This case study explored the potential applications of data mining in the educational program evaluation of online professional development workshops for pre K-12 teachers. Multiple data mining analyses were implemented in combination with traditional evaluation instruments and student outcomes to determine learner engagement and more clearly understand the relationship between logged activities and learner experiences. Data analysis focused on the following aspects: 1) Shared learning characteristics, 2) frequent learning paths, 3) engagement prediction, 4) expectation prediction, 5) workshop satisfaction prediction, and 6) instructor quality prediction. Results indicated that interaction and engagement were important factors in learning outcomes for this workshop. In addition, participants who had online teaching experience could be expected to have a higher engagement level but prior online learning experience did NOT show a similar relationship.
This document was originally published in the International Journal of Technology in Teaching & Learning by the Society of International Chinese in Educational Technology (SICET). Copyright restrictions may apply.
Rice, Kerry and Hung, Jui-Long. (2015). "Data Mining in Online Professional Development Program Evaluation: An Exploratory Case Study". International Journal of Technology in Teaching & Learning, 11(1), 1-20.