Abstract
Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of students’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.
Highlights
In recent years, there has been a growing interest in examining the ways students approach the challenge of learning programming [1,2]
We argue that a holistic approach that is based on the analysis of data originating from several relevant sources to programming education would provide a more accurate view of students’ learning, considering how the temporality of the learning tactics, the interconnectedness of events, and the combination of various learning strategies may influence the outcome of the learning process [21]
A possible explanation might be that some students did not use the automated assessment tool repeatedly during the development but rather resorted to using the tool only before submitting to ensure that they are obtaining the highest score
Summary
There has been a growing interest in examining the ways students approach the challenge of learning programming [1,2]. This topic has drawn the attention of researchers and practitioners due to the high failure rates that programming courses often have [3,4]. Research has consistently shown that effective use and implementation of learning strategies is a consistent predictor of a successful learning process and of academic success. Temporality is embedded in the way programming learning is designed, how learning materials are organized, and how students are assessed. An expanding repertoire of methods has proven valuable, such as process mining and sequence mining [17]
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