Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model
Predicting student performance as early as possible and analysing to which extent initial student behaviour could lead to failure or success is critical in introductory programming (CS1) courses, for allowing prompt intervention in a move towards alleviating their high failure rate. However, in CS1 performance prediction, there is a serious lack of studies that interpret the predictive model’s decisions. In this sense, we designed a long-term study using very fine-grained log-data of 2056 students, collected from the first two weeks of CS1 courses. We extract features that measure how students deal with deadlines, how they fix errors, how much time they spend programming, and so forth. Subs…
Identifying objectives for a learning space management system with value-focused thinking
A classroom with a blackboard and some rows of desks is obsolete in special education. Depending on the needs, some students may need more tactile and inspiring surroundings with various pedagogical accessories while others benefit from a simplified environment without unnecessary stimuli. This understanding is applied to a new Finnish special education school building with open and adaptable learning spaces. We have joined the initiative creation process by developing software support for these new spaces in the form of a learning space management system. Participatory design and value-focused thinking were implemented to elicit the actual values of all the stakeholders involved and transf…