Blended learning is gaining ground in contemporary education. However, studies on predictive learning analytics in the context of blended learning remain relatively scarce compared to Massive Open Online Courses (MOOCs), where such applications have gained a strong foothold. Data sets obtained from blended learning environments suffer from a high dimensionality and typically expose a limited number of instances, which makes predictive analysis a challenging task. In this work, we explore the log data of a master-level blended course to predict the students' grades based entirely on the data obtained from an online module (a small private online course), using and comparing logistic regression and random forest-based predictive models. The results of the analysis show that, despite the limited data, success vs. fail predictions can be made as early as in the middle of the course. This could be used in the future for timely interventions, both for failure prevention as well as for reinforcing positive learning behaviours of students.

Sequence classification deals with the task of finding discriminative and concise sequential patterns. To this purpose, many techniques have been proposed, which mainly resort to the use of partial orders to capture the underlying sequences in a database according to the labels. Partial orders, however, pose many limitations, especially on expressiveness, i.e., the aptitude towards capturing certain behavior, and on conciseness, i.e., doing so in a compact and informative way. These limitations can be addressed by using a better representation. In this paper, we present the interesting Behavioral Constraint Miner (iBCM), a sequence classification technique that discovers patterns using behavioral constraint templates. The templates comprise a variety of constraints and can express patterns ranging from simple occurrence, to looping and position-based behavior over a sequence. Furthermore, iBCM also captures negative constraints, i.e., absence of particular behavior. The constraints can be discovered by using simple string operations in an efficient way. Finally, deriving the constraints with a window-based approach allows to pinpoint where the constraints hold in a string, and to detect whether patterns are subject to concept drift. Through empirical evaluation, it is shown that iBCM is better capable of classifying sequences more accurately and concisely in a scalable manner.