Skip to main content

Advertisement

SpringerLink
Log in

Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines

  • Work-in-progress
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. The purpose of the two reported cases studies is to identify alternative approaches to data analysis and to determine the validity and accuracy of a learning analytics framework and its corresponding student and learning profiles. The findings indicate that educational data for learning analytics is context specific and variables carry different meanings and can have different implications across educational institutions and area of studies. Benefits, concerns, and challenges of learning analytics are critically reflected, indicating that learning analytics frameworks need to be sensitive to idiosyncrasies of the educational institution and its stakeholders.

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.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Aflalo, E., & Gabay, E. (2012). An information system for dropout prevention. Education and Information Technologies, 17(2), 233–250. doi:10.1007/s10639-011-9156-x.

    Article  Google Scholar 

  • Allen, C. B., Higgs, Z. R., & Holloway, J. R. (1988). Identifying students at risk for academic difficulty. Journal of Professional Nursing, 4(2), 113–118. doi:10.1016/S8755-7223(88)80033-4.

    Article  Google Scholar 

  • Ashby, F. G. (Ed.). (1992). Multidimensional models of perception and cognition. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (2005). Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science, 33(5–6), 381–412.

    Article  Google Scholar 

  • Bartholomew, D. J. (1967). Stochastic models for social processes. New York: Wiley.

    Google Scholar 

  • Bauer, R. (1966). Social indicators. Cambridge, MA: MIT Press.

    Google Scholar 

  • Brabrand, C., & Dahl, B. (2009). Using the SOLO taxonomy to analyze competence progression of university science curricula. Higher Education, 58(4), 531–549. doi:10.1007/s10734-009-9210-4.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324.

    Article  Google Scholar 

  • Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology and Society, 15(3), 3–26.

    Google Scholar 

  • Campbell, J. P., DeBlois, P. B., & Oblinger, D. (2010). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57.

    Google Scholar 

  • Christmann, A., & Steinwart, I. (2008). Support vector machines. New York: Springer.

    Google Scholar 

  • Cleophas, T. J., & Zwinderman, A. H. (2013). Support vector machines. Machine learning in medicine (pp. 155–161). Amsterdam: Springer.

    Chapter  Google Scholar 

  • Coates, H. (2009). What’s the difference? A model for measuring the value added by higher education in Australia. Higher Education Management and Policy, 21(1), 69–88.

    Article  Google Scholar 

  • Coates, H. (2010). Defining and monitoring standards in Australian higher education. Higher Education Management and Policy, 22(1), 41–58.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. doi:10.1007/bf00994018.

    Google Scholar 

  • Crosling, G., Heagney, M., & Thomas, L. (2009). Improving student retention in higher education. Australian Universities’ Review, 51(2), 9–18.

    Google Scholar 

  • d’Aquin, M., Dietze, S., Herder, E., Drachsler, H., & Taibi, D. (2014). Using linked data in learning analytics. E-Learning Papers, 36, 1–9.

    Google Scholar 

  • da Silva, J. L., Caeiro, F., Natário, I., & Braumann, C. A. (2013). Advances in regression, survival analysis, extreme values, markov processes and other statistical applications. Berlin: Springer.

    Book  Google Scholar 

  • Dawson, S., Macfadyen, L., Lockyer, L., & Mazzochi-Jones, D. (2011). Using social network metrics to assess the effectiveness of broad-based admission practices. Australasian Journal of Educational Technology, 27(1), 16–27.

    Google Scholar 

  • Dobozy, E., & Ifenthaler, D. (2014). Initial teacher education by open and distance modes: A snapshot of e-competency experiences in Australia. eLearning Papers, 38, 43–54.

  • Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems 9 (pp. 155–161). Cambridge, MA: MIT Press.

    Google Scholar 

  • Fenwick, L., & Cooper, M. (2012). Prevailing pedagogies for classes in low SES contexts and the implications for standards-based reform in Australia. The Australian Educational Researcher, 39(3), 349–361. doi:10.1007/s13384-012-0066-8.

    Article  Google Scholar 

  • Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317. doi:10.1504/IJTEL.2012.051816.

    Article  Google Scholar 

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

    Google Scholar 

  • Ifenthaler, D. (in press). Learning analytics. In J. M. Spector (Ed.), Encyclopedia of educational technology. Thousand Oaks, CA: Sage.

  • Ifenthaler, D., & Pirnay-Dummer, P. (2011). States and processes of learning communities: Engaging students in meaningful reflection and elaboration. In B. White, I. King, & P. Tsang (Eds.), Social media tools and platforms in learning environments: Present and future (pp. 81–94). New York: Springer.

    Chapter  Google Scholar 

  • Ifenthaler, D., Pirnay-Dummer, P., & Seel, N. M. (Eds.). (2010). Computer-based diagnostics and systematic analysis of knowledge. New York: Springer.

    Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2011). A longitudinal perspective on inductive reasoning tasks. Illuminating the probability of change. Learning and Instruction, 21(4), 538–549. doi:10.1016/j.learninstruc.2010.08.004.

    Article  Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2013). Model-based reasoning. Computers and Education, 64, 131–142. doi:10.1016/j.compedu.2012.11.014.

    Article  Google Scholar 

  • James, R., Krause, K.-L., & Jennings, C. (2010). The first-year experience in Australian universities: Findings from 1994 to 2009. Melbourne, VIC: Centre for the Study of Higher Education.

    Google Scholar 

  • Johnson, L., Adams Becker, S., Cummins, M., Freeman, A., Ifenthaler, D., & Vardaxis, N. (2013). Technology outlook for Australian tertiary education 2013–2018: An NMC horizon project regional analysis. Austin, TX: The New Media Consortium.

    Google Scholar 

  • Kalyuga, S. (2006). Assessment of learners’ organised knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20, 333–342.

    Article  Google Scholar 

  • Koggalage, R., & Halgamuge, S. (2004). Reducing the number of training samples for fast support vector machine classification. Neural Information Processing-Letters and Reviews, 2(3), 57–65.

  • Lin, C. F., Yeh, Y.-C., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers and Education, 68, 199–210. doi:10.1016/j.compedu.2013.05.009.

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

    Article  Google Scholar 

  • Long, P. D., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.

    Google Scholar 

  • Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers and Education, 54(2), 588–599.

    Article  Google Scholar 

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

    Google Scholar 

  • Perumallaa, C., Maka, J., Keea, N., & Matthewsa, S. (2010). Integrating web applications to provide an effective distance online learning environment for students. Procedia Computer Science, 3, 770–784. doi:10.1016/j.procs.2010.12.127.

    Article  Google Scholar 

  • Pirnay-Dummer, P., & Ifenthaler, D. (2011a). Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks. Instructional Science, 39(6), 901–919. doi:10.1007/s11251-010-9153-2.

    Article  Google Scholar 

  • Pirnay-Dummer, P., & Ifenthaler, D. (2011b). Text-guided automated self assessment: A graph-based approach to help learners with ongoing writing. In D. Ifenthaler, P. I. Kinshuk, D. G. Sampson, & J. M. Spector (Eds.), Multiple perspectives on problem solving and learning in the digital age (pp. 217–225). New York: Springer.

    Chapter  Google Scholar 

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. doi:10.1023/A:1022643204877.

    Google Scholar 

  • Robinson, R. (2004). Pathways to completion: Patterns of progression through a university degree. Higher Education, 47(1), 1–20. doi:10.1023/B:HIGH.0000009803.70418.9c.

    Article  Google Scholar 

  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. D. (Eds.). (2011). Handbook of educational data mining. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Schreurs, B., de Laat, M., Teplovs, C., & Voogd, S. (2014). Social learning analytics applied in a MOOC-environment. e-Learning Papers, 26, 45–48.

    Google Scholar 

  • Slade, S., & Prinsloo, P. (in press). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, doi:10.1177/0002764213479366.

  • Thomas, L. (2011). Engaging students to improve retention and success. In L. Thomas, & M. Tight (Eds.), Institutional transformation to engage a diverse student body (Vol. 6, pp. 41–55, International perspectives on higher education research). Bingley: Emerald Group Publishing Limited.

  • Thompson, K., Ashe, D., Carvalho, L., Goodyear, P., Kelly, N., & Parisio, M. (in press). Processing and visualizing data in complex learning environments. American Behavioral Scientist, doi:10.1177/0002764213479368.

  • Tinto, V. (1982). Limits of theory and practice I student attrition. The Journal of Higher Education, 53(6), 687–700.

    Article  Google Scholar 

  • Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA Journal, 19(2), 5–9. doi:10.12930/0271-9517-19.2.5.

  • Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.

  • Williams, G. (2011). Support vector machines. In Data mining with Rattle and R (pp. 293-304, Use R). New York: Springer.

Download references

Author information

Authors and Affiliations

  1. Deakin University, Melbourne, Australia

    Dirk Ifenthaler

  2. Open Universities Australia, Melbourne, Australia

    Chathuranga Widanapathirana

Authors
  1. Dirk Ifenthaler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chathuranga Widanapathirana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dirk Ifenthaler.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ifenthaler, D., Widanapathirana, C. Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines. Tech Know Learn 19, 221–240 (2014). https://doi.org/10.1007/s10758-014-9226-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-014-9226-4

Keywords

Search

Navigation