Skip to main content
SpringerLink
Log in

Learning Feedback Based on Dispositional Learning Analytics

  • Chapter
  • First Online:
Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 158))

Abstract

The combination of trace data captured from technology-enhanced learning support systems, formative assessment data and learning disposition data based on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk, but also provide both students and teacher with actionable feedback. The ability to link predictions, such as a risk for drop-out, with characterizations of learning dispositions, such as profiles of learning strategies, implies that the provision of learning feedback is not the end point, but can be extended to the design of learning interventions that address suboptimal learning dispositions. Building upon the case studies we developed in our previous research, we replicated the Dispositional Learning Analytics analyses in the most recent 17/18 cohort of students based on the learning processes of 1017 first-year students in a blended introductory quantitative course. We conclude that the outcomes of these analyses, such as boredom being an important learning emotion, planning and task management being crucial skills in the efficient use of digital learning tools, help both predict learning performance and design effective interventions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., et al.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agents systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 427–449. Springer, Amsterdam (2013)

    Chapter  Google Scholar 

  2. Buckingham Shum, S., Deakin Crick, R.: Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics. In: Buckingham Shum, S., Gasevic, D., Ferguson, R. (eds.) Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 92–101. ACM, New York, NY, USA (2012). https://doi.org/10.1145/2330601.2330629

  3. Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40, 4715–4729 (2013). https://doi.org/10.1016/j.eswa.2013.02.007

    Article  Google Scholar 

  4. Crick, R.: Learning analytics: layers, loops and processes in a virtual learning infrastructure. In: Lang, C., Siemens, G., Wise, A., Gašević, D. (eds.) Handbook of Learning Analytics, Chap. 25, pp. 291–308. Society for Learning Analytics Research, Solar (2017) https://doi.org/10.18608/hla17

  5. Elliot, A., Murayama, K., Kobeisy, A., Lichtenfeld, S.: Potential-based achievement goals. Brit. J. Educ. Psychol. 85, 192–206 (2015). https://doi.org/10.1111/bjep.12051

  6. Gašević, D., Jovanović, J., Pardo, A., Dawson, S.: Detecting learning strategies with analytics: links with self-reported measures and academic performance. J. Learn. Anal. 4(1), 113–128 (2017). https://doi.org/10.18608/jla.2017.42.10

    Article  Google Scholar 

  7. Hofstede, G., Hofstede, G.J., Minkov, M.: Cultures and Organizations: Software of the Mind. Revised and Expanded, 3rd edn. McGraw-Hill, Maidenhead (2010)

    Google Scholar 

  8. Martin, A.J.: Examining a multidimensional model of student motivation and engagement using a construct validation approach. Br. J. Educ. Psychol. 77(2), 413–440 (2007). https://doi.org/10.1348/000709906X118036

    Article  Google Scholar 

  9. Matzavela, V., Chrysafiadi, K., Alepis, E.: Questionnaires and artificial neural networks: a literature review on modern techniques in education. In: Proceedings 2017 IEEE Global Engineering Education Conference (EDUCON), 25–28 April 2017, Athens, Greece (2017). https://doi.org/10.1109/educon.2017.7943077

  10. Mittelmeier, J., Rienties, B., Tempelaar, D.T., Hillaire, G., Whitelock, D.: The influence of internationalised versus local content on online intercultural collaboration in groups: a randomised control trial study in a statistics course. Comput. Educ. 118(1), 82–95 (2018). https://doi.org/10.1016/j.compedu.2017.11.003

    Article  Google Scholar 

  11. Nguyen, Q., Tempelaar, D.T., Rienties, B., Giesbers, B.: What learning analytics based prediction models tell us about feedback preferences of students. In: Amirault, R., Visser, Y. (eds.) e-Learners and their data, part 1: conceptual, research, and exploratory perspectives. Q. Rev. Distance Educ. 17(3) (2016)

    Google Scholar 

  12. Non, A., Tempelaar, D.: Time preferences, study effort, and academic performance. Econ. Educ. Rev. 54, 36–61 (2016). https://doi.org/10.1016/j.econedurev.2016.06.003

    Article  Google Scholar 

  13. Ochea, X.: Multimodal learning analytics. In: Lang, C., Siemens, G., Wise, A., Gašević, D. (eds.) Handbook of Learning Analytics, Chap. 11, pp. 129–141. Solar: Society for Learning Analytics (2017). https://doi.org/10.18608/hla17

  14. Pekrun, R., Linnenbrink-Garcia, L.: Academic emotions and student engagement. In: Christenson, S.L., Reschly, A.L., Wylie, C. (eds.) Handbook of Research on Student Engagement, pp. 259–282. Springer US (2012). https://doi.org/10.1007/978-1-4614-2018-7_12

  15. Rienties, B., Alden Rivers, B.: Measuring and understanding learner emotions: evidence and prospects. In: LACE Review Papers, vol. 1. LACE, Milton Keynes (2014)

    Google Scholar 

  16. Rienties, B., Cross, S., Zdrahal, Z.: Implementing a learning analytics intervention and evaluation framework: what works? In: Kei Daniel, B. (ed.) Big Data and Learning Analytics: Current Theory and Practice in Higher Education, pp. 147–166. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-06520-5_10

  17. Rogaten, J., Rienties, B., Sharpe, R., Cross, S., Whitelock, D., Lygo-Baker, S., Littlejohn, A.: Reviewing affective, behavioural, and cognitive learning gains in higher education. In: Assessment & Evaluation in Higher Education (2018). https://doi.org/10.1080/02602938.2018.1504277

  18. Tempelaar, D.T.: Learning analytics and formative assessments in blended learning of mathematics and statistics. Innov. Infotechnologies Sci. Bus. Educ. 2(17), 9–13 (2014)

    Google Scholar 

  19. Tempelaar, D.T., Cuypers, H., Van de Vrie, E., Heck, A., Van der Kooij, H.: Formative assessment and learning analytics. In: Suthers, D., Verbert, K. (eds.) Proceedings of the 3rd International Conference on Learning Analytics and Knowledge, pp. 205–209. ACM, New York (2013). https://doi.org/10.1145/2460296.2460337

  20. Tempelaar, D.T., Gijselaers, W.H., Schim van der Loeff, S., Nijhuis, J.F.H.: A structural equation model analyzing the relationship of student achievement motivations and personality factors in a range of academic subject-matter areas. Contemp. Educ. Psychol. 32(1), 105–131 (2007). https://doi.org/10.1016/j.cedpsych.2006.10.004

    Article  Google Scholar 

  21. Tempelaar, D.T., Niculescu, A., Rienties, B., Giesbers, B., Gijselaers, W.H.: How achievement emotions impact students’ decisions for online learning, and what precedes those emotions. Internet High. Educ. 15(2012), 161–169 (2012). https://doi.org/10.1016/j.iheduc.2011.10.003

  22. Tempelaar, D.T., Rienties, B., Giesbers, B.: Who profits most from blended learning? Ind. High. Educ. 23(4), 285–292 (2009). https://doi.org/10.5367/000000009789346130

    Article  Google Scholar 

  23. Tempelaar, D.T., Rienties, B., Giesbers, B.: Computer assisted, formative assessment and dispositional learning analytics in learning mathematics and statistics. In: Kalz, M., Ras, E. (eds.) Computer Assisted Assessment. Research into E-Assessment, vol. 439, pp. 67–78. Springer International Publishing (2014)

    Google Scholar 

  24. Tempelaar, D.T., Rienties, B., Giesbers, B.: Computer assisted, formative assessment and dispositional learning analytics in learning mathematics and statistics. In: Kalz, M., Ras, E. (eds.) Computer Assisted Assessment. Research into E-Assessment, pp. 67–78. Communications in Computer and Information Science, vol. 439. Springer, Berlin (2014). https://doi.org/10.1007/978-3-319-08657-6_7

  25. Tempelaar, D.T., Rienties, B., Giesbers, B.: In search for the most informative data for feedback generation: learning analytics in a data-rich context. Comput. Hum. Behav. 47, 157–167 (2015) (Special Issue Learning Analytics). https://doi.org/10.1016/j.chb.2014.05.038

  26. Tempelaar, D.T., Rienties, B., Giesbers, B.: Understanding the role of time on task in formative assessment: the case of mathematics learning. In: Ras, E., Joosten-ten Brinke, D. (eds.) Computer Assisted Assessment—Research into E-Assessment. Communications in Computer and Information Science, vol. 571, pp. 120–133. Springer, Zug, Switzerland (2015). https://doi.org/10.1007/978-3-319-27704-2_12

  27. Tempelaar, D.T., Rienties, B., Giesbers, B.: Verifying the stability and sensitivity of learning analytics based prediction models: an extended case study. In: Zvacek, S., Restivo, M.T., Uhomoibhi, J., Helfert, M. (eds.) Computer Supported Education, pp. 256–273. Communications in Computer and Information Science, vol. 583. Springer, Switzerland (2016). https://doi.org/10.1007/978-3-319-29585-5_15

  28. Tempelaar, D.T., Rienties, B., Giesbers, B., Schim van der Loeff, S.: How cultural and learning style differences impact students’ learning preferences in blended learning. In: Jean Francois, E. (ed.) Transcultural Blended Learning and Teaching in Postsecondary Education, pp. 30–51. IGI Global, Hershey, PA (2013). https://doi.org/10.4018/978-1-4666-2014-8.ch003

  29. Tempelaar, D.T., Rienties, B., Giesbers, B., Schim van der Loeff, S.: Cultural differences in learning approaches. In: Van den Bossche, P., Gijselaers, W.H., Milter, R.G. (eds.) Facilitating Learning in the 21st Century: Leading through Technology, Diversity and Authenticity, pp. 1–28. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-6137-7_1

  30. Tempelaar, D.T., Rienties, B., Mittelmeier, J., Nguyen, Q.: Student profiling in a dispositional learning analytics application using formative assessment. Comput. Hum. Behav. 78, 408–420 (2018). https://doi.org/10.1016/j.chb.2017.08.010

    Article  Google Scholar 

  31. Tempelaar, D.T., Rienties, B., Nguyen, Q.: Towards actionable learning analytics using dispositions. IEEE Trans. Educ. 10(1), 6–16 (2017). https://doi.org/10.1109/TLT.2017.2662679

    Article  Google Scholar 

  32. Tempelaar, D.T., Rienties, B., Nguyen, Q.: Adding dispositions to create pedagogy-based learning analytics. Zeitschrift für Hochschulentwicklung, ZFHE 12(1), 15–35 (2017)

    Google Scholar 

  33. Tempelaar, D., Rienties, B., Nguyen, Q.: Investigating learning strategies in a dispositional learning analytics context: the case of worked examples. In: Proceedings of the International Conference on Learning Analytics and Knowledge, Sydney, Australia, March 2018 (LAK’18), pp. 201–205 (2018). https://doi.org/10.1145/3170358.3170385

  34. Tempelaar, D., Rienties, B., Nguyen, Q.: A multi-modal study into students’ timing and learning regulation: time is ticking. In: Interactive Technology and Smart Education (2018) (in press)

    Google Scholar 

  35. Tempelaar, D.T., Verhoeven, P.: Adaptive and maladaptive emotions, behaviours and cognitions in the transition to university: the experience of international full degree students. In: Jindal-Snape, D., Rienties, B. (eds.) Multi-Dimensional Transitions of International Students to Higher Education, Chapter 12, pp. 334–358. Routledge, New York (2016)

    Google Scholar 

  36. Vermunt, J.D.: Metacognitive, cognitive and affective aspects of learning styles and strategies: a phenomenographic analysis. High. Educ. 31(25–50) (1996). https://doi.org/10.1007/bf00129106

  37. Williams, A., Sun, Z., Xie, K., Garcia, E., Ashby, I., Exter, M., Largent, D., Lu, P., Szafron, D., Ahmed, S., Onuczko, T., Smith, J., Tempelaar, D.T., Bitting, K.S., Olcott Marshall, A., Christensen, E., Tseng, H., Walsh, J.: Flipping STEM. In: Santos Green, L., Banas, J., Perkins, R. (eds.) The Flipped College Classroom, Conceptualized and Re-Conceptualized, Part II, pp. 149–186. Springer International Publishing, Switzerland (2017). https://doi.org/10.1007/978-3-319-41855-1_8

Download references

Author information

Authors and Affiliations

  1. School of Business and Economics, Maastricht University, Maastricht, Netherlands

    Dirk Tempelaar

  2. Institute of Educational Technology, The Open University, Walton Hall, Milton Keynes,, MK7 6AA, UK

    Quan Nguyen & Bart Rienties

Authors
  1. Dirk Tempelaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quan Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bart Rienties
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dirk Tempelaar .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Efthimios Alepis

  3. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  4. Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tempelaar, D., Nguyen, Q., Rienties, B. (2020). Learning Feedback Based on Dispositional Learning Analytics. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation