![]() Using the interaction data from students’ Programming environment for introductory computer scienceĮducation. Interactions with programming activities in a block-based Present results from a cluster analysis of student programs from ![]() Prompts and hints, when they face challenges. Instructors to more effectively tailor feedback to students, such as Misconceptions will enable these programming environments and Developing a better understanding of these Investigates the types of misconceptions students might exhibit in Novice programmers, there has been limited work that While theseĮnvironments eliminate many syntax-related errors faced by ![]() In popularity, especially in introductory courses. Programming environments for novice programmers have grown Recent years have seen an increasing interest in identifyingĬommon student misconceptions during introductory Through a case study of two online programming courses (N>600), this paper demonstrates two example applications of DETECT: 1) to identify how cohort behaviour develops over time and 2) to identify student behaviours that characterise exercises where many students give up. DETECT is easy to apply, highly customisable, applicable to a wide range of educational datasets and yields easily interpretable results. The resulting clusters are similar in structure to a decision tree, with a hierarchy of clusters defined by decision rules on features. This paper presents `DETECT' (Detection of Educational Trends Elicited by Clustering Time-series data), a novel divisive hierarchical clustering algorithm that incorporates temporal information into its objective function to prioritise the detection of behavioural trends. ![]() This is because the objective functions used by these methods do not explicitly aim to find cluster trends in time, so these trends may not be clearly represented in the results. However, one important aspect of student behaviour, namely its evolution over time, can often be challenging to identify using existing methods. Techniques for clustering student behaviour offer many opportunities to improve educational outcomes by providing insight into student learning. ![]()
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