| Sumario: | Background: Technological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing timely interventions. Objectives: While most learning analytics studies focus on using LMS data to predict performance, this study instead predicts students' self‐reported time‐management skills using trace data from the Canvas LMS. This is done for courses at one Dutch technical university with in total 462 students. Methods: Linear regression and multi‐level regression models are constructed using both theory and findings from previous research. The predictions made by these models are compared to previously filled in questionnaire data to validate the results. Results: Our results show that models can be constructed and time‐management can be predicted for individual courses. Furthermore, there are several predictors that are significant in multiple models. However, these models and predictions are not immediately transferable to other courses. Conclusions: The study therefore emphasizes the need for further research, using multiple sources of data or more theoretically grounded predictors, to investigate the extent of the portability issues with these predictive models. Despite this we were able to predict the students' self‐reported time‐management skills in multiple different courses using Learning Analytics, and managed to identify multiple consistently predictive trace data variables. Lay Description: What is currently known: Learning analytics is mainly used to predict academic performance.Models can be constructed for single courses that are able to predict students' time‐management skills with a high accuracyIt is unclear whether or not models predicting time‐management for one course can be used for other courses as well. What do we add: We predict time‐management skills for multiple different courses.We identify multiple predictors that are consistently predictive of time‐management.We identify a possible portability problem in predictive models. What are the implications of our findings: For some courses, predictive models can be used to identify students with lower time‐management skills. These can constitute the target group for further educational support.For other courses, teachers can use predictive models to identify students who should be monitored more closely using additional means to decide whether they constitute the target group for further support.Course design needs to be taken into account when constructing a predictive model.
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