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Erschienen in: Technology, Knowledge and Learning 1/2018

24.11.2017 | Original research

Facilitating Student Success in Introductory Chemistry with Feedback in an Online Platform

verfasst von: Sam Van Horne, Maura Curran, Anna Smith, John VanBuren, David Zahrieh, Russell Larsen, Ross Miller

Erschienen in: Technology, Knowledge and Learning | Ausgabe 1/2018

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Abstract

Instructional technologists and faculty in post-secondary institutions have increasingly adopted learning analytics interventions such as dashboards that provide real-time feedback to students to support student’ ability to regulate their learning. But analyses of the effectiveness of such interventions can be confounded by measures of students’ prior learning as well as their baseline level of self-regulated learning. For this research study, we sought to examine whether the frequency of accessing a dashboard was associated with learning outcomes after matching subjects on confounding variables. And because prior research has suggested that measures of prior learning are associated with students’ likelihood to use learning analytics interventions, we sought to adequately control for learners’ likelihood to access the feedback by using a propensity score matching with a non-binary treatment variable. We administered the Motivated Strategies for Learning Questionnaire and also collected demographic information for a propensity score matching process. Users’ frequency of accessing the intervention was categorized as High, Moderate, or Low/No usage. After matching users on characteristics associated with dashboard usage (gender, high school GPA, and the “Test Anxiety” and “Self Efficacy” factors) we found that both the “High” and “Moderate” users achieved significantly higher course grades than the “Low/No” users. The results suggest learners benefited from regularly accessing the feedback, but extreme amounts of usage were not necessary to achieve a positive effect. We discuss the implications for recommending how students use learning analytics interventions without excessively accessing feedback.

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Literatur
Zurück zum Zitat Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2013.05.031. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior. https://​doi.​org/​10.​1016/​j.​chb.​2013.​05.​031.
Zurück zum Zitat Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics. https://doi.org/10.1002/pst.433. Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics. https://​doi.​org/​10.​1002/​pst.​433.
Zurück zum Zitat Bentler, P. M., & Bonett, D. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–600.CrossRef Bentler, P. M., & Bonett, D. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–600.CrossRef
Zurück zum Zitat Bryer, J. M. (2013). TriMatch: An R package for propensity score matching of non-binary treatments. In The R user conference, useR! 2013 July 10–12, 2013 University of Castilla-La Mancha, Albacete, Spain (Vol. 10, No. 30, p. 34). Bryer, J. M. (2013). TriMatch: An R package for propensity score matching of non-binary treatments. In The R user conference, useR! 2013 July 1012, 2013 University of Castilla-La Mancha, Albacete, Spain (Vol. 10, No. 30, p. 34).
Zurück zum Zitat Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.CrossRef Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.CrossRef
Zurück zum Zitat Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57. Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review42(4), 40–57.
Zurück zum Zitat Charleer, S., Klerkx, J., & Duval, E. (2014). Learning dashboards. Journal of Learning Analytics, 1(3), 199–202.CrossRef Charleer, S., Klerkx, J., & Duval, E. (2014). Learning dashboards. Journal of Learning Analytics, 1(3), 199–202.CrossRef
Zurück zum Zitat Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. In Extended abstracts on human factors in computing systems (pp. 869–884). ACM. Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. In Extended abstracts on human factors in computing systems (pp. 869–884). ACM.
Zurück zum Zitat Griffin, T. D., Wiley, J., & Salas, C. R. (2013). Supporting effective self-regulated learning: The critical role of monitoring. In International handbook of metacognition and learning technologies (pp. 19–34). New York: Springer. Griffin, T. D., Wiley, J., & Salas, C. R. (2013). Supporting effective self-regulated learning: The critical role of monitoring. In International handbook of metacognition and learning technologies (pp. 19–34). New York: Springer.
Zurück zum Zitat Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education, 27, 54–63.CrossRef Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education, 27, 54–63.CrossRef
Zurück zum Zitat Larusson, J. A., & White, B. (2014). Learning analytics. Berlin: Springer.CrossRef Larusson, J. A., & White, B. (2014). Learning analytics. Berlin: Springer.CrossRef
Zurück zum Zitat Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.CrossRef Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.CrossRef
Zurück zum Zitat Pintrich, P. R., & Zusho, A. (2002). The development of academic self-regulation: The role of cognitive and motivational factors. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation: A volume in the educational psychology series (pp. 249–284). San Diego, CA: Academic Press.CrossRef Pintrich, P. R., & Zusho, A. (2002). The development of academic self-regulation: The role of cognitive and motivational factors. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation: A volume in the educational psychology series (pp. 249–284). San Diego, CA: Academic Press.CrossRef
Zurück zum Zitat Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.CrossRef Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.CrossRef
Zurück zum Zitat Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. Metacognition in Educational Theory and Practice, 93, 27–30. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. Metacognition in Educational Theory and Practice, 93, 27–30.
Zurück zum Zitat Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidne (Eds.), Handbook of self-regulation (pp. 531–566). Orlando, FL: Academic Press.CrossRef Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidne (Eds.), Handbook of self-regulation (pp. 531–566). Orlando, FL: Academic Press.CrossRef
Zurück zum Zitat Wise, A. F. (2014). 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). ACM. Wise, A. F. (2014). 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). ACM.
Metadaten
Titel
Facilitating Student Success in Introductory Chemistry with Feedback in an Online Platform
verfasst von
Sam Van Horne
Maura Curran
Anna Smith
John VanBuren
David Zahrieh
Russell Larsen
Ross Miller
Publikationsdatum
24.11.2017
Verlag
Springer Netherlands
Erschienen in
Technology, Knowledge and Learning / Ausgabe 1/2018
Print ISSN: 2211-1662
Elektronische ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-017-9341-0

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