. Lang. 60, 437–447 (2009). https://doi.org/10.1016/j.jml.2009.01.004

    Article  Google Scholar 

  25. Pyc, M.A., Rawson, K.A.: Costs and benefits of dropout schedules of test–restudy practice: implications for student learning. Appl. Cog. Psychol. 25, 87–95 (2011). https://doi.org/10.1002/acp.1646

    Article  Google Scholar 

  26. Van Merriënboer, J.J.G., Kester, L., Paas, F.: Teaching complex rather than simple tasks: balancing intrinsic and germane load to enhance transfer of learning. Appl. Cog. Psychol. 20, 343–352 (2006). https://doi.org/10.1002/acp.1250

    Article  Google Scholar 

  27. Sweller, J.: Cognitive load theory, learning difficulty and instructional design. Learn. Instr. 4, 295–312 (1994). https://doi.org/10.1016/0959-4752(94)90003-5

    Article  Google Scholar 

  28. Sweller, J.: Element interactivity and intrinsic, extraneous and germane cognitive load. Educ. Psychol. Rev. 22, 123–138 (2010). https://doi.org/10.1007/s10648-010-9128-5

    Article  Google Scholar 

  29. Yan, V.X., Bjork, E.L., Bjork, R.A.: On the difficulty of mending metacognitive illusions: a priori theories, fluency effects, and misattributions of the interleaving benefit. J. Exp. Psychol. Gen. 145, 918–933 (2016). https://doi.org/10.1037/xge0000177

    Article  Google Scholar 

  30. Durlach, P.J., Ray, J.M.: Designing Adaptive Instructional Environments: Insights from Empirical Evidence. Technical Report, U.S. Army Research Institute for the Behavioral and Social Sciences (2011)

    Google Scholar 

  31. Landsberg, C.R., Astwood, R.S., Jr., Van Buskirk, W.L., Townsend, L.N., Steinhauser, N.B., Mercado, A.D.: Review of adaptive training system techniques. Mil. Psychol. 24, 96–113 (2012). https://doi.org/10.1080/08995605.2012.672903

    Article  Google Scholar 

  32. Mettler, E., Burke, T., Massey, C.M., Kellman, P.J.: Comparing adaptive and random spacing schedules during learning to mastery criteria. In: Proceedings of the 42nd Annual Conference of the Cognitive Science Society, pp. 773–779. Cognitive Sciences Society, Virtual (2020)

    Google Scholar 

  33. Mettler, E., Kellman, P.J.: Adaptive response-time-based category sequencing in perceptual learning. Vision. Res. 99, 111–123 (2014). https://doi.org/10.1016/j.visres.2013.12.009

    Article  Google Scholar 

  34. Bjork, E.L., Bjork, R.A.: Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning. In: Gernsbacher, M.A., Pew, R.W., Hough, L.M., Pomerantz, J.R. (eds.) Psychology And the Real World: Essays Illustrating Fundamental Contributions to Society, pp. 56–64. Worth Publishers, New York (2011)

    Google Scholar 

  35. Bjork, R.A.: Memory and metamemory considerations in the training of human beings. In: Metcalfe, J., Shimamura, A. (eds.) Metacognition: Knowing About Knowing, pp. 185–205. MIT Press, Cambridge (1994)

    Google Scholar 

  36. Baddeley, A.D., Hitch, G.: Working memory. In: Psychology of Learning and Motivation, vol. 8, pp. 47–89. Academic Press (1974)

    Google Scholar 

  37. Conway, A.R.A., Kane, M.J., Bunting, M.F., Hambrick, D.Z., Wilhelm, O., Engle, R.W.: Working memory span tasks: a methodological review and user’s guide. Psychon. Bull. Rev. 12, 769–786 (2005). https://doi.org/10.3758/bf03196772

    Article  Google Scholar 

  38. Engle, R.W.: Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23 (2002). https://doi.org/10.1111/1467-8721.00160

    Article  Google Scholar 

  39. Engle, R.W.: Working memory and executive attention: a revisit. Perspect. Psychol. Sci. 13, 190–193 (2018). https://doi.org/10.1177/1745691617720478

    Article  Google Scholar 

  40. Kane, M., Hambrick, Z., Tuholski, S., Wilhelm, O., Payne, T., Engle, R.: The generality of working memory capacity: a latent-variable approach to verbal and visuospatial memory span and reasoning. J. Exp. Psychol. Gen. 133, 189–217 (2004). https://doi.org/10.1037/0096-3445.133.2.189

    Article  Google Scholar 

  41. Engle, R.W., Laughlin, J.E., Tuholski, S.W., Conway, A.R.A.: Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. J. Exp. Psychol. Gen. 128, 309–331 (1999)

    Article  Google Scholar 

  42. Kane, M.J., Bleckley, M.K., Conway, A.R., Engle, R.W.: A controlled-attention view of working-memory capacity. J. Exp. Psychol. Gen. 130, 169–183 (2001)

    Article  Google Scholar 

  43. Klein, K., Boals, A.: The relationship of life event stress and working memory capacity. Appl. Cognit. Psychol. 15, 565–579 (2001). https://doi.org/10.1002/acp.727

    Article  Google Scholar 

  44. Agarwal, P.K., Finley, J.R., Rose, N.S., Roediger, H.L.: Benefits from retrieval practice are greater for students with lower working memory capacity. Memory 25, 764–771 (2017). https://doi.org/10.1080/09658211.2016.1220579

    Article  Google Scholar 

  45. Wang, J., Liu, Z., Xing, Q., Seger, C.A.: The benefit of interleaved presentation in category learning is independent of working memory. Memory 28, 285–292 (2020). https://doi.org/10.1080/09658211.2019.1705490

    Article  Google Scholar 

  46. Cowan, N.: The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–114 (2001). https://doi.org/10.1017/S0140525X01003922

    Article  Google Scholar 

  47. Luck, S.J., Vogel, E.K.: Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends Cogn. Sci. 17, 391–400 (2013). https://doi.org/10.1016/j.tics.2013.06.006

    Article  Google Scholar 

  48. Rouder, J.N., Morey, R.D., Morey, C.C., Cowan, N.: How to measure working memory capacity in the change detection paradigm. Psychon. Bull. Rev. 18(2), 324–330 (2011). https://doi.org/10.3758/s13423-011-0055-3

    Article  Google Scholar 

  49. Hollingworth, A., Richard, A.M., Luck, S.L.: Understanding the function of visual short-term memory: transsaccadic memory, object correspondence, and gaze correction. J. Exp. Psychol. Gen. 137, 163–181 (2008). https://doi.org/10.1037/0096-3445.137.1.163

    Article  Google Scholar 

  50. Hollingworth, A., Matsukura, M., Luck, S.J.: Visual working memory modulates rapid eye movements to simple onset targets. Psychol. Sci. 24, 790–796 (2013). https://doi.org/10.1177/0956797612459767

    Article  Google Scholar 

  51. Alloway, T.P., Alloway, R.G.: Investigating the predictive roles of working memory and IQ in academic attainment. J. Exp. Child Psychol. 106, 20–29 (2010). https://doi.org/10.1016/j.jecp.2009.11.003

    Article  Google Scholar 

  52. Fukuda, K., Vogel, E., Mayr, U., Awh, E.: Quantity, not quality: the relationship between fluid intelligence and working memory capacity. Psychon. Bull. Rev. 17(5), 673–679 (2010). https://doi.org/10.3758/17.5.673

    Article  Google Scholar 

  53. Redick, T.S., et al.: Measuring working memory capacity with automated complex span tasks. Eur. J. Psychol. Assess. 28, 164–171 (2012). https://doi.org/10.1027/1015-5759/a000123

    Article  Google Scholar 

  54. Inquisit 6 Automated Symmetry Span Task (ASSPAN) [computer software]. https://www.millisecond.com (2020)

  55. Luck, S.J., Vogel, E.K.: The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997). https://doi.org/10.1038/36846

    Article  Google Scholar 

  56. Inquisit 6: https://www.millisecond.com (2021)

  57. Inquisit 6: Solving Anagrams. https://www.millisecond.com (2019)

Download references

Acknowledgments

Many thanks to Sean Chancellor, Morgan Kelley, and Crystal Meyer for their assistance with data collection and to Sadan Yagci for his assistance with programming.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lisa Durrance Blalock .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Blalock, L.D. (2022). Adaptively Adding Cards to a Flashcard Deck Improves Learning Compared to Adaptively Dropping Cards Regardless of Cognitive Ability. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2022. Lecture Notes in Computer Science, vol 13332. Springer, Cham. https://doi.org/10.1007/978-3-031-05887-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05887-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05886-8

  • Online ISBN: 978-3-031-05887-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics