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Assessing the Interaction Between Self-Regulated Learning (SRL) Profiles and Actual Learning in the Chemistry Online Blended Learning Environment (COBLE)

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Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment

Abstract

This chapter addresses the challenges and opportunities of virtual teaching of a complex scientific topic, such as chemistry, to high-school students. Chemistry Online Blended Learning Environment (COBLE) is a learning environment for students that are willing to expand their knowledge of Chemistry but have no opportunity to do so in their schools. It is claimed that certain skills help cope with learning, in general, and are vital in advancing learning, such as Self-Regulated Learning (SRL) skills. The chapter describes a recent study that investigated and characterized the students’ learning profiles, self-regulated learning processes (skills and strategies), and followed the change in these variables throughout the 3 year program learning Chemistry via COBLE in order to predict students’ success in learning Chemistry this way. Such prediction may enable teachers to be aware of possible problems earlier than usual and also help personalize the teaching and learning processes according to students’ profiles. Results indicate that there are some significant differences in some of the SRL categories between students studying via face-to-face and virtual environments and also among intervention students that possessed different SRL profiles when examining the involvement variable throughout their studies over time. On the basis of the data, influential indicators were isolated to enable future prediction of student success in studying Chemistry in a virtual manner and better planning of personalized support.

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Notes

  1. 1.

    Attitude and interest (ATT); concentration and attention to academic tasks (CON); motivation, diligence, self-discipline, and willingness to work hard (MOT); use of support techniques and materials (STA); use of time management principles for academic tasks (TMT); test strategies and preparing for tests (TST).

  2. 2.

    Participation in the synchronic lesson (although not a voluntary action) was also considered as synchronic behavior since it is an action related to the synchronic lesson, but since there were no differences in the SEM when absent, the existence of this component is merely acknowledged.

  3. 3.

    Time spent at the site was also considered as a-synchronic behavior since it is an action related to the a-synchronic assignments; students could have spent more or less time at the site while performing their homework assignments or revising the learning materials. The existence of this component is merely acknowledged, since it is impossible to differentiate the actual time of student engagement or idle connectivity to the site.

References

  • Akçapınar, G. (2015). Profiling students’ approaches to learning through moodle logs. In Multidisciplinary Academic Conference on Education, Teaching and Learning (MAC-ETL 2015). Chudenicka: MAC Prague consulting Ltd.

    Google Scholar 

  • Akçapınar, G., Altun, A., & Coşgun, E. (2014). Investigating students’ interaction profile in an online learning environment with clustering. In 14th IEEE International Conference on Advanced Learning Technologies (ICALT2014), IEEE Computer Society: 7–9 July, 2014, Athens, Greece (pp. 109–111). Washington, DC: IEEE.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bergman, L. R., & El-Khouri, B. M. (1999). Studying individual patterns of development using I-states as objects analysis (ISOA). Biometrical Journal, 41(6), 753–770.

    Article  Google Scholar 

  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.

    Article  Google Scholar 

  • Castro, F., Vellido, A., Nebot, À., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In Evolution of teaching and learning paradigms in intelligent environment (pp. 183–221). Berlin: Springer.

    Chapter  Google Scholar 

  • Chen, G.-D., Liu, C.-C., Ou, K.-L., & Liu, B.-J. (2000). Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research, 23, 305.

    Article  Google Scholar 

  • Cho, M. H. (2004). The effects of design strategies for promoting students’ self-regulated learning skills on students’ self-regulation and achievements in online learning environments. Bloomington, IN: Association for Educational Communications and Technology.

    Google Scholar 

  • Landis, R. J., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.

    Article  Google Scholar 

  • Ley, K., & Young, D. B. (2001). Instructional principles for self-regulation. Educational Technology Research and Development, 49(2), 93–103.

    Article  Google Scholar 

  • Ning, H. K., & Downing, K. (2015). A latent profile analysis of University students’ self-regulated learning strategies. Studies in Higher Education, 40, 1328. https://doi.org/10.1080/03075079.2014.880832

    Article  Google Scholar 

  • O’Neill, K., Singh, G., & O’Donoghue, J. (2004). Implementing eLearning programmes for higher education: A review of the literature. Journal of Information Technology Education, 3, 313.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Preidys, S., & Sakalauskas, L. (2010). Analysis of students’ study activities in virtual learning environments using data mining methods. Ukio Technologinis ir Ekonominis Vystymas, 16(1), 94–108.

    Google Scholar 

  • R Development Core Team. (2017). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/

    Google Scholar 

  • Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368–384.

    Article  Google Scholar 

  • Rosenberg, J. M., Schmidt, J. A., & Beymer, P. N. (2017). prcr: Person-centered analysis (R package version 0.1.5.). Retrieved from https://CRAN.R-project.org/package=prcr

  • Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37–68.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265–283.

    Article  Google Scholar 

  • Schmidt, J. A., Rosenberg, J. M., & Beymer, P. (2018). A person- in-context approach to student engagement in science: Examining learning activities and choice. Journal of Research in Science Teaching, 55, 19. https://doi.org/10.1002/tea.21409

    Article  Google Scholar 

  • Sharp, C., Pocklington, K., & Weindling, D. (2002). Study support and the development of the self-regulated learner. Educational Research, 44(1), 29–41.

    Article  Google Scholar 

  • Sheard, J., Ceddia, J., Hurst, J., & Tuovinen, J. (2003). Inferring student learning behaviour from website interactions: A usage analysis. Education and Information Technologies, 3, 245–266. https://doi.org/10.1023/A:1026360026073

    Article  Google Scholar 

  • Sim, J., & Wright, C. C. (2005). The kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy, 85(3), 257–268. https://doi.org/10.1093/ptj/85.3.257

    Article  Google Scholar 

  • Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., & Cooper, S. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), 93–135.

    Article  Google Scholar 

  • Tsai, C. W. (2011). Achieving effective learning effects in the blended course: A combined approach of online self-regulated learning and collaborative learning with initiation. Cyberpsychology, Behavior, and Social Networking, 14(9), 505–510.

    Article  Google Scholar 

  • Weinstein, C. E., Palmer, D. R., & Shulte, A. C. (2002). LASSI: Learning and study strategies inventory (2nd ed.). Clearwater, FL: H&H Publishing Company, Inc..

    Google Scholar 

  • Zemsky, R., & Massy, W. F. (2004). Thwarted innovation. What happened to e-learning and why. A final report for the Weather station project of the learning alliance. Philadelphia, PA: University of Pennsylvania in Cooperation with the Thomson Corporation.

    Google Scholar 

  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339.

    Article  Google Scholar 

  • Zimmerman, B. J. (1990). self regulating learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.

    Article  Google Scholar 

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909

    Article  Google Scholar 

  • Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners: Beyond achievement to self-efficacy. Washington, DC: APA.

    Book  Google Scholar 

  • Zusho, A., & Edwards, K. (2011). Self-regulation and achievement goals in the college classroom. New Directions for Teaching and Learning, 126, 21–31.

    Article  Google Scholar 

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Acknowledgements

We thank the Meital Research Foundation and the Davidson Institute for funding. We thank Mrs. Yetty Varon whose statistical expertise was invaluable.

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Correspondence to Rachel Rosanne Eidelman .

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Eidelman, R.R., Rosenberg, J.M., Shwartz, Y. (2019). Assessing the Interaction Between Self-Regulated Learning (SRL) Profiles and Actual Learning in the Chemistry Online Blended Learning Environment (COBLE). In: Sampson, D., Spector, J.M., Ifenthaler, D., Isaías, P., Sergis, S. (eds) Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-15130-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-15130-0_12

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