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Utilizing learning analytics in course design: voices from instructional designers in higher education

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Abstract

Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers’ perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education regarding factors influencing their intent and actual practice of LA approach in course design practice, based on analysis of multiple strategies such as focus group, individual, and email interviews. Most instructional designers admitted LA had great potential, but adoption was limited. Their perception, intention, and the current state of adoption are affected by individual differences, system characteristics, social influence, and facilitating conditions. Findings have imperative implications for promoting effective implementation of LA approach in higher education.

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References

  • Abdous, M., He, W., & Yen, C. J. (2012). Using data mining for predicting relationships between online question theme and final grade. Educational Technology and Society, 15(3), 77–88.

    Google Scholar 

  • Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: A systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4), 567–598. https://doi.org/10.1007/s10758-019-09408-7.

    Article  Google Scholar 

  • Al-Alak, B. A., & Alnawas, I. A. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management and E-Learning, 3(2), 201–221.

    Google Scholar 

  • Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130–148. https://doi.org/10.1016/j.compedu.2012.10.023.

    Article  Google Scholar 

  • Arnold, K. E. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1). http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/SignalsApplyingAcademicAnalyti/199385.

  • Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report, 13(4), 544–559.

    Google Scholar 

  • Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541–556. https://doi.org/10.1016/j.compeleceng.2017.03.005.

    Article  Google Scholar 

  • Casey, K., & Azcona, D. (2017). Utilizing student activity patterns to predict performance. International Journal of Educational Technology in Higher Education, 14(1), 4. https://doi.org/10.1186/s41239-017-0044-3.

    Article  Google Scholar 

  • Christensen, T. K. (2008). The role of theory in instructional design: Some views of an ID practitioner. Performance Improvement, 47(4), 25–32. https://doi.org/10.1002/pfi.199.

    Article  Google Scholar 

  • Dahlstrom, E., Brooks, D. C., & Bichsel, J. (2014). The current ecosystem of learning management systems in higher education: Student, faculty, and IT perspectives. Louisville CO: EDUCAUSE Research Report.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Davies, R., Nyland, R., Bodily, R., Chapman, J., Jones, B., & Young, J. (2017). Designing technology-enabled instruction to utilize learning analytics. TechTrends, 61(2), 155–161. https://doi.org/10.1007/s11528-016-0131-7.

    Article  Google Scholar 

  • De Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., et al. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https://doi.org/10.1111/bjet.12212.

    Article  Google Scholar 

  • Denley, T. (2014). How predictive analytics and choice architecture can improve student success. Research & Practice in Assessment, 9(2), 61–69.

    Google Scholar 

  • Dietz, B., Hurn, J. E., Mays, T. A., & Woods, D. (2018). An introduction to learning analytics. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and issues in instructional design and technology (4th ed., pp. 104–111). New York: Pearson.

    Google Scholar 

  • Dietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.

    Google Scholar 

  • Dunbar, R. L., Dingel, M. J., & Prat-Resina, X. (2014). Connecting analytics and curriculum design: Process and outcomes of building a tool to browse data relevant to course designers. Journal of Learning Analytics, 1(3), 223–243. https://doi.org/10.18608/jla.2014.13.26.

    Article  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. Educational Technology and Society, 15(3), 58–76. https://doi.org/10.1177/0002764213479367.

    Article  Google Scholar 

  • Elliott, V. (2018). Thinking about coding process in qualitative data analysis. The Qualitative Report, 23(11), 2850–2861.

    Google Scholar 

  • Fasse, R., Humber, J., & Rappold, R. (2009). Rochester Institute of Technology: Analyzing student success. Journal of Asynchronous Learning Networks, 13(3), 37–48.

    Google Scholar 

  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) In higher education institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210–232. https://doi.org/10.12720/joams.4.2.92-97.

    Article  Google Scholar 

  • Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. https://doi.org/10.1504/IJTEL.2012.051816.

    Article  Google Scholar 

  • Firat, M. (2016). Determining the effects of LMS learning behaviors on academic achievement in a learning analytic perspective. Journal of Information Technology Education: Research, 15, 75–87.

    Article  Google Scholar 

  • Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. Internet and Higher Education, 14(2), 89–97. https://doi.org/10.1016/j.iheduc.2010.07.007.

    Article  Google Scholar 

  • Gagné, R. M. (1985). The conditions of learning and theory of instruction (4th ed.). New York, NY: Holt, Rinehart and Winston.

    Google Scholar 

  • Gasevic, 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. The Internet and Higher Education, 28, 68–84. https://doi.org/10.1016/J.IHEDUC.2015.10.002.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Goulding, C. (2005). Grounded theory, ethnography and phenomenology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing, 39(3/4), 294–308. https://doi.org/10.1108/03090560510581782.

    Article  Google Scholar 

  • Gray, B. (2004). Informal learning in an online community of practice. Journal of Distance Education, 19(1), 20–35.

    Google Scholar 

  • Groenewald, T. (2003). Growing talented people through cooperative education: A phenomenological exploration. International Journal of Work-Integrated Learning, 4(2), 49–61.

    Google Scholar 

  • Groenewald, T. (2004). A phenomenological research design illustrated. International Journal of Qualitative Studies in Education, 3(1), 1–26. Retrieved from http://www.ualberta.ca/~iiqm/backissues/3_1/html/groenewald.html.

  • Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, C., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: The teachers’ role and perspective. Educational Technology Research and Development. https://doi.org/10.1007/s11423-019-09685-0.

    Article  Google Scholar 

  • Huang, T. C. K., Liu, C. C., & Chang, D. C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257–270. https://doi.org/10.1016/j.ijinfomgt.2011.11.006.

    Article  Google Scholar 

  • Hung, J. L., Hsu, Y. C., & Rice, K. (2012). Integrating data mining in program evaluation of K-12 online education. Journal of Educational Technology & Society, 15(3), 27–41.

    Google Scholar 

  • Hycner, R. H. (1999). Some guidelines for the phenomenological analysis of interview data. In A. Bryman & R. G. Burgess (Eds.), Qualitative research (Vol. 3, pp. 143–164). London: 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., & 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 

  • Keller, J. M. (1987). Strategies for stimulating the motivation to learn. Performance and Instruction, 26(9), 1–7.

    Google Scholar 

  • Kitto, K., Buckingham Shum, S., & Gibson, A. (2018). Embracing imperfection in learning analytics. In Proceedings of the 8th international conference on learning analytics and knowledge (LAK’18) (pp. 451–460). http://dx.doi.org/10.1145/3170358.3170413

  • Krefting, L. (1991). Rigor in qualitative research: The assessment of trustworthiness. American Journal of Occupational Therapy, 45, 214–222.

    Article  Google Scholar 

  • Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569–579. https://doi.org/10.1016/j.compedu.2012.03.006.

    Article  Google Scholar 

  • Lara, J. A., Lizcano, D., Martínez, M. A., Pazos, J., & Riera, T. (2014). A system for knowledge discovery in e-learning environments within the European Higher Education Area-Application to student data from Open University of Madrid, UDIMA. Computers & Education, 72, 23–36. https://doi.org/10.1016/j.compedu.2013.10.009.

    Article  Google Scholar 

  • Lee, Y. F., Altschuld, J. W., & White, J. L. (2007). Effects of multiple stakeholders in identifying and interpreting perceived needs. Evaluation and Program Planning, 30(1), 1–9. https://doi.org/10.1016/j.evalprogplan.2006.10.001.

    Article  Google Scholar 

  • Leshin, C., Pollock, J., & Reigeluth, C. M. (1992). Instructional design strategies and tactics. Englewood Cliffs, NJ: Educational Technology Publications.

    Google Scholar 

  • Li, Y., Bao, H., & Xu, C. (2017). Learning analytics: Serving the learning process design and optimization. In F.-Q. Lai & J. D. Lehman (Eds.), Learning and knowledge analytics in open education: Selected readings from the AECT-LKAOE 2015 summer international research symposium (pp. 31–40). Cham: Springer. https://doi.org/10.1007/978-3-319-38956-1_4.

    Chapter  Google Scholar 

  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Lockyer, L., & Dawson, S. (2011). Learning designs and learning analytics. Proceedings of the 1st international conference on learning analytics and knowledge-LAK’11 (pp. 153–156). https://doi.org/10.1145/2090116.2090140

  • Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. https://doi.org/10.1177/0002764213479367.

    Article  Google Scholar 

  • Luo, T., Freeman, C., & Stefaniak, J. (2020). “Like, comment, and share”—professional development through social media in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1659–1683.

    Article  Google Scholar 

  • Luo, T., Moore, D., Franklin, T., & Crompton, H. (2019). Applying a modified technology acceptance model to qualitatively analyze the factors affecting microblogging integration in a hybrid course. International Journal of Social Media and Interactive Learning Environments, 6(2), 102–143. https://doi.org/10.1504/IJSMILE.2019.102143.

    Article  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(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008.

    Article  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 & Practice in Assessment, 9(2), 17–28.

    Google Scholar 

  • Mangaroska, K., & Giannakos, M. N. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516–534. https://doi.org/10.1109/TLT.2018.2868673.

    Article  Google Scholar 

  • Morrison, G. R., Ross, S. M., Kalman, H. K., & Kemp, J. E. (2013). Designing effective instruction (7th ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  • Muljana, P. S., Luo, T., Watson, S., Euefueno, W. D., & Jutzi, K. N. W. (2020). Promoting instructional designers’ participation in free, asynchronous professional development: A formative evaluation. Journal of Formative Design in Learning. https://doi.org/10.1007/s41686-020-00044-4.

    Article  Google Scholar 

  • Muljana, P. S., & Placencia, G. (2018). Learning analytics: Translating data into “just-in-time” interventions. Scholarship of Teaching and Learning: Innovative Pedagogy, 1, 50–69. Retrieved from: https://digitalcommons.humboldt.edu/sotl_ip/vol1/iss1/6/.

  • Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703–714.

    Article  Google Scholar 

  • Nunn, S., Avella, J. T., Kanai, T., & Kebritchi, M. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13–29. https://doi.org/10.24059/olj.v20i2.790

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

    Google Scholar 

  • Park, S. Y. (2009). An analysis of the Technology Acceptance Model in understanding university student’s behavioral intention to use e-learning. Educational Technology & Society., 12(3), 150–162.

    Google Scholar 

  • Persico, D., & Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry. British Journal of Educational Technology, 46(2), 230–248. https://doi.org/10.1111/bjet.12207.

    Article  Google Scholar 

  • Phillippi, J., & Lauderdale, J. (2018). A guide to field notes for qualitative research: Context and conversation. Qualitative Health Research, 28(3), 381–388. https://doi.org/10.1177/1049732317697102.

    Article  Google Scholar 

  • Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244. https://doi.org/10.1016/j.compedu.2004.10.007.

    Article  Google Scholar 

  • Reimann, P. (2016). Connecting learning analytics with learning research: The role of design-based research. Learning. Research and Practice, 2(2), 130–142. https://doi.org/10.1080/23735082.2016.1210198.

    Article  Google Scholar 

  • Rienties, B., Nguyen, Q., Holmes, W., & Reedy, K. (2017). A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK. Interaction Design and Architecture (s), 33, 134–154.

    Google Scholar 

  • Ritzhaupt, A., & Kumar, S. (2015). Knowledge and skills needed by instructional designers in higher education. Performance Improvement Quarterly, 28(3), 51–69. https://doi.org/10.1002/piq.21196.

    Article  Google Scholar 

  • Saldaña, J. (2013). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Schumacher, C., & Ifenthaler, D. (2017). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. https://doi.org/10.1016/j.chb.2017.06.030.

    Article  Google Scholar 

  • Shenton, A. K. (2004). Strategies for ensuring strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22(2), 63–75.

    Article  Google Scholar 

  • Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30.

    Google Scholar 

  • Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions. Journal of Asynchronous Learning Networks, 16(3), 51–61.

    Google Scholar 

  • Smith, P. L., & Ragan, T. J. (1993). Instructional design. New York: Macmillan & Co.

    Google Scholar 

  • Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. https://doi.org/10.1016/j.compedu.2015.08.004.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013a). Factors affecting students’ acceptance of e-Learning environments in developing countries: A structural equation modeling approach. International Journal of Information and Education Technology, 3(1), 54–59. https://doi.org/10.7763/IJIET.2013.V3.233.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013b). User acceptance towards web-based learning systems: Investigating the role of social, organizational and individual factors in European higher education. Procedia Computer Science, 17, 189–197. https://doi.org/10.1016/j.procs.2013.05.026.

    Article  Google Scholar 

  • Tessmer, M., & Richey, R. C. (1997). The role of context in learning and instructional design. Educational Technology Research and Development, 45(2), 85–115.

    Article  Google Scholar 

  • Tracey, M. W., & Boling, E. (2014). Preparing instructional designer: Traditional and emerging perspectives. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 653–660). https://doi.org/10.1007/978-1-4614-3185-5_52

  • Trust, T., Carpenter, J. P., & Krutka, D. G. (2017). Moving beyond silos: professional learning networks in higher education. Internet and Higher Education, 35, 1–11. https://doi.org/10.1016/j.iheduc.2017.06.001.

    Article  Google Scholar 

  • Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., & Karakos, A. (2012). A clustering methodology of web log data for learning management systems. Education Technology & Society, 15(2), 154–167.

    Google Scholar 

  • Van Leeuwen, A. (2018). Teachers’ perceptions of the usability of learning analytics reports in a flipped university course: When and how does information become actionable knowledge? Educational Technology and Research Development. https://doi.org/10.1007/s11423-018-09639-y.

    Article  Google Scholar 

  • Venkatesh, V. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x.

    Article  Google Scholar 

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

    Google Scholar 

  • Welman, J. C., & Kruger, S. J. (1999). Research methodology for the business and administrative sciences. Johannesburg: International Thompson.

    Google Scholar 

  • Wiley, K. J., Dimitriadis, Y., Bradford, A., & Linn, M. C. (2020). From theory to action: developing and evaluating learning analytics for learning design. In Proceedings of the tenth international conference on learning analytics & knowledge (LAK’20) (pp. 569–578). https://doi.org/10.1145/3375462.3375540

  • Williams, D. D., South, J. B., Yanchar, S. C., Wilson, B. G., & Allen, S. (2011). How do instructional designers evaluate? A qualitative study of evaluation in practice. Educational Technology Research and Development, 59(6), 885–907. https://doi.org/10.1007/s11423-011-9211-8.

    Article  Google Scholar 

  • Wise, A. F., & Jung, Y. (2019). Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Analytics, 6(2), 53–69. https://doi.org/10.18608/jla.2019.62.4.

    Article  Google Scholar 

  • Wise, A. F., & Vytasek, J. (2017). Learning analytics implementation design. In C. Lang, G. Siemens, A. F. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 151–160). Beaumont, AB: Society for Learning Analytics Research. https://doi.org/10.18608/hla17.013.

    Chapter  Google Scholar 

  • Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181. https://doi.org/10.1016/j.chb.2014.09.034.

    Article  Google Scholar 

  • Yen, C. H., Chen, I., Lai, S. C., & Chuang, Y. R. (2015). An analytics-based approach to managing cognitive load by using log data of Learning Management Systems and footprints of social media. Journal of Educational Technology & Society, 18(4), 141–158.

    Google Scholar 

  • You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003.

    Article  Google Scholar 

  • Zafra, A., & Ventura, S. (2012). Multi-instance genetic programming for predicting student performance in web based educational environments. Applied Soft Computing, 12(8), 2693–2706. https://doi.org/10.1016/j.asoc.2012.03.054.

    Article  Google Scholar 

  • Zinker, J. (1978). Creative process in gestalt therapy. New York: Vintage.

    Google Scholar 

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Acknowledgments

We are grateful for the IDs who willingly participated and shared their insights with us in order to contribute to the academic community. We also thank Dr. Jason Lynch and Dr. Greg Placencia for their invaluable suggestions and insights as we conducted this study and developed this manuscript.

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Correspondence to Pauline Salim Muljana.

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Muljana, P.S., Luo, T. Utilizing learning analytics in course design: voices from instructional designers in higher education. J Comput High Educ 33, 206–234 (2021). https://doi.org/10.1007/s12528-020-09262-y

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