Skip to main content

Advertisement

SpringerLink
Log in

Utilizing Learning Analytics to Support Students' Academic Self-efficacy and Problem-Solving Skills

  • Regular Article
  • Published:
The Asia-Pacific Education Researcher Aims and scope Submit manuscript

Abstract

The use of the flipped classroom (FC) model in higher education is becoming increasingly common. Although the FC model has many benefits, there are some limitations using this model for learners who do not have self-directed learning skills and do not have a developed learner autonomy. One of these limitations is that students with low academic self-efficacy (ASE) cannot improve their problem-solving skills (PSS). In the FC model, it is thought that these problems can be solved and supported by providing learning analytics to the learners and making recommendations and guidance based on these results. The aim of this study is to investigate the effect of recommendations and guidance based on learning analytics on ASE and PSS. The research was conducted according to experimental design with pretest–posttest control group and mixed methods approach (QUAN + qual) was used. This study was carried out on 44 university students randomly assigned to experimental group (LA group) and control group (non-LA groups). While feedback messages showing learning analytics to LA groups students were sent via learning management system (LMS) on a weekly basis, non-LA groups students did not receive any feedback messages. The experimental process of the study continued for 7 weeks within the FC model. Research data were obtained through ASE scale, problem-solving inventory and student opinion form. The results of the research show that sending feedback messages showing learning analytics to students has a statistically significant effect on their ASE and PSS. The qualitative findings of the study confirm the results obtained from the quantitative findings. Based on the current implementation, various suggestions were summarized for instructors, instructional designers, and researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers and Education, 58(1), 470–489.

    Google Scholar 

  • Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270). New York: ACM.

  • Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117–148.

    Google Scholar 

  • Barr, J., & Gunawardena, A. (2012). Classroom salon: a tool for social collaboration. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 197–202). New York: ACM.

  • Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Eugene: International Society for Technology in Education.

    Google Scholar 

  • Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599.

    Google Scholar 

  • Broughan, C., & Prinsloo, P. (2019). (Re) centring students in learning analytics: In conversation with Paulo Freire. Assessment and Evaluation in Higher Education. https://doi.org/10.1080/02602938.2019.1679716.

    Article  Google Scholar 

  • Casey, K., & Azcona, D. (2017). Utilizing student activity patterns to predict performance. International Journal of Educational Technology in Higher Education, 14(4), 1–15.

    Google Scholar 

  • Chen, L., Yoshimatsu, N., Goda, Y., Okubo, F., Taniguchi, Y., Oi, M., et al. (2019). Direction of collaborative problem solving-based STEM learning by learning analytics approach. Research and Practice in Technology Enhanced Learning, 14(1), 1–28.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Journal of Educational Technology and Society, 15(3), 58–76.

    Google Scholar 

  • Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013). Supporting action research with learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 220–229). New York: ACM.

  • Ekici, G. (2012). Academic self-efficacy scale: The study of adaptation to Turkish, validity and reliability. Hacettepe University Journal of Education, 43, 174–185.

    Google Scholar 

  • Ferguson, R., & Buckingham Shum, S. (2012). Social learning analytics: Five approaches. In 2nd international conference on learning analytics and knowledge. New York: ACM Press.

  • Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. The Internet and Higher Education, 14(2), 89–97.

    Google Scholar 

  • Giabbanelli, P.J., Tawfik, A.A., Gupta, V.K. (2019). Learning analytics to support teachers assessment of problem solving: A novel application for machine learning and graph algorithms. In: Utilizing learning analytics to support study success, (pp. 175–199). New York: Springer.

  • Grabau, C. R. (2015). Undergraduate student motivation and academic performance in a flipped classroom learning environment, Doctoral dissertation, Saint Louis University, St. Louis, MO.

  • Hämäläinen, R., De Wever, B., Nissinen, K., & Cincinnato, S. (2019). What makes the difference–PIAAC as a resource for understanding the problem-solving skills of Europe’s higher-education adults. Computers and Education, 129, 27–36.

    Google Scholar 

  • Heppner, P. P., & Petersen, C. H. (1982). The development and implications of a personal problem solving inventory. Journal of Counseling Psychology, 29, 66–75.

    Google Scholar 

  • Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84.

    Google Scholar 

  • Iandoli, L., Quinto, I., De Liddo, A., & Shum, S. B. (2014). Socially augmented argumentation tools: Rationale, design and evaluation of a debate dashboard. International Journal of Human-Computer Studies, 72(3), 298–319.

    Google Scholar 

  • Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 31–40). New York: ACM Digital Library.

  • Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016).In NMC horizon report: 2016 higher education edition (pp. 1–50). New York: The New Media Consortium.

  • Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.

    Google Scholar 

  • Jonassen, D. H. (2003). Learning to solve problems an instructional design guide. San Francisco: Pfeiffer.

    Google Scholar 

  • Karaoglan Yilmaz, F. G., Olpak, Y. Z., & Yilmaz, R. (2018). The effect of the metacognitive support via pedagogical agent on self-regulation skills. Journal of Educational Computing Research, 56(2), 159–180.

    Google Scholar 

  • Karaoglan Yilmaz, F. G. (2020). Modeling different variables in flipped classrooms supported with learning analytics feedback. Journal of Information and Communication Technologies, 2(1), 1–16.

    Google Scholar 

  • Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020a). Learning analytics as a metacognitive tool to influence learner transactional distance and motivation in online learning environments. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2020.1794928.

    Article  Google Scholar 

  • Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020b). Student opinions about personalized recommendation and feedback based on learning analytics. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-020-09460-8.

    Article  Google Scholar 

  • Kassab, S. E., Al-Shafei, A. I., Salem, A. H., & Otoom, S. (2015). Relationships between the quality of blended learning experience, self-regulated learning, and academic achievement of medical students: A path analysis. Advances in Medical Education and Practice, 6, 27–34.

    Google Scholar 

  • Kneeland, S. (2001). Problem solving (translated by N. Kalaycı). Ankara: Gazi Publishing.

    Google Scholar 

  • Kokoc, M. (2016). The relation of interaction with learning dashboards as a learning analytics tool in e-learning environment between learning outcomes. Doctoral dissertation, Hacettepe University, Ankara, Turkey.

  • Krumrei-Mancuso, E. J., Newton, F. B., Kim, E., & Wilcox, D. (2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54, 247–266.

    Google Scholar 

  • Lu, O. H., Huang, J. C., Huang, A. Y., & Yang, S. J. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220–234.

    Google Scholar 

  • Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics 120 approach: The role of the instructor. Internet and Higher Education, 24, 26–34.

    Google Scholar 

  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Owen, S. & Froman, R.D. (1988). Development of a college academic self-efficacy scale. Paper presented at the annual meeting of the National Council on Measurement in Education. New Orleans, LA.

  • Prinsloo, P. (2015). Algorithmic decision-making in higher education: There be dragons there. Retrieved from https://opendistanceteachingandlearning.wordpress.com/2015/12/14/algorithmic-decision-making-inhigher-education-there-be-dragons-there/. Accessed 30 July 2020.

  • Prinsloo, P. (2016). Failing our students: Not noticing the traces they leave behind. https://opendistanceteachingandlearning.wordpress.com/ ceteachingandlearning.wordpress.com/. Accessed July 26, 2020.

  • Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In Proceedings of the seventh ınternational learning analytics & knowledge conference (pp. 46–55). New York: ACM.

  • Prinsloo, P. (2019). Tracking (un) belonging: At the intersections of human-algorithmic student support. Retrieved from http://dspace.col.org/handle/11599/3373. Accessed 28 July 2020.

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

    Google Scholar 

  • Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Kloos, C. D. (2015). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior, 47, 139–148.

    Google Scholar 

  • Sabin, M., Alrumaih, H., Impagliazzo, J., Lunt, B., & Zhang, M. (2017). Information technology curricula 2017. In Curriculum guidelines for baccalaureate degree programs in information technology. New York: ACM and IEEE

  • Shen, C. X. (2018). Does school-related Internet Information seeking improve academic self-efficacy? The moderating role of internet information seeking styles. Computers in Human Behavior, 86, 91–98.

    Google Scholar 

  • Shum, S. (2012). Learning analytics policy brief. Moscow: UNESCO.

    Google Scholar 

  • Siemens, G., & Baker, R.S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254). New York: ACM.

  • Siemens, G., & Gasevic, D. (2012). Guest editorial-Learning and knowledge analytics. Educational Technology and Society, 15(3), 1–2.

    Google Scholar 

  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

    Google Scholar 

  • Solberg, V. S., O’Brien, K., Villareal, P., Kennel, R., & Davis, B. (1993). Self-efficacy and hispanic college students: Validation of the college self-efficacy instrument. Hispanic Journal of Behavioral Sciences, 15(1), 80–95.

    Google Scholar 

  • Strayer, J. F. (2012). How learning in an inverted classroom influences cooperation, innovation and task orientation. Learning Environments Research, 15(2), 171–193.

    Google Scholar 

  • Şahin, M. (2018). Design and development of the intervention engine based on learning analytics for e-learning environments. Doctoral dissertation, Hacettepe University, Ankara, Turkey.

  • Sahin, N., Sahin, N. H., & Heppner, P. P. (1993). Psychometric properties of the problem solving inventory in a group of Turkish university students. Cognitive Therapy and. Research, 17(4), 379–396.

    Google Scholar 

  • Tabak, F., Nguyen, N., Basuray, T., & Darrow, W. (2009). Exploring the impact of personality on performance: How time-on-task moderates the mediation by self-efficacy. Personality and Individual Differences, 47, 823–828.

    Google Scholar 

  • Ustun, A. B., & Tracey, M. W. (2019). An effective way of designing blended learning: A three phase design-based research approach. Education and Information Technologies. https://doi.org/10.1007/s10639-019-09999-9.

    Article  Google Scholar 

  • Ustun, A. B., Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020). Are teachers ready for e-learning? A study on exploring e-learning readiness of teachers. Journal of Ahmet Kelesoglu Education Faculty, 2(1), 54–69.

    Google Scholar 

  • Veerasamy, A. K., D’Souza, D., Lindén, R., & Laakso, M. J. (2019). Relationship between perceived problem-solving skills and academic performance of novice learners in introductory programming courses. Journal of Computer Assisted Learning, 35, 246–255.

    Google Scholar 

  • Wang, M., Yuan, B., Kirschner, P. A., Kushniruk, A. W., & Peng, J. (2018). Refective learning with complex problems in a visualization-based learning environment with expert support. Computers in Human Behavior, 87, 406–415.

    Google Scholar 

  • Wen, C. T., Chang, C. J., Chang, M. H., Chiang, S. H. F., Liu, C. C., Hwang, F. K., & Tsai, C. C. (2018). The learning analytics of model-based learning facilitated by a problem-solving simulation game. Instructional Science, 46(6), 847–867.

    Google Scholar 

  • Winne, P. H., Vytasek, J. M., Patzak, A., Rakovic, M., Marzouk, Z., Pakdaman-Savoji, A., et al. (2017). Designs for learning analytics to support information problem solving. In Informational environments (pp. 249–272). Cham: Springer.

  • Yilmaz, F. G., Yilmaz, R., Ustun, A. B., & Keser, H. (2019). Examination of critical thinking standards and academic self-efficacy of teacher candidates as a predictor of metacognitive thinking skills through structural equation modelling. Journal of Theoretical Educational Science, 12(4), 1239–1256.

    Google Scholar 

  • Yilmaz, F. G., Ustun, A. B., & Yilmaz, R. (2020). Investigation of pre-service teachers’ opinions on advantages and disadvantages of online formative assessment: An example of online multiple-choice exam. Journal of Teacher Education and Lifelong Learning, 2(1), 10–19.

    Google Scholar 

  • Yilmaz, R. (2020). Enhancing community of inquiry and reflective thinking skills of undergraduates through using learning analytics-based process feedback. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12449.

    Article  Google Scholar 

  • Yılmaz, M., Gürçay, D., & Ekici, G. (2007). Adaptation of the academic self-efficacy scale to Turkish. Hacettepe University Journal of Education, 33(33), 253–259.

    Google Scholar 

  • Zimmerman, B. J. (1995). Self-efficacy and educational development. In A. Bandura (Ed.), Self-efficacy in changing societies (pp. 202–231). New York: Cambridge University Press.

    Google Scholar 

Download references

Funding

The authors received no financial support for the research and/or authorship of this article.

Author information

Authors and Affiliations

  1. Department of Information Systems and Technologies, Faculty of Science, Bartin University, Bartin, Turkey

    Fatma Gizem Karaoglan Yilmaz

Authors
  1. Fatma Gizem Karaoglan Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatma Gizem Karaoglan Yilmaz.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karaoglan Yilmaz, F.G. Utilizing Learning Analytics to Support Students' Academic Self-efficacy and Problem-Solving Skills. Asia-Pacific Edu Res 31, 175–191 (2022). https://doi.org/10.1007/s40299-020-00548-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40299-020-00548-4

Keywords

Search

Navigation