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Linking Assessment and Learning Analytics to Support Learning Processes in Higher Education

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Learning, Design, and Technology

Abstract

In higher education assessments are mostly used for summative purposes such as grading and certifying. Albeit, assessments are also considered to support learning processes by offering formative feedback to learners about their current performance and how to improve. Even though such feedback might enhance learners’ self-regulated learning processes, it is used infrequently due to resource constraints. In addition, the competences, skills, and knowledge that should be assessed are evermore complex. To derive valid inferences about learners’ current performance, ongoing assessments across contexts are desirable. With the advancing use of digital learning environments, learning analytics are also coming in for increasing discussion in higher education. However, learning analytics are still not sufficiently linked to learning theory and are lacking empirical evidence. Hence, the purpose of this paper is to propose how theory on assessment and related feedback can be linked to learning analytics with regard to supporting self-regulated learning. Therefore, relevant concepts of assessment, assessment design, and feedback plus current perspectives on learning analytics are introduced. Based on this theoretical foundation, a conceptual integrative framework and potential learning analytics features were derived. The framework and its implications plus further research needs are discussed and concluded.

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References

  • AERA, APA, & NCME. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association, American Psychological Association, National Council on Measurement in Education.

    Google Scholar 

  • Aguilar, S. J. (2018). Learning analytics: At the nexus of big data, digital innovations, and social justice in education. TechTrends, 62, 37–45. https://doi.org/10.1007/s11528-017-0226-9

    Article  Google Scholar 

  • Aldowah, H., Al-Smarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analyitics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007

    Article  Google Scholar 

  • Aljohani, N. R., Daud, A., Abbasi, R. A., Alowibdi, J. S., Basheri, M., & Aslam, M. A. (2019). An integrated framework for course adapted student learning analytics dashboard. Computers in Human Behavior, 92, 679–690. https://doi.org/10.1016/j.chb.2018.03.035

    Article  Google Scholar 

  • Almond, R. G. (2010). Using evidence centered design to think about assessments. In V. J. Shute & B. J. Becker (Eds.), Innovative assessment for the 21st century. Supporting educational needs (pp. 75–100). New York, NY: Springer.

    Chapter  Google Scholar 

  • Baker, R. S., Martin, T., & Rossi, L. M. (2017). Educational data mining and learning analytics. In A. A. Rupp & J. P. Leighton (Eds.), The handbook of cognition and assessment: Frameworks, methodologies, and applications (pp. 379–396). Chichester, WSX: Wiley.

    Google Scholar 

  • Bannert, M. (2009). Promoting self-regulated learning through prompts. Zeitschrift für Pädagogische Psychologie, 23(2), 139–145.

    Article  Google Scholar 

  • Bearman, M., Dawson, P., Boud, D., Bennett, S., Hall, M., & Molloy, E. (2016). Support for assessment practice: Developing the assessment design decisions framework. Teaching in Higher Education, 21(5), 545–556. https://doi.org/10.1080/13562517.2016.1160217

    Article  Google Scholar 

  • Bennett, R. E. (2011). Formative assessment: A critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5–25. https://doi.org/10.1080/0969594X.2010.513678

    Article  Google Scholar 

  • Bevitt, S. (2015). Assessment innovation and student experience: A new assessment challenge and call for a multi-perspecitve approach to assessment research. Assessment and Evaluation in Higher Education, 40(1), 103–119. https://doi.org/10.1080/02602938.2014.890170

    Article  Google Scholar 

  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analyitcs: An issue brief. Washington, DC: Office of Educational Technology.

    Google Scholar 

  • Black, P. (2013). Formative and summative aspects of assessment: Theoretical and research foundations in the context of pedagogy. In J. H. McMillan (Ed.), SAGE handbook of research on classroom assessment (pp. 167–178). Thousandsand Oaks, CA: SAGE.

    Chapter  Google Scholar 

  • Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2003). Assessment for learning. putting it into practice. Maidenhead, UK: Open University Press.

    Google Scholar 

  • Black, P., McCormick, R., James, M., & Pedder, D. (2006). Learning how to learn and assessment for learning: A theoretical inquiry. Research Papers in Education, 21(2), 119–132. https://doi.org/10.1080/02671520600615612

    Article  Google Scholar 

  • Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21, 5–15. https://doi.org/10.1007/s11092-008-9068-5

    Article  Google Scholar 

  • Black, P., & Wiliam, D. (2018). Classroom assessment and pedagogy. Assessment in Education: Principles, Policy & Practice, 25(6), 551–575. https://doi.org/10.1080/0969594X.2018.1441807

    Article  Google Scholar 

  • Bosse, E. (2015). Exploring the role of student diversity for the first-year experience. Zeitschrift für Hochschulentwicklung, 10(4), 45–66.

    Article  Google Scholar 

  • Boud, D. (2007). Reframing assessment as if learning were important. In D. Boud & N. Falchikov (Eds.), Rethinking assessment in higher education (pp. 14–25). London, UK: Routledge.

    Chapter  Google Scholar 

  • Boud, D., & Falchikov, N. (2007). Assessment for the longer term. In D. Boud & N. Falchikov (Eds.), Rethinking assessment in higher education (pp. 3–13). New York, NY: Routledge.

    Chapter  Google Scholar 

  • Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: The challenge of design. Assessment and Evaluation in Higher Education, 38(6), 698–712. https://doi.org/10.1080/02602938.2012.691462

    Article  Google Scholar 

  • Broadbent, J., Panadero, E., & Boud, D. (2017). Implementing summative assessment with a formative flavour: A case study in a large class. Assessment and Evaluation in Higher Education, 43(2), 307–322. https://doi.org/10.1080/02602938.2017.1343455

    Article  Google Scholar 

  • Brooks, C., & Thompson, C. (2017). Predictive modelling in teaching and learning. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 61–68). SOLAR, Society for Learning Analytics and Research. https://www.solaresearch.org/wp-content/uploads/2017/05/hla17.pdf

  • 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 

  • Carless, D. (2007). Learning-oriented assessment: Conceptual bases and practical implications. Innovations in Education and Teaching International, 44(1), 57–66. https://doi.org/10.1080/14703290601081332

    Article  Google Scholar 

  • Carless, D. (2017). Scaling up assessment for learning: Progress and prospects. In D. Carless, S. M. Bridges, C. K. Y. Chan, & R. Glofcheski (Eds.), Scaling up assessment for learning in higher education (pp. 3–17). Singapore, Singapore: Springer.

    Chapter  Google Scholar 

  • Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment and Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354

    Article  Google Scholar 

  • Carless, D., Salter, D., Yang, M., & Lam, J. (2011). Developping sustainable feedback practices. Studies in Higher Education, 36(4), 395–407. https://doi.org/10.1080/03075071003642449

    Article  Google Scholar 

  • Cartney, P. (2010). Exploring the use of peer assessment as a vehicle for closing the gap between feedback given and feedback used. Assessment and Evaluation in Higher Education, 35(5), 551–564. https://doi.org/10.1080/02602931003632381

    Article  Google Scholar 

  • Cassidy, S. (2006). Developing employability skills: Peer assessment in higher education. Education and Training, 48(7), 508–517. https://doi.org/10.1108/00400910610705890

    Article  Google Scholar 

  • Cassidy, S. (2011). Self-regulated learning in higher education: Identifying key component processes. Studies in Higher Education, 36(8), 989–1000.

    Article  Google Scholar 

  • Chen, X., Breslow, L., & DeBoer, J. (2018). Analyzing productive learning behaviors for students using immediate corrective feedback in a blended learning environment. Computers & Education, 117, 59–74. https://doi.org/10.1016/j.compedu.2017.09.013

    Article  Google Scholar 

  • Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653

    Article  Google Scholar 

  • Corrin, L., & da Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J. McDonald, & S.-K. Loke (Eds.), Rethoric and reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 629–633). Dunedin, New Zealand.

    Google Scholar 

  • Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

    Article  Google Scholar 

  • Deci, E. L. (1992). The relation of interest to the motivation of behavior: A self-determination theory perspective. In K. A. Renninger, S. Hidi, & A. Krapp (Eds.), The role of interest in learning and development (pp. 43–70). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • DiCerbo, K. E., Shute, V. J., & Kim, Y. J. (2016). The future of assessment in technology-rich environments: Psychometric considerations. In M. J. Spector, B. B. Lockee, & M. D. Childress (Eds.), Learning, design, and technology: An international compendium of theory, research, practice, and policy (pp. 1–21). Cham, Switzerland: Springer.

    Google Scholar 

  • Draper, S. W. (2009). What are learners actually regulating when given feedback? British Journal of Educational Technology, 40(2), 306–315. https://doi.org/10.1111/j.1467-8535.2008.00930.x

    Article  Google Scholar 

  • Ellis, C. (2013). Broadening the scope and increasing the usefulness of learning analytics: The case for assessment analytics. British Journal of Educational Technology, 44(4), 662–664. https://doi.org/10.1111/bjet.12028

    Article  Google Scholar 

  • Ellis, R. A., Han, F., & Pardo, A. (2017). Improving learning analytics – Combining observational and self-report data on student learning. Educational Technology & Society, 20(3), 158–169.

    Google Scholar 

  • Evans, C. (2013). Making sense of assessment feedback in higher education. Review of Educational Research, 83(1), 70–120. https://doi.org/10.3102/0034654312474350

    Article  Google Scholar 

  • Falchikov, N. (2005). Improving assessment through student involvement. Practical solutions for aiding learning in higher and further education. Abingdon, OX: Routledge.

    Google Scholar 

  • Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317.

    Article  Google Scholar 

  • Ferguson, R., & Buckingham Shum, S. (2012). Social learning analytics: Five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (LAK) (pp. 22–33). Vancouver, CA: ACM.

    Google Scholar 

  • Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In LAK ‘17 proceedings of the seventh international learning analytics & knowledge conference (pp. 56–65). New York, NY: ACM.

    Chapter  Google Scholar 

  • Fogarty, R. J., & Kerns, G. M. (2009). inFormative assessment: When It’s not about a grade. Thousand Oaks, CA: Corwin.

    Google Scholar 

  • Forster, M. (2009). Informative assessment: Understanding and guiding learning. Paper presented at the ACER research conference: Assessment and student learning, Perth, WA.

    Google Scholar 

  • Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84.

    Article  Google Scholar 

  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

    Article  Google Scholar 

  • Gašević, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128. https://doi.org/10.18608/jla.2017.42.10

    Article  Google Scholar 

  • Gibbs, G., & Simpson, C. (2005). Conditions under which assessment supports students’ learning. Learning and Teaching in Higher Education, 1, 3–31.

    Google Scholar 

  • Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.

    Google Scholar 

  • Hargreaves, E. (2007). The validity of collaborative assessment for learning. Assessment in Education: Principles, Policy & Practice, 14(2), 185–199. https://doi.org/10.1080/09695940701478594

  • Hattie, J. A. C., & Clarke, S. (2019). Visible learning: Feedback. New York, NY: Routledge.

    Book  Google Scholar 

  • Hattie, J. A. C., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

    Article  Google Scholar 

  • Hernández-Garcíac, Á., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68–80. https://doi.org/10.1016/j.chb.2014.10.038

    Article  Google Scholar 

  • Howell, J. A., Roberts, L. D., & Mancini, V. O. (2018). Learning analytics messages: Impact of grade, sender, comparative information and message style on student affect and academic resilience. Computers in Human Behavior, 89, 8–15. https://doi.org/10.1016/j.chb.2018.07.021

    Article  Google Scholar 

  • Hsu, Y.-S., Wang, C.-Y., & Zhang, W.-X. (2017). Supporting technology-enhanced inquiry through metacognitive and cognitive prompts: Sequential analysis of metacognitive actions in response to mixed prompts. Computers in Human Behavior, 72, 701–712. https://doi.org/10.1016/j.chb.2016.10.004

    Article  Google Scholar 

  • Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The Sage encyclopedia of educational technology (Vol. 2, pp. 447–451). Los Angeles, CA: SAGE.

    Google Scholar 

  • Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371. https://doi.org/10.1007/s11528-016-0154-0

    Article  Google Scholar 

  • Ifenthaler, D. (2019). Learning analytics and study success. Current landscape of learning analytics research. Paper presented at the innovations in education: Opportunities and challenges of digitization research workshop, Mannheim, BW.

    Google Scholar 

  • Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938. https://doi.org/10.1007/s11423-016-9477-y

  • Ifenthaler, D., Greiff, S., & Gibson, D. C. (2018). Making use of data for assessments: Harnessing analytics and data science. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of information Technology in Primary and Secondary Education (pp. 649–663). New York, NY: Springer.

    Google Scholar 

  • Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240. https://doi.org/10.1007/s10758-014-9226-4

    Article  Google Scholar 

  • Ito, J. (2019). Forget about artificial intelligence, extended intelligence is the future. Retrieved from https://www.wired.co.uk/article/artificial-intelligence-extended-intelligence

  • Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions of performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284.

    Article  Google Scholar 

  • Knight, P. (2006). The local practice of assessment. Assessment and Evaluation in Higher Education, 31(4), 435–452. https://doi.org/10.1080/02602930600679126

    Article  Google Scholar 

  • Kramarski, B., & Kohen, Z. (2017). Promoting preservice teachers’ dual self-regulation roles as learners and as teachers: Effects of generic vs. specific prompts. Metacognition and Learning, 12, 157–191. https://doi.org/10.1007/s11409-016-9164-8

    Article  Google Scholar 

  • Kulik, J. A., & Kulik, C.-L. C. (1988). Timing of feedback and verbal learning. Review of Educational Research, 58, 79.

    Article  Google Scholar 

  • Lave, J., & Wenger, E. (1991). Situated learning. Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Liu, M., Kang, J., Zou, W., Lee, H., Pan, Z., & Corliss, S. (2017). Using data to understand how to better design adaptive learning. Technology, Knowledge and Learning, 22, 271–298. https://doi.org/10.1007/s10758-017-9326-z

    Article  Google Scholar 

  • Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90–97. https://doi.org/10.1016/j.chb.2014.07.013

    Article  Google Scholar 

  • Luecht, R. M. (2013). An introduction to assessment engineering for automatic item generation. In M. J. Gierl & T. M. Haladyna (Eds.), Automatic item generation: Theory and practice (pp. 59–76). New York, NY: Routledge.

    Google Scholar 

  • Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54, 588–599.

    Article  Google Scholar 

  • Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technolgy & Society, 15(3), 149–163.

    Google Scholar 

  • Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research and Practice in Assessment, 9, 17–28.

    Google Scholar 

  • Mah, D.-K., & Ifenthaler, D. (2018). Students’ perceptions toward academic competencies: The case of German first-year students. Issues in Educational Research, 28(1), 120–137.

    Google Scholar 

  • Martin, F., & Ndoye, A. (2016). Using learning analytics to assess student learning in online courses. Journal of University Teaching & Learning Practice, 13(3), Art. 7.

    Google Scholar 

  • Martin, T., & Sherin, B. (2013). Learning analytics and computational techniques for detecting and evaluating patterns in learning: An introduction to the special issue. Journal of the Learning Sciences, 22(4), 511–520. https://doi.org/10.1080/10508406.2013.840466

    Article  Google Scholar 

  • Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., … Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6), 1–18. https://doi.org/10.14742/ajet.3058.

  • Mislevy, R. J., Almond, R. G., & Lukas, J. F. (2003). A brief introduction to evidence-centered design. ETS Report Series, 2003(1), i–29.

    Article  Google Scholar 

  • Mislevy, R. J., & Haertel, G. D. (2006). Implications of evidence-centered design for educational testing. Educational Measurement: Issues and Practice, 25(4), 6–20. https://doi.org/10.1111/j.1745-3992.2006.00075.x

    Article  Google Scholar 

  • Mislevy, R. J., & Riconscente, M. M. (2005). Evidence-centered assessment design: Layers, structures, and terminology. PADI Technical Report, 2005(9).

    Google Scholar 

  • Müller, N. M., & Seufert, T. (2018). Effects of self-regulation prompts in hypermedia learning on learning performance and self-efficacy. Learning and Instruction, 58, 1–11. https://doi.org/10.1016/j.learninstruc.2018.04.011

    Article  Google Scholar 

  • Narciss, S. (2008). Feedback strategies for interactive learning tasks. In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (pp. 125–144). New York, NY: Lawrence Erlbaum Associates.

    Google Scholar 

  • Narciss, S. (2012). Feedback in instructional settings. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 1285–1289). Berlin, Germany: Springer.

    Chapter  Google Scholar 

  • Narciss, S. (2017). Conditions and effects of feedback viewed through the lens of the interactive tutoring feedback model. In D. Carless, S. M. Bridges, C. K. Y. Chan, & R. Glofcheski (Eds.), Scaling up assessment for learning in higher education (pp. 173–189). Singapore, Singapore: Springer.

    Chapter  Google Scholar 

  • Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., & Melis, E. (2014). Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers and Education, 71, 56–76. https://doi.org/10.1016/j.compedu.2013.09.011

    Article  Google Scholar 

  • Neumann, R., Parry, S., & Becher, T. (2002). Teaching and learning in their disciplinary contexts: A conceptual analysis. Studies in Higher Education, 27(4), 405–417. https://doi.org/10.1080/0307507022000011525

    Article  Google Scholar 

  • Nichols, P. D., Kobrin, J. L., Lai, E., & Koepfler, J. (2017). The role of theories of learning and cognition in assessment design and development. In A. A. Rupp & J. P. Leighton (Eds.), The handbook on cognition and assessment. frameworks, methodologies, and applications (pp. 15–40). Chichester, UK: Wiley.

    Google Scholar 

  • Nicol, D. J. (2009). Assessment for learner self-regulation: Enhancing achievement in the first year using learning technologies. Assessment and Evaluation in Higher Education, 34(3), 335–352. https://doi.org/10.1080/02602930802255139

    Article  Google Scholar 

  • Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles for good feedback practice. Studies in Higher Education, 31(2), 199–218.

    Article  Google Scholar 

  • Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford, CA: Stanford University Press.

    Google Scholar 

  • Nistor, N., & Hernández-Garcíac, Á. (2018). What types of data are used in learning analytics? An overview of six cases. Computers in Human Behavior, 89, 335–338. https://doi.org/10.1016/j.chb.2018.07.038

    Article  Google Scholar 

  • NRC. (1996). National science education standards. Washington, DC: National Academy Press.

    Google Scholar 

  • Panadero, E., Broadbent, J., Boud, D., & Lodge, J. M. (2018). Using formative assessment to influence self- and co-regulated learning: The role of evaluative judgement. European Journal of Psychology of Education, 34, 535. https://doi.org/10.1007/s10212-018-0407-8

    Article  Google Scholar 

  • Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74–98. https://doi.org/10.1016/j.edurev.2017.08.004

    Article  Google Scholar 

  • Papamitsiou, Z., & Economides, A. A. (2014). Learning analyitics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.

    Google Scholar 

  • Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalized feedback. British Journal of Educational Technology, 50(1), 128–138. https://doi.org/10.1111/bjet.12592

    Article  Google Scholar 

  • Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.

    Article  Google Scholar 

  • Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated laearning. Educational Psychologist, 36(2), 89–101.

    Article  Google Scholar 

  • Park, Y., & Jo, I.-H. (2015). Development of the learning analytics dashboard to support students’ learning performance. Journal of Universal Computer Science, 21(1), 110–133.

    Google Scholar 

  • Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: The National Academies Press.

    Google Scholar 

  • Pereira, D., Assunção Flores, M., & Niklasson, L. (2016). Assessment revisited: A review of research in assessment and evaluation in higher education. Assessment & Evaluation in Higher Education, 41(7), 1008–1032.

    Article  Google Scholar 

  • Piaget, J. (1975). L’Equilibration des Structures Cognitives. Problème Central du Développement. Paris, France: Presses Universitaires de France.

    Google Scholar 

  • Pinheiro Cavalcanti, A., Rolim, V., André, M., Freitas, F., Ferreira, R., & Gašević, D. (2019). An analysis of the use of good feedback practices in online learning courses. Paper presented at the IEEE international conference on advanced learning technologies and technology-enhanced learning (ICALT), Maceió, Brazil.

    Google Scholar 

  • Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic Press.

    Chapter  Google Scholar 

  • Prieger, E., & Bannert, M. (2018). Differential effects of students’ self-directed metacognitive prompts. Computers in Human Behavior, 86, 165–173. https://doi.org/10.1016/j.chb.2018.04.022

    Article  Google Scholar 

  • Ramaprasad, A. (1983). On the definition of feedback. Behavioral Science, 28, 4–13.

    Article  Google Scholar 

  • Roberts, L. D., Howell, J. A., & Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge and Learning, 22, 317–333. https://doi.org/10.1007/s10758-017-9316-1

    Article  Google Scholar 

  • Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3(January/February), 12–27. https://doi.org/10.1002/widm.1075

    Article  Google Scholar 

  • Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18, 119–144.

    Article  Google Scholar 

  • Sadler, D. R. (1998). Formative assessment: Revisiting the territory. Assessment in Education: Principles, Policy & Practice, 5(1), 77–84. https://doi.org/10.1080/0969595980050104

    Article  Google Scholar 

  • Sadler, D. R. (2010a). Assessment in higher education. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (3rd ed., pp. 249–255). Oxford, UK: Academic Press.

    Chapter  Google Scholar 

  • Sadler, D. R. (2010b). Beyond feedback: Developing student capability in complex appraisal. Assessment and Evaluation in Higher Education, 35(5), 535–550. https://doi.org/10.1080/02602930903541015

    Article  Google Scholar 

  • Schroth, M. L. (1992). The effects of delay of feedback on a delayed concept formation transfer task. Contemporary Educational Psychology, 17, 78–82.

    Article  Google Scholar 

  • Schunk, D. H. (2008). Attributions as motivators of self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning. Theory, research, and applications (pp. 245–266). New York, NY: Routledge.

    Google Scholar 

  • Schumacher, C., & Ifenthaler, D. (2018a). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. https://doi.org/10.1016/j.chb.2017.06.030

  • Schumacher, C., & Ifenthaler, D. (2018b). The importance of students’ motivational dispositions for designing learning analytics. Journal of Computing in Higher Education, 30(3), 599–619. https://doi.org/10.1007/s12528-018-9188-y

  • Schumacher, C., & Ifenthaler, D. (under review). Designing effective means of supporting students’ regulation of learning processes through analytics-based prompts.

    Google Scholar 

  • Schumacher, C., Klasen, D., & Ifenthaler, D. (2019). Implementation of a learning analytics system in a productive higher education environment. In M. S. Khine (Ed.), Emerging Trends in Learning Analytics. Leveraging the Power of Educational Data (pp. 177–199). Leiden: Brill.

    Google Scholar 

  • Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education. A review of UK and international practice. Retrieved from https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf

  • Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2018). Linking learning behavior analytics and science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2018.05.004

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795

    Article  Google Scholar 

  • Shute, V. J., & Becker, B. J. (2010). Prelude: Assessment for the 21st century. In V. J. Shute & B. J. Becker (Eds.), Innovative assessment for the 21st century. Supporting educational needs (pp. 1–11). New York, NY: Springer.

    Chapter  Google Scholar 

  • Shute, V. J., Leighton, J. P., Jang, E. E., & Chu, M.-W. (2016). Advances in the science of assessment. Educational Assessment, 21(1), 34–59. https://doi.org/10.1080/10627197.2015.1127752

    Article  Google Scholar 

  • Shute, V. J., Rahimi, S., & Emihovich, B. (2017). Assessment for learning in immersive environments. In D. Liu, C. Dede, R. Huang, & J. Richards (Eds.), Virtual, augmented, and mixed realities in education (pp. 71–87). Singapore, Singapore: Springer.

    Chapter  Google Scholar 

  • Siemens, G. (2010). 1st international conference on learning analytics & knowledge 2011. Retrieved from https://tekri.athabascau.ca/analytics/

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

    Article  Google Scholar 

  • Smith, G. (2007). How does student performance on formative assessments relate to learning assessed by exams? Journal of College Science Teaching, 36(7), 28–34.

    Google Scholar 

  • Sønderlund, A. L., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594–2618. https://doi.org/10.1111/bjet.12720

    Article  Google Scholar 

  • Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467–481. https://doi.org/10.1007/s10734-017-0220-3

    Article  Google Scholar 

  • Tolstrup Holmegaard, H., Møller Madsen, L., & Ulriksen, L. (2017). Why should European higher education care about the retention of non-traditional students? European Educational Research Journal, 16(1), 3–11. https://doi.org/10.1177/1474904116683688

    Article  Google Scholar 

  • Tsai, Y.-S., & Gašević, D. (2017). Learning analytics in higher education – Challenges and policies: A review of eight learning analytics policies. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 233–242). New York, NY: ACM.

    Chapter  Google Scholar 

  • Van Horne, S., Curran, M., Smith, A., VanBuren, J., Zahrieh, D., Larsen, R., & Miller, R. (2018). Facilitating student success in introductory chemistry with feedback in an online platform. Technology, Knowledge and Learning, 23, 21–40. https://doi.org/10.1007/s10758-017-9341-0

    Article  Google Scholar 

  • van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2014). Supporting teachers in guiding collaborating students: Effects of learning analytics in CSCL. Computers and Education, 79, 28–39. https://doi.org/10.1016/j.compedu.2014.07.007

    Article  Google Scholar 

  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.

    Article  Google Scholar 

  • Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499–1514.

    Google Scholar 

  • Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133–148.

    Google Scholar 

  • Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027

    Article  Google Scholar 

  • Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119–135. https://doi.org/10.1016/j.compedu.2018.03.018

    Article  Google Scholar 

  • Volet, S., Vauras, M., Salo, A.-E., & Khosa, D. (2017). Individual contributions in student-led collaborative learning: Insights from two analytical approaches to explain the quality of group outcome. Learning and Individual Differences, 53, 79–92. https://doi.org/10.1016/j.lindif.2016.11.006

    Article  Google Scholar 

  • Webb, M., Gibson, D., & Forkosh-Baruch, A. (2013). Challenges for information technology supporting educational assessment. Journal of Computer Assisted Learning, 29, 451–462. https://doi.org/10.1111/jcal.12033

    Article  Google Scholar 

  • Webb, M., & Ifenthaler, D. (2018). Assessment as, for, and of twenty-first-century learning using information technology: An overview. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), Handbook of information technology in primary and secondary education. Cham, Switzerland: Springer.

    Google Scholar 

  • Webb, M., Prasse, D., Philipps, M., Kadijevich, D., Angeli, C., Strijker, A., … Laugesen, H. (2018). Challenges for IT-enabled formative assessment of complex 21st century skills. Technology, Knowledge and Learning, 23, 441–456. https://doi.org/10.1007/s10758-018-9379-7.

  • Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573.

    Article  Google Scholar 

  • Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M. C. Wittrock (Ed.), Handbook of research on teaching (pp. 315–327). New York, NY: Macmillan.

    Google Scholar 

  • West, D., Heath, D., & Huijser, H. (2016). Let’s talk learning analytics: A framework for implementation in relation to student retention. Online Learning, 20(2), 1–21.

    Google Scholar 

  • Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37, 3–14. https://doi.org/10.1016/j.stueduc.2011.03.001

    Article  Google Scholar 

  • Wiliam, D., & Black, P. (1996). Meanings and consequences: A bias for distinguishing formative and summative functions of assessment? British Educational Research Journal, 22(5), 537–548.

    Article  Google Scholar 

  • Wiliam, D., & Thompson, M. (2008). Integrating assessment with learning: What will it take to make it work? In C. A. Dwyer (Ed.), The future of assessment. Shaping teaching and learning. New York, NY: Lawrence Erlbaum Associates.

    Google Scholar 

  • Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991–1007.

    Article  Google Scholar 

  • Wilson, J., & Andrada, G. N. (2015). Using automated feedback to improve writing quality: Opportunities and challenges. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of research on technology tools for real-world skill development (pp. 678–703). Hershey, PA: IGI Global.

    Google Scholar 

  • Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Wingate, U. (2006). Doing away with ‘study skills’. Teaching in Higher Education, 11(4), 457–469. https://doi.org/10.1080/13562510600874268

    Article  Google Scholar 

  • Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). New York, NY: Routledge.

    Google Scholar 

  • Winne, P. H. (2017a). Cognition and metacognition within self-regulated learning. In P. A. Alexander, D. H. Schunk, & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 36–48). New York, NY: Routledge.

    Chapter  Google Scholar 

  • Winne, P. H. (2017b). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 241–249). SOLAR, Society for Learning Analytics and Research. https://www.solaresearch.org/wp-content/uploads/2017/05/hla17.pdf

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

    Google Scholar 

  • Wong, J., Baars, M., de Koning, B. B., van der Zee, T., Davis, D., Khalil, M., … Paas, F. (2019). Educational theories and learning analytics: From data to knowledge. The whole is greater than the sum of its parts. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing learning analytics to support study success (pp. 3–25). Cham, Switzerland: Springer.

    Google Scholar 

  • Xiong, W., Litman, D., & Schunn, C. (2012). Natural language processing techniques for researching and improving peer feedback. Journal of Writing Research, 4(2), 155–176.

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic Press.

    Chapter  Google Scholar 

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Schumacher, C. (2020). Linking Assessment and Learning Analytics to Support Learning Processes in Higher Education. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_166-1

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