Designing Automated Systems for Learning Analysis

In the context of the e-FRAN program, our research focused on engineering freshmen’s learning processes of 3-D modeling in relation to their spatial skills. One of the approaches developed consisted in identifying the students’ sequences of micro operations using a double observation of their actions by recording their on-screen activity, and filming their gestures and postures. The objective consisted in determining the logical sequences of both their bodily attitudes, which are related to their manipulation of the software, and their conceptual approaches, which are linked to the results they obtained by interacting with the software. This preliminary characterization aimed to establish the possible recurring patterns in human behavior in the context studied, to then determine the robustness of the codings with regard to the software functionalities, to finally identify characteristic sequences of operations. The later phase consisted in feeding a parser with the encoded traces left by the subjects’ interactions with the software interface, using a heuristic library of actions. The objective was to identify sequences indicative of efficient, or non-efficient, conceptual processes in the task performance. The encoded operations logs revealed recurring sequences, which were then processed statistically to identify prototypes of rationality of intentions. The encoded sequences were submitted to a further manual encoding to identify strategies that arose at the students’ first encounter with the modeling interface.