Abstract
One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher- and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model- and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.
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References
Feigenbaum, E.A.: Knowledge engineering: the applied side of artificial intelligence. Ann. New York Acad. Sci. 426, 91–107 (1984)
Ruqian, L.: New Approaches to Knowledge Acquisition. World Scientific Series in Computer Science. World Scientific Pub. Co. Inc. (1994)
Xu, H., Hoffmann, A.: RDRCE: combining machine learning and knowledge acquisition. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS, vol. 6232, pp. 165–179. Springer, Heidelberg (2010)
Lockyer, L., Heathcote, E., Dawson, S.: Informing pedagogical action: aligning learning analytics with learning design. Am. Behav. Sci. 57, 1439–1459 (2013)
Liu, D.Y.T., Froissard, J.-C., Richards, D., Atif, A.: An enhanced learning analytics plugin for Moodle: student engagement and personalised intervention. In: Reiners, T., von Konsky, B.R., Gibson, D., Chang, V., Irving, L., Clarke, K. (eds.) 32nd Conference of the Australasian Society for Computers in Learning in Tertiary Education, Perth (2015)
Liu, D.Y.T., Froissard, J.-C., Richards, D., Atif, A.: Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance. In: 2015 Americas Conference on Information Systems, Puerto Rico (2015)
Dawson, P.: Analytics block to identify students at risk of disengaging. Paper presented at 2012 Moodle Moot, Gold Coast, Queensland, Australia (2012)
Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an “early warning system” for educators: a proof of concept. Comput. Educ. 54, 588–599 (2010)
Romero, C., Ventura, S., García, E.: Data mining in course management systems: Moodle case study and tutorial. Comput. Educ. 51, 368–384 (2008)
Jayaprakash, S.M., Moody, E.W., Lauría, E.J.M., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: An open source analytics initiative. J. Learn. Analytics 1, 6–47 (2014)
Corana, A., Marchesi, M., Martini, C., Ridella, S.: Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm. ACM Trans. Math. Softw. (TOMS) 13, 262–280 (1987)
Cohen, J.: A power primer. Psychol. Bull. 112, 155 (1992)
Schohn, G., Cohn, D.: Less is more: active learning with support vector machines. In: ICML, San Francisco, pp. 839–846 (2000)
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere project: bayesian user modeling for inferring the goals and needs of software users. In: Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256–265. Morgan Kaufmann Publishers Inc. (1998)
Romero, C., Ventura, S., Espejo, P.G., Hervás, C.: Data mining algorithms to classify students. In: 1st International Conference on Educational Data Mining, Montreal, pp. 8–17 (2008)
Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1, 1–23 (1993)
Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM, Vancouver (2012)
Krumm, A.E., Waddington, R.J., Teasley, S.D., Lonn, S.: A learning management system-based early warning system for academic advising in undergraduate engineering. In: Larusson, J.A., White, B. (eds.) Learning Analytics, pp. 103–119. Springer, New York (2014)
Gašević, D., Dawson, S., Rogers, T., Gasevic, D.: Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High. Educ. 28, 68–84 (2016)
Watson, I., Marir, F.: Case-based reasoning: a review. Knowl. Eng. Rev. 9, 327–354 (1994)
Acknowledgements
This work was supported by a Macquarie University Teaching Development Grant. We wish to thank all the staff who worked with us on this study, and our colleagues for many insightful conversations. Initial development and conceptualization of MEAP was supported by a NetSpot Innovation Fund grant, and conducted by a team including Adam Olley, Ashley Holman, Angela Carbone, and others.
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Liu, D.Y.T., Richards, D., Dawson, P., Froissard, JC., Atif, A. (2016). Knowledge Acquisition for Learning Analytics: Comparing Teacher-Derived, Algorithm-Derived, and Hybrid Models in the Moodle Engagement Analytics Plugin. In: Ohwada, H., Yoshida, K. (eds) Knowledge Management and Acquisition for Intelligent Systems . PKAW 2016. Lecture Notes in Computer Science(), vol 9806. Springer, Cham. https://doi.org/10.1007/978-3-319-42706-5_14
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