i-manager Publications

Student Interactions with Prerequisite Knowledge Tools: How Students’ Daily-Usage Patterns Can Inform Pedagogy in Calculus I

James Folkestad *, Mary E. Pilgrim **, Ben Sencindiver ***, Priya Harindranathan ****

* School of Education, Colorado State University, USA.

** Department of Mathematics and Statistics, San Diego State University, USA.

*** Department of Mathematics at Colorado State University, USA.

**** Center for the Analytics of Learning and Teaching (C-ALT), Colorado State University, USA.

Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jet.16.2.15799

Abstract

Many factors play a role in a students' learning experience, but students' course interaction behaviors are particularly important toward fostering success. Instructors build learning tools (such as videos, online quizzes, etc.) that provide students with the opportunity to extend their learning outside the classroom. These tools require students to self-regulate their learning behaviors, taking initiative to incorporate them into their study routine. However, measuring how students actually use the tools is a challenge. In this paper, online tools were designed around Precalculus content and tested in the first two weeks of a introductory calculus course. Our intent was to identify students who may or may not be engaged in behaviors associated with self-regulation by collecting data on student interaction with online tools. If at-risk students could be identified early in a semester, then it might be possible to intervene in order to change engagement and behaviors. Data was partitioned into behavior-based clusters and interpreted based on course outcomes. This paper discuss about the cluster-based findings in relation to student performance measures and student self-assessments of self-efficacy for learning and performance in the course. This paper concludes with a discussion of how findings may inform pedagogical choices and future study.

Keywords

Behavioral Data, Online Tools, Learning Analytics, Calculus, Pedagogy

How to Cite this Article?

Folkestad, J. E., Pilgrim, M. E., Sencindiver, B., and Harindranathan, P. (2019). Student Interactions with Prerequisite Knowledge Tools: How Students’ Daily-Usage Patterns Can Inform Pedagogy in Calculus I. i-manager’s Journal of Educational Technology, 16(2), 14-34. https://doi.org/10.26634/jet.16.2.15799

References

[1]. 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.

[2]. Alhadad, S. S. (2018). Visualizing data to support judgement, inference, and decision making in learning analytics: insights from cognitive psychology and visualization science. Journal of Learning Analytics, 5(2), 60-85.

[3]. Ahmed, W., van der Werf, G., Kuyper, H., & Minnaert, A. (2013). Emotions, self-regulated learning, and achievement in mathematics: A growth curve analysis. Journal of Educational Psychology, 105(1), 150-161.

[4]. Agustin, M. & Agustin, M. (2009). Algebra and precalculus skills and performance in first-semester calculus. International Journal of Case Method Research & Application, 21(3), 232-236.

[5]. Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students' self-regulated learning. Metacognition and Learning, 9(2), 161-185.

[6]. Bressoud, D. M., Carlson, M. P., Mesa, V., & Rasmussen, C. (2013). The calculus student: Insights from the Mathematical Association of America national study. International Journal of Mathematical Education in Science and Technology, 44 (5), 685-698.

[7]. 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.

[8]. Brooks, C., Erickson, G., Greer, J., & Gutwin, C. (2014). Modelling and quantifying the behaviours of students in lecture capture environments. Computers & Education, 75, 282-292.

[9]. Caprara, G. V., Fida, R., Vecchione, M., Del Bove, G., Vecchio, G. M., Barbaranelli, C., & Bandura, A. (2008). Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement. Journal of Educational Psychology, 100(3), 525-534.

[10]. 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.

[11]. Chiu, T. K., & Churchill, D. (2015). Exploring the characteristics of an optimal design of digital materials for concept learning in mathematics: Multimedia learning and variation theory. Computers & Education, 82, 280- 291.

[12]. Cleary, T. J., Callan, G. L., Malatesta, J., & Adams, T. (2015). Examining the level of convergence among self-regulated learning micro analytic processes, achievement, and a self-report questionnaire. Journal of Psychoeducational Assessment, 33(5), 439-450.

[13]. Colorado Department of Higher Education. (2018).

[14]. 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.

[15]. Echeverria, V., Martinez-Maldonado, R., & Shum, S.B. (2019, April). Towards collaboration translucence: giving meaning to multimodal group data. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 39). ACM.

[16]. Ellis, J., Fosdick, B. K., & Rasmussen, C. (2016). Women 1.5 times more likely to leave STEM pipeline after calculus compared to men: Lack of mathematical confidence a potential culprit. PloS One, 11(7).

[17]. Ellis, J., Rasmussen, C., & Duncan, K. (2013, February). Switcher and persister experiences in Calculus 1. In Sixteenth Annual Conference on Research in Undergraduate Mathematics Education. Denver, CO.

[18]. Fincham, O. E., Gasevic, D. V., Jovanovic, J. M., & Pardo, A. (2018). From study tactics to learning strategies: an analytical method for extracting interpretable representations. IEEE Transactions on Learning Technologies.

[19]. Folkestad, J., & Burns, S. (2014). Predicting D, F, and withdrawals (Ws) from Calculus I at Colorado State University (Unpublished manuscript), Colorado State University.

[20]. Gillett, A., Hammond, A., & Martala, M. (2013). Inside track to successful academic writing. Pearson UK.

[21]. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100- 108.

[22]. 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.

[23]. Johns, C. (2018). Self-Regulation in Calculus I. In A. Weinberg, C. Rasmussen, J. Rabin, M. Wawro, & S. Brown (Eds.), Proceedings of the 21^stAnnual Conference on Research in Undergraduate Mathematics Education. (pp. 16-30). San Diego, CA.

[24]. Kabacoff, R. I. (2010). R in action. Shelter Island, New York: Manning Publications.

[25]. 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.

[26]. Karpicke, J. D., Butler, A. C., & Roediger III, H. L. (2009). Metacognitive strategies in student learning: do students practise retrieval when they study on their own? Memory, 17(4), 471-479.

[27]. Karpicke, J. D., & Roediger III, H. L. (2007). Expanding retrieval practice promotes short-term retention, but equally spaced retrieval enhances longterm retention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(4), 704-719.

[28]. Kinnebrew, J., Biswas, G., Sulcer, B., & Taylor, R. (2013). Investigating self-regulated learning in teachable agent environments. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 451-470). Amsterdam, the Netherlands: Springer.

[29]. Koriat, A., Bjork, R. A., Sheffer, L., & Bar, S. K. (2004). Predicting one's own forgetting: the role of experiencebased and theor y-based processes. Journal of Experimental Psychology: General, 133(4), 643-656.

[30]. Logan, J. M., & Balota, D. A. (2008). Expanded vs. equal interval spaced retrieval practice: Exploring different schedules of spacing and retention interval in younger and older adults. Aging, Neuropsychology, and Cognition, 15(3), 257-280.

[31]. Marzouk, Z., Rakovic, M., & Winne, P. H. (2016). Generating Learning Analytics to Improve Learners' Metacognitive Skills Using nStudy Trace Data and the ICAP Framework. In Proceedings of the LAK 2016 Workshop on Learning Analytics for Learners (pp. 11-16).

[32]. Mayfield, K. H., & Chase, P. N. (2002). The effects of cumulative practice on mathematics problem solving. Journal of Applied Behavior Analysis, 35(2), 105-123.

[33]. Morehead, K., Rhodes, M. G., & DeLozier, S. (2016). Instructor and student knowledge of study strategies. Memory, 24(2), 257-271.

[34]. Pardo, A., Han, F., & Ellis, R. A. (2016, April). Exploring the relation between self-regulation, online activities, and academic performance: a case study. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp.422-429). Edinburgh, UsK.

[35]. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40.

[36]. Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).

[37]. Reinholz, D. L. (2015). Peer-Assisted Reflection: A design-based intervention for improving success in calculus. International Journal of Research in Undergraduate Mathematics Education, 1(2), 234-267.

[38]. Riel, J., Lawless, K. A., & Brown, S. W. (2018). Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses. Journal of Learning Analytics, 5(1), 25-40.

[39]. Roediger III, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20-27.

[40]. Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7- 12.

[41]. Sadler, P., & Sonnert, G. (2018). The path to college calculus: The impact of high school mathematics coursework. Journal for Research in Mathematics Education, 49(3), 292-329.

[42]. Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503- 509.

[43]. Sonnenberg, C., & Bannert, M. (2015). Discovering the effects of metacognitive prompts on the sequential structure of SRL-processes using process mining techniques. Journal of Learning Analytics, 2(1), 72-100.

[44]. 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.

[45]. Winne, P. H. (2018). Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88(1), 9-20.

[46]. Winne, P. H., & Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1-8.

[47]. Winne, P. H. (2019). Enhancing self-regulated learning for information problem solving with ambient big data gathered by nStudy. Contemporary Technologies in Education (pp. 145-162). Palgrave Macmillan, Cham.

[48]. Wise, A. F. (2014, March). Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the Fourth International Conference On Learning Analytics and Knowledge (pp. 203-211). Indianapolis.

[49]. Zimmerman, B. J. (2000). Attaining self-regulation. In Boekaerts, M., Pintrich, P. R., & Zeidner, M (Eds.), Handbook of Self-Regulation (pp. 13-39). San Diego, CA: Academic Press.

[50]. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.

[51]. Zimmerman, B. J. (2008). Investigating selfregulation and motivation: historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183.

[52]. Zimmerman, B. J., & Kitsantas, A. (2014). Comparing students' self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145-155.

[53]. Zimmerman, B. J., Moylan, A., Hudesman, J., White, N., & Flugman, B. (2011). Enhancing self-reflection and mathematics achievement of at-risk urban technical college students. Psychological Test and Assessment Modeling, 53(1), 141-160.

Student Interactions with Prerequisite Knowledge Tools: How Students’ Daily-Usage Patterns Can Inform Pedagogy in Calculus I

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25