Research Team: Sepehr Sabeti, Omidreza Shoghli, Glenda Mayo
The number of students taking online courses continues to grow significantly in the US, and academic leaders are optimistic about the learning process in the online delivery of the courses. In response to this need, advanced versions of learning management systems have become available with improved features that provide intuitive and efficient user interfaces, data tracking capabilities, and compatibility with portable devices. At the same time, they can easily house several types of learning material and keep track of users' engagement. These platforms make several data points on students' behavior readily available to educators. The overall purpose of this study is to transform the data obtained from learning management systems, course video management tools, and socio-demographic background of students to predict students’ performance to provide valuable early learner-centered feedback. The timely prediction-based feedback is expected to increase students’ engagement and mobilize them upward in performance clusters (e.g., from low-performing to medium-performing cluster). For this purpose, in this study, we evaluate the effectiveness of the newly redesigned Building Information Modeling (BIM) course with the incorporation of short instructor-created videos and develop an algorithm to predict the students’ final performance given the early-in-semester data of their online activities.
Building Information Modeling (BIM) | Fall 2020
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