Handbook of Learning Analytics

Aguiar, E., Lakkaraju, H., Bhanpuri, N., Miller, D., Yuhas, B., & Addison, K. L. (2015). Who, when, and why: A machine learning approach to prioritizing students at risk of not graduating high school on time. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 93–102). New York: ACM.

Alhadad, S., Arnold, K., Baron, J., Bayer, I., Brooks, C., Little, R. R., Rocchio, R. A., Shehata, S., & Whitmer, J. (2015, October 7). The predictive learning analytics revolution: Leveraging learning data for student success. Technical report, EDUCAUSE Center for Analysis and Research.

Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/

Baker. R. S. J. d. (2007). Modeling and understanding students’ on-task behaviour in intelligent tutoring systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ʼ07), 28 April–3 May 2007, San Jose, CA (pp. 1059–1068). New York: ACM.

Baker, R. S. J. d., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). On-task behaviour in the cognitive tutor classroom: When students game the system. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ʼ04), 24–29 April 2004, Vienna, Austria (pp. 383–390). New York: ACM.

Baker, R. S. J. d., Gowda, S. M., & Corbett, A. T. (2011). Towards predicting future transfer of learning. Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED ʼ11), 28 June–2 July 2011, Auckland, New Zealand (pp. 23–30). Lecture Notes in Computer Science. Springer Berlin Heidelberg.

Barber, R., & Sharkey, M. (2012). Course correction: Using analytics to predict course success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 259–262). New York: ACM. doi:10.1145/2330601.2330664

Brooks, C., Thompson, C., & Teasley, S. (2015). A time series interaction analysis method for building predictive models of learners using log data. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 126–135). New York: ACM.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.

D’Mello, S. K., Craig, S. D., Witherspoon, A., McDaniel, B., & Graesser, A. (2007). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1–2), 45–80.

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009).The Weka data mining software: An update. SIGKDD Explorations Newsletter, 11(1), 10–18. doi:10.1145/1656274.1656278.

Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333–353.

Lonn, S., & Teasley, S. D. (2014). Student explorer: A tool for supporting academic advising at scale. Proceedings of the 1st ACM Conference on Learning @ Scale (L@S 2014), 4–5 March 2014, Atlanta, Georgia, USA (pp. 175–176). New York: ACM. doi:10.1145/2556325.2567867

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. doi:10.1214/10-STS330

Stripling, J., Mangan, K., DeSantis, N., Fernandes, R., Brown, S., Kolowich, S., McGuire, P., & Hendershott, A. (2016, March 2). Uproar at Mount St. Mary’s. The Chronicle of Higher Education. http://chronicle.com/specialreport/Uproar-at-Mount-St-Marys/30.

Taylor, C., Veeramachaneni, K., & O’Reilly, U.-M. (2014, August 14). Likely to stop? Predicting stopout in massive open online courses. http://dai.lids.mit.edu/pdf/1408.3382v1.pdf

Wang, Y., Heffernan, N. T., & Heffernan, C. (2015). Towards better affect detectors: Effect of missing skills, class features and common wrong answers. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 31–35). New York: ACM.

Whitehill, J., Williams, J. J., Lopez, G., Coleman, C. A., & Reich, J. (2015). Beyond prediction: First steps toward automatic intervention in MOOC student stopout. In O. C. Santos et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM2015), 26–29 June 2015, Madrid, Spain (pp. XXX–XXX). International Educational Data Mining Society. http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_112.pdf

Witten, I. H. (2016). Weka courses. The University of Waikato. https://weka.waikato.ac.nz/explorer

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques, 3rd ed. San Francisco, CA: Morgan Kaufmann Publishers.

Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low-hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119–129.

Xing, W., & Goggins, S. (2015). Learning analytics in outer space: A hidden naive Bayes model for automatic students’ on-task behaviour detection. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 176–183). New York: ACM.