Bringing Synchrony and Clarity to Complex Multi-Channel Data: A Learning Analytics Study in Programming Education

Supporting teaching and learning programming with learning analytics is an active area of inquiry. Most data used for learning analytics research comes from learning management systems. However, such systems were not developed to support learning programming. Therefore, educators have to resort to other systems that support the programming process, which can pose a challenge when it comes to understanding students’ learning since it takes place in different contexts. Methods that support the combination of different data sources are needed. Such methods would ideally account for the time-ordered sequence of students’ learning actions. In this article, we use a novel method (multi-channel sequence mining with Hidden Markov Models, HMMs) that allows the combination of multiple data sources, accounts for the temporal nature of students’ learning actions, and maps the transitions between different learning tactics. Our study included 291 students enrolled in a higher education programming course. Students’ trace-log data were collected from the learning management system and from a programming automated assessment tool. Data were analyzed using multi-channel sequence mining and HMM. The results reveal different patterns of students’ approaches to learning programming. High achievers start earlier to work on the programming assignments, use more independent strategies and consume learning resources more frequently, while the low achievers procrastinate early in the course and rely on help forums. Our findings demonstrate the potentials of multi-channel sequence mining and how this method can be analyzed using HMM. Furthermore, the results obtained can be of use for educators to understand students’ strategies when learning programming.