Applying Learning Analytics to Enhance the Technological Pedagogical Content Knowledge of Teachers Teaching Massive Open Online Courses
Over the past few years, massive open online courses (MOOCs) have flourished globally. This study investigated big data-based general education MOOCs developed by its author to learn about e-learning in practice, enhance the technological pedagogical content knowledge of teachers, and determine students’ MOOC passing rates at the end of a semester. The study participants were students who studied in the aforementioned MOOCs in the first semester of academic year 2018-2019. Learning analytics were performed on the learning process of 306 students (which contained 1,871,747 pieces of data) to identify the students’ background variables (i.e., gender, college, and grade level) and determine whether the three “learning behavior” of students (i.e., how much of the educational videos they had finished watching, self-assessment and textbook-viewing strategies that they had adopted, and the diversity of devices that they had used to support their learning) had an effect on their MOOC results at the end of a semester. A chi-square test was conducted and a logistic regression model was used to perform a statistical analysis, where the results showed that college and the three learning behaviors exhibited a pronounced effect on whether the students passed the MOOCs at the end of a semester as well as their passing rates. In addition, this study used harmony scores and ROC curves to test the effectiveness of the student learning model. The results showed a harmony score of 83.2%, signifying high accuracy. In the future, early-warning systems can be developed to elevate students’ academic performance and MOOC passing rates.
|關鍵詞||大規模開放線上課程、科技學科教學知識、學習分析、massive open online courses、technological pedagogical content knowledge、learning analytics|