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Tracing Undergraduate Science Learners’ Digital Cognitive Strategy Use and Relation to Performance

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Abstract

Digital environments like learning management systems can afford opportunities for students to engage in cognitive learning strategies including preparatory reading of advance organizers including lecture outlines and self-testing using ungraded quizzes. When timed appropriately, self-testing can afford distributed practice, an optimal approach to self-testing that confers additional benefits. At a large, public university in the southwestern USA, we examined the frequency and timing of digital learning behaviors that reflect these practices in a large gateway science course and how these event types predicted exam performance of 220 undergraduates’ exam grades in the first unit of a 16-week anatomy and physiology course. Coursework over this 31-day span included lessons on cytology, histology, the integumentary system, and osteology; we observed the timing and frequency of students’ use of the lecture outline, ungraded self-testing quizzes, and hypothesized that those who self-regulated by downloading advance organizers before lecture (i.e., pre-reading) and utilizing quizzes to self-test (i.e., retrieval practice) and distributed this practice would achieve superior performances. Whereas students massed self-testing prior to the exam, a regression model that also included pre-reading, self-testing, and its distribution predicted achievement over and above massed practice. In authentic contexts, students used digital resources and benefitted from early lecture access or pre-reading advance organizers, and self-testing despite challenges to distribute practice and to self-test frequently and on recommended schedules.

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References

  • Adesope, O. O., Trevisan, D. A., & Sundararajan, N. (2017). Rethinking the use of tests: A meta-analysis of practice testing. Review of Educational Research, 87(3), 659–701. https://doi.org/10.3102/0034654316689306

    Article  Google Scholar 

  • Ajideh, P. (2003). Schema theory-based pre-reading tasks: A neglected essential in the Esl reading class. The Reading Matrix, 3(1), 15.

    Google Scholar 

  • Andrade, H. L. (2010). Students as the definitive source of formative assessment: Academic self-assessment and the self-regulation of learning. In H. J. Andrade, & G. J. Cizek (Eds.), Handbook of formative assessment  (pp. 90–105). New York, NY: Routledge.

  • Arnold, K. M., & McDermott, K. B. (2013). Test-potentiated learning: Distinguishing between direct and indirect effects of tests. Journal of Experimental Psychology. Learning, Memory, and Cognition, 39(3), 940–945. https://doi.org/10.1037/a0029199

  • Bae, C. L., Therriault, D. J., & Redifer, J. L. (2019). Investigating the testing effect: Retrieval as a characteristic of effective study strategies. Learning and Instruction, 60, 206–214. https://doi.org/10.1016/j.learninstruc.2017.12.008

    Article  Google Scholar 

  • Bannert, M., Molenaar, I., Azevedo, R., Järvelä, S., & Gašević, D. (2017). Relevance of learning analytics to measure and support students’ learning in adaptive educational technologies. Proceedings of the Seventh International Learning Analytics & Knowledge Conference. https://doi.org/10.1145/3027385.3029463

    Article  Google Scholar 

  • Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. The International Review of Research in Open and Distributed Learning, 11(1), 61. https://doi.org/10.19173/irrodl.v11i1.769

  • Bassett, K., Olbricht, G. R., & Shannon, K. B. (2020). Student preclass preparation by both reading the textbook and watching videos online improves exam performance in a partially flipped course. CBE Life Sciences Education, 19(3), ar32. https://doi.org/10.1187/cbe.19-05-0094

  • Ben-Eliyahu, A., & Bernacki, M. L. (2015). Context, contingency, and dynamic relations in self-regulated learning. Metacognition & Learning, 10(1), 1–13. https://doi.org/10.1007/s11409-015-9134-6

    Article  Google Scholar 

  • Bernacki, M. L. (2018). Examining the cyclical, loosely sequenced, and contingent features of self-regulated learning: Trace data and their analysis. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulated learning and performance (pp. 370–387). Routledge.

    Google Scholar 

  • Bernacki, M. L., Aleven, V., & Nokes-Malach, T. J. (2015). An examination of self-efficacy during a learning episode: initial levels, changes and associations with learning. Metacognition & Learning, 10(1), 99–117. https://doi.org/10.1007/s11409-014-9127-x

    Article  Google Scholar 

  • Bernacki, M. L., Chavez, M. M., & Uesbeck, P. M. (2020a). Predicting STEM achievement with learning management system data: prediction modeling and a test of an early warning system. Computers & Education, 158. https://doi.org/10.1016/j.compedu.2020.103999

  • Bernacki, M. L., Vosicka, L., & Utz, J. C. (2020b). Can brief, web-delivered training help STEM undergraduates “learn to learn”? Journal of Educational Psychology, 112(4), 765–781. https://doi.org/10.1037/edu0000405

    Article  Google Scholar 

  • Bernacki, M. L., Vosicka, L., Utz, J. C., & Warren, C. (2021). Effects of digital learning skill training on the academic performance of undergraduates in science and mathematics. Journal of Educational Psychology, 113(6), 1107–1125. https://doi.org/10.1037/edu0000485

    Article  Google Scholar 

  • Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

    Article  Google Scholar 

  • Brown, G., & Harris, L. (2013). Student self-assessment. In SAGE handbook of research on classroom assessment (pp. 367–393). SAGE.

  • Carpenter, S. K. (2009). Cue strength as a moderator of the testing effect: The benefits of elaborative retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(6), 1563–1569. https://doi.org/10.1037/a0017021

    Article  Google Scholar 

  • Carpenter, S. K. (2011). Semantic information activated during retrieval contributes to later retention: Support for the mediator effectiveness hypothesis of the testing effect. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(6), 1547–1552. https://doi.org/10.1037/a0024140

    Article  Google Scholar 

  • Carpenter, S. K., & DeLosh, E. L. (2005). Application of the testing and spacing effects to name learning. Applied Cognitive Psychology, 19(5), 619–636. https://doi.org/10.1002/acp.1101

    Article  Google Scholar 

  • Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. https://doi.org/10.1037/0033-2909.132.3.354

    Article  Google Scholar 

  • Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, 19(11), 1095–1102.

    Article  Google Scholar 

  • Chung, E. -K., Nam, K. -I., Oh, S. -A., Han, E. -R., Woo, Y. -J., & Hitchcock, M. A. (2013). Advance organizers in a gross anatomy dissection course and their effects on academic achievement. Clinical Anatomy, 26(3), 327–332. https://doi.org/10.1002/ca.22089

    Article  Google Scholar 

  • Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823

    Article  Google Scholar 

  • Cho, K. W., & Powers, A. (2019). Testing enhances both memorization and conceptual learning of categorical materials. Journal of Applied Research in Memory and Cognition. https://doi.org/10.1016/j.jarmac.2019.01.003

    Article  Google Scholar 

  • Cogliano, M. C., Bernacki, M. L., Hilpert, J. C., & Strong,. (2022). A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students. Journal of Educational Psychology, 114(8), 1801–1816. https://doi.org/10.1037/edu0000745

    Article  Google Scholar 

  • Cook, E., Kennedy, E., & McGuire, S. Y. (2013). Effect of teaching metacognitive learning strategies on performance in general chemistry courses. Journal of Chemical Education, 90(8), 961–967. https://doi.org/10.1021/ed300686h

    Article  Google Scholar 

  • Crippen, K. J., Schraw, G., & Brooks, D. W. (2005). Using an interactive, compensatory model of learning to improve chemistry teaching. Journal of Chemical Education, 82(4), 637. https://doi.org/10.1021/ed082p637

    Article  Google Scholar 

  • Cutrer, W. B., Castro, D., Roy, K. M., & Turner, T. L. (2011). Use of an expert concept map as an advance organizer to improve understanding of respiratory failure. Medical Teacher, 33(12), 1018–1026. https://doi.org/10.3109/0142159X.2010.531159

    Article  Google Scholar 

  • Davis, D., Chen, G., Hauff, C., & Houben, G. -J. (2016). Gauging MOOC learners’ adherence to the designed learning path. Proceedings of the 9th International Conference on Educational Data Mining.

  • Derr, K., Hübl, R., & Ahmed, M. Z. (2018). Prior knowledge in mathematics and study success in engineering: Informational value of learner data collected from a web-based pre-course. European Journal of Engineering Education, 43(6), 911–926. https://doi.org/10.1080/03043797.2018.1462765

    Article  Google Scholar 

  • Dörrenbächer, L., & Perels, F. (2016). Self-regulated learning profiles in college students: Their relationship to achievement, personality, and the effectiveness of an intervention to foster self-regulated learning. Learning and Individual Differences, 51, 229–241. https://doi.org/10.1016/j.lindif.2016.09.015

    Article  Google Scholar 

  • Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266

    Article  Google Scholar 

  • Eddy, S. L., & Hogan, K. A. (2014). Getting under the hood: How and for whom does increasing course structure work? CBE Life Sciences Education, 13(3), 453–468. https://doi.org/10.1187/cbe.14-03-0050

    Article  Google Scholar 

  • Elfeky, A. I. M., Masadeh, T. S. Y., & Elbyaly, M. Y. H. (2020). Advance organizers in flipped classroom via e-learning management system and the promotion of integrated science process skills. Thinking Skills and Creativity, 35, 100622. https://doi.org/10.1016/j.tsc.2019.100622

    Article  Google Scholar 

  • Gerbier, E., & Koenig, O. (2012). Influence of multiple-day temporal distribution of repetitions on memory: A comparison of uniform, expanding, and contracting schedules. Quarterly Journal of Experimental Psychology, 65(3), 514–525. https://doi.org/10.1080/17470218.2011.600806

    Article  Google Scholar 

  • Gerbier, E., Toppino, T. C., & Koenig, O. (2015). Optimising retention through multiple study opportunities over days: The benefit of an expanding schedule of repetitions. Memory, 23(6), 943–954. https://doi.org/10.1080/09658211.2014.944916

    Article  Google Scholar 

  • Gidena, A., & Gebeyehu, D. (2017). The effectiveness of advance organiser model on students’ academic achievement in learning work and energy. International Journal of Science Education, 39(16), 2226–2242. https://doi.org/10.1080/09500693.2017.1369600

    Article  Google Scholar 

  • Goda, Y., Yamada, M., Kato, H., Matsuda, T., Saito, Y., & Miyagawa, H. (2015). Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes. Learning and Individual Differences, 37, 72–80. https://doi.org/10.1016/j.lindif.2014.11.001

    Article  Google Scholar 

  • Green, K. (2019). Campus computing project. Retrieved from https://www.campuscomputing.net/

  • Greene, J. A., & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45(4), 203–209.

    Article  Google Scholar 

  • Greene, J. A., Plumley, R. D., Urban, C. J., Bernacki, M. L., Gates, K. M., Hogan, K. A., ... & Panter, A. T. (2021). Modeling temporal self-regulatory processing in a higher education biology course. Learning and Instruction, 72, 101201.

  • Gurlitt, J., Dummel, S., Schuster, S., & Nückles, M. (2012). Differently structured advance organizers lead to different initial schemata and learning outcomes. Instructional Science, 40(2), 351–369. https://doi.org/10.1007/s11251-011-9180-7

    Article  Google Scholar 

  • Hartwig, M. K., & Dunlosky, J. (2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review, 19(1), 126–134. https://doi.org/10.3758/s13423-011-0181-y

    Article  Google Scholar 

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

    Article  Google Scholar 

  • Heiner, C. E., Banet, A. I., & Wieman, C. (2014). Preparing students for class: How to get 80% of students reading the textbook before class. American Journal of Physics, 82(10), 989–996. https://doi.org/10.1119/1.4895008

    Article  Google Scholar 

  • Hintzman, D., Summers, J., & Block, R. (1975). Spacing judgments as an index of study-phase retrieval. Journal of Experimental Psychology: Human Learning and Memory, 1, 31–40. https://doi.org/10.1037/0278-7393.1.1.31

    Article  Google Scholar 

  • Hopkins, R. F., Lyle, K. B., Hieb, J. L., & Ralston, P. A. (2016). Spaced retrieval practice increases college students’ short- and long-term retention of mathematics knowledge. Educational Psychology Review, 28(4), 853–873. https://doi.org/10.1007/s10648-015-9349-8

    Article  Google Scholar 

  • Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12–19. https://doi.org/10.1177/2372732215624708

    Article  Google Scholar 

  • Kang, S. H. K., Lindsey, R. V., Mozer, M. C., & Pashler, H. (2014). Retrieval practice over the long term: Should spacing be expanding or equal-interval? Psychonomic Bulletin & Review, 21(6), 1544–1550. https://doi.org/10.3758/s13423-014-0636-z

    Article  Google Scholar 

  • Karpicke, J. D., & Bauernschmidt, A. (2011). Spaced retrieval: Absolute spacing enhances learning regardless of relative spacing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(5), 1250–1257.

    Google Scholar 

  • Karpicke, J. D., Butler, A. C., & Roediger III, H. L. R. (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory, 17(4), 471–479. https://doi.org/10.1080/09658210802647009

    Article  Google Scholar 

  • Karpicke, J. D., & Roediger, H. L. (2010). Is expanding retrieval a superior method for learning text materials? Memory & Cognition, 38(1), 116–124. https://doi.org/10.3758/MC.38.1.116

    Article  Google Scholar 

  • Karpicke, J. D., & Roediger, H. L. I. (2007). Expanding retrieval practice promotes short-term retention, but equally spaced retrieval enhances long-term retention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(4), 704–719. https://doi.org/10.1037/0278-7393.33.4.704

    Article  Google Scholar 

  • Kitsantas, A. (2002). Test preparation and performance: A self-regulatory analysis. The Journal of Experimental Education, 70(2), 101–113.

    Article  Google Scholar 

  • Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK ’13, 170. https://doi.org/10.1145/2460296.2460330

  • Konrad, M., Joseph, L. M., & Itoi, M. (2011). Using guided notes to enhance instruction for all students. Intervention in School and Clinic, 46(3), 131–140. https://doi.org/10.1177/1053451210378163

    Article  Google Scholar 

  • Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297–1317. https://doi.org/10.1002/acp.1537

    Article  Google Scholar 

  • Korur, F., Toker, S., & Eryilmaz, A. (2016). Effects of the integrated online advance organizer teaching materials on students’ science achievement and attitude. Journal of Science Education and Technology, 25(4), 628–640.

    Article  Google Scholar 

  • Koscianski, A., Ribeiro, R. J., & da Silva, S. C. R. (2012). Short animation movies as advance organizers in physics teaching: A preliminary study. Research in Science & Technological Education, 30(3), 255–269. https://doi.org/10.1080/02635143.2012.732057

    Article  Google Scholar 

  • Küpper-Tetzel, C. E., Kapler, I. V., & Wiseheart, M. (2014). Contracting, equal, and expanding learning schedules: The optimal distribution of learning sessions depends on retention interval. Memory & Cognition, 42(5), 729–741. https://doi.org/10.3758/s13421-014-0394-1

    Article  Google Scholar 

  • Li, C.-H., Wu, M.-H., & Lin, W. -L. (2019). The use of a “think-pair-share” brainstorming advance organizer to prepare learners to listen in the L2 classroom. International Journal of Listening, 33(2), 114–127. https://doi.org/10.1080/10904018.2017.1394193

    Article  Google Scholar 

  • Lieu, R., Wong, A., Asefirad, A., & Shaffer, J. F. (2017). Improving exam performance in introductory biology through the use of preclass reading guides. CBE Life Sciences Education, 16(3), ar46. https://doi.org/10.1187/cbe.16-11-0320

  • Lin, C., McDaniel, M. A., & Miyatsu, T. (2018). Effects of flashcards on learning authentic materials: The role of detailed versus conceptual flashcards and individual differences in structure-building ability. Journal of Applied Research in Memory and Cognition, 7(4), 529–539. https://doi.org/10.1016/j.jarmac.2018.05.003

    Article  Google Scholar 

  • Liu, W. C., Wang, C. K. J., Kee, Y. H., Koh, C., Lim, B. S. C., & Chua, L. (2014). College students’ motivation and learning strategies profiles and academic achievement: A self-determination theory approach. Educational Psychology, 34(3), 338–353. https://doi.org/10.1080/01443410.2013.785067

    Article  Google Scholar 

  • Lombardi, D., Shipley, T. F., Bailey, J. M., Bretones, P. S., Prather, E. E., Ballen, C. J., Knight, J. K., Smith, M. K., Stowe, R. L., Cooper, M. M., Prince, M., Atit, K., Uttal, D. H., LaDue, N. D., McNeal, P. M., Ryker, K., & St. John, K., van der Hoeven Kraft, K. J., & Docktor, J. L. (2021). The curious construct of active learning. Psychological Science in the Public Interest, 22(1), 8–43. https://doi.org/10.1177/1529100620973974

    Article  Google Scholar 

  • Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80, 179–196. https://doi.org/10.1016/j.chb.2017.11.011

    Article  Google Scholar 

  • McIntyre, S. H., & Munson, J. M. (2008). Exploring cramming: Student behaviors, beliefs, and learning retention in the principles of marketing course. Journal of Marketing Education, 30(3), 226–243. https://doi.org/10.1177/0273475308321819

    Article  Google Scholar 

  • Mouri, K., Uosaki, N., Hasnine, M., Shimada, A., Yin, C., Kaneko, K., & Ogata, H. (2021). An automatic quiz generation system utilizing digital textbook logs. Interactive Learning Environments, 29(5), 743–756. https://doi.org/10.1080/10494820.2019.1620291

    Article  Google Scholar 

  • Morehead, K., Rhodes, M. G., & DeLozier, S. (2016). Instructor and student knowledge of study strategies. Memory, 24(2), 257–271. https://doi.org/10.1080/09658211.2014.1001992

    Article  Google Scholar 

  • Nakiboğlu, C., & Nakiboğlu, N. (2021). Views of prospective chemistry teachers on the use of graphic organizers supported with interactive PowerPoint presentation technology in teaching electrochemistry concepts. International Journal of Physics & Chemistry Education, 13(3), 47–63. https://doi.org/10.51724/ijpce.v13i3.216

  • Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

    Article  Google Scholar 

  • Nisyah, M., Gunawan, G., Harjono, A., & Kusdiastuti, M. (2020). Inquiry learning model with advance organizers to improve students’ understanding on physics concepts. Journal of Physics: Conference Series, 1521(2), 022057. https://doi.org/10.1088/1742-6596/1521/2/022057

    Article  Google Scholar 

  • Olson, S., & Riordan, D. G. (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the President. Executive Office of the President.

  • Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2017.00422

    Article  Google Scholar 

  • Panadero, E., & Alonso-Tapia, J. (2013). Autoevaluación: Connotaciones Teóricas y Prácticas. Cuándo Ocurre, Cómo se Adquiere y qué Hacer para Potenciarla en nuestro Alumnado. Electronic Journal of Research in Education Psychology, 11(30), 551–576. https://doi.org/10.14204/ejrep.30.12200

  • Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49–64.

    Google Scholar 

  • Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106(1), 315–329. https://doi.org/10.1037/a0034027

    Article  Google Scholar 

  • Persky, A. M. (2018). A four year longitudinal study of student learning strategies. Currents in Pharmacy Teaching and Learning, 10(11), 1496–1500. https://doi.org/10.1016/j.cptl.2018.08.012

    Article  Google Scholar 

  • Peverly, S. T., Brobst, K. E., Graham, M., & Shaw, R. (2003). College adults are not good at self-regulation: A study on the relationship of self-regulation, note taking, and test taking. Journal of Educational Psychology, 95(2), 335–346. https://doi.org/10.1037/0022-0663.95.2.335

    Article  Google Scholar 

  • Ponce, H. R., Mayer, R. E., López, M. J., & Loyola, M. S. (2018). Adding interactive graphic organizers to a whole-class slideshow lesson. Instructional Science, 46(6), 973–988. https://doi.org/10.1007/s11251-018-9465-1

    Article  Google Scholar 

  • Popova, A., Kirschner, P. A., & Joiner, R. (2014). Effects of primer podcasts on stimulating learning from lectures: How do students engage? British Journal of Educational Technology, 45(2), 330–339. https://doi.org/10.1111/bjet.12023

    Article  Google Scholar 

  • Pyc, M. A., & Rawson, K. A. (2007). Examining the efficiency of schedules of distributed retrieval practice. Memory & Cognition, 35(8), 1917–1927. https://doi.org/10.3758/BF03192925

    Article  Google Scholar 

  • Pyc, M. A., & Rawson, K. A. (2012). Why is test–restudy practice beneficial for memory? An evaluation of the mediator shift hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(3), 737–746. https://doi.org/10.1037/a0026166

    Article  Google Scholar 

  • Rank, M. A., Volcheck, G. W., Swagger, T., & Cook, D. A. (2012). Pretests or advance organizers for Web-based allergy-immunology medical education? A randomized controlled trial. Allergy and Asthma Proceedings, 33(2), 191–196. https://doi.org/10.2500/aap.2012.33.3511

    Article  Google Scholar 

  • Rawson, K. A. (2012). Why do rereading lag effects depend on test delay? Journal of Memory and Language, 66(4), 870–884. https://doi.org/10.1016/j.jml.2012.03.004

    Article  Google Scholar 

  • Rawson, K. A., & Dunlosky, J. (2011). Optimizing schedules of retrieval practice for durable and efficient learning: How much is enough? Journal of Experimental Psychology: General, 140(3), 283–302. https://doi.org/10.1037/a0023956

    Article  Google Scholar 

  • Rawson, K. A., Dunlosky, J., & Sciartelli, S. M. (2013). The power of successive relearning: Improving performance on course exams and long-term retention. Educational Psychology Review, 25(4), 523–548. http://dx.doi.org.ezproxy.library.unlv.edu/10.1007/s10648-013-9240-4

  • Rodriguez, F., Kataoka, S., Janet Rivas, M., Kadandale, P., Nili, A., & Warschauer, M. (2021). Do spacing and self-testing predict learning outcomes? Active Learning in Higher Education, 22(1), 77–91. https://doi.org/10.1177/1469787418774185

    Article  Google Scholar 

  • Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27. https://doi.org/10.1016/j.tics.2010.09.003

    Article  Google Scholar 

  • Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1(3), 181–210. https://doi.org/10.1111/j.1745-6916.2006.00012.x

    Article  Google Scholar 

  • Rohrer, D. (2009). The effects of spacing and mixing practice problems. Journal for Research in Mathematics Education, 40(1), 4–17.

    Article  Google Scholar 

  • Rovers, S. F. E., Stalmeijer, R. E., van Merriënboer, J. J. G., Savelberg, H. H. C. M., & de Bruin, A. B. H. (2018). How and why do students use learning strategies? A mixed methods study on learning strategies and desirable difficulties with effective strategy users. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2018.02501

    Article  Google Scholar 

  • Rowland, C. A. (2014). The effect of testing versus restudy on retention: A meta-analytic review of the testing effect. Psychological Bulletin, 140(6), 1432–1463. https://doi.org/10.1037/a0037559

    Article  Google Scholar 

  • Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31(1), 64–96. https://doi.org/10.1016/j.cedpsych.2005.02.002

    Article  Google Scholar 

  • Schunk, D. H., & Greene, J. A. (2017). Historical, contemporary, and future perspectives on self-regulated learning and performance. In Handbook of self-regulation of learning and performance (pp. 1–15). Routledge.

  • Susser, J. A., & McCabe, J. (2013). From the lab to the dorm room: Metacognitive awareness and use of spaced study. Instructional Science, 41(2), 345–363. https://doi.org/10.1007/s11251-012-9231-8

    Article  Google Scholar 

  • Theobald, E. (2018). Students are rarely independent: When, why, and how to use random effects in discipline-based education research. CBE—Life Sciences Education, 17(3), rm2. https://doi.org/10.1187/cbe.17-12-0280

  • Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Nicole Arroyo, E., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., & Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences of the United States of America, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117

    Article  Google Scholar 

  • Toppino, T. C. (2018). Level of initial training moderates the effects of distributing practice over multiple days with expanding, contracting, and uniform schedules: Evidence for study-phase retrieval. 10.

  • Utz, J. C., & Bernacki, M. L. (2018). Voluntary web-based self-assessment quiz use is associated with improved exam performance, especially for learners with low prior knowledge. HAPS Educator, 22(2), 129–135. Retrieved April 29, 2022, from https://doi.org/10.1016/j.compedu.2020.103999

  • Valle, A., Núñez, J. C., Cabanach, R. G., González-Pienda, J. A., Rodríguez, S., Rosário, P., Cerezo, R., & Muñoz-Cadavid, M. A. (2008). Self-regulated profiles and academic achievement. 9.

  • Vaughn, K. E., & Rawson, K. A. (2011). Diagnosing criterion-level effects on memory: What aspects of memory are enhanced by repeated retrieval? Psychological Science, 22(9), 1127–1131. https://doi.org/10.1177/0956797611417724

    Article  Google Scholar 

  • Vogel-Walcutt, J. J. (2013). Using a video game as an advance organizer: Effects on development of procedural and conceptual knowledge, cognitive load, and casual adoption. 9(3), 18.

  • Wang, Q., Mousavi, A., & Lu, C. (2022). A scoping review of empirical studies on theory-driven learning analytics. Distance Education, 43(1), 6–29. https://doi.org/10.1080/01587919.2021.2020621

    Article  Google Scholar 

  • Winne, P. H. (2017). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. Wise, & D. Gasevic (Eds.), Handbook of learning analytics (pp. 241–249). Society for Learning Analytics and Research. https://doi.org/10.18608/hla17.021

  • Winne, P. H. (2018). Cognition and metacognition within self-regulated learning. In D. H. Schunk, & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 36–48). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315697048-3

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 227–304). Erlbaum.

    Google Scholar 

  • Wissman, K. T., Rawson, K. A., & Pyc, M. A. (2012). How and when do students use flashcards? Memory, 20(6), 568–579. https://doi.org/10.1080/09658211.2012.687052

    Article  Google Scholar 

  • Zhao, N., Wardeska, J., McGuire, S., & Cook, E. (2014). Metacognition: An effective tool to promote success in college science learning. Journal of College Science Teaching, 043(04). https://doi.org/10.2505/4/jcst14_043_04_48

  • Zhu, Y., Au, W., & Yates, G. (2016). University students’ self-control and self-regulated learning in a blended course. The Internet and Higher Education, 30, 54–62. https://doi.org/10.1016/j.iheduc.2016.04.001

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

  • Zimmerman, B. J., & Pons, M. M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23(4), 614–628. https://doi.org/10.2307/1163093

    Article  Google Scholar 

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant DRL 1420491. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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    Kyle Castro Mefferd

  2. University of North Carolina, Chapel Hill, USA

    Matthew L. Bernacki

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Mefferd, K.C., Bernacki, M.L. Tracing Undergraduate Science Learners’ Digital Cognitive Strategy Use and Relation to Performance. J Sci Educ Technol 32, 837–857 (2023). https://doi.org/10.1007/s10956-022-10018-9

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