Ateneo Laboratory for the Learning Sciences

Thesis Title: Transitions Between Affective States and Its Relationship to Learner Performance in an Intelligent Tutoring System
Thesis Abstract:
As affect is associated with cognition and learning, the paper aims to determine how affective states relate to student performance within SQL-Tutor, a constraint-based tutor in Structured Query Language (SQL) from the University of Canterbury, New Zealand. Specifically, the study will analyze relationship between dynamics of affective transitions and learning. Using user action log files and human observation data gathered from MIS students, the researcher intends to determine how an affective state at a given time influences the affective state at a later time. The researcher will find the likely transitions of affective states by mining through the observation files. Its relationship to student performance will then be analyzed using the action log files obtained from the ITS.
Member:
Thea Faye G. Guia

Thesis Abstract:
A non-literal error is defined as an error message given by the compiler that doesn’t correspond to the actual error present in the code. In these cases, debugging code can be a difficult even for seasoned programmers. Continuing recent work on most frequently committed non-literal errors, we plan to improve on the current detection system by integrating manually and automatically generated detection rules into the already existing error detection system. We believe this will enhance the capability of the detector by improving the methods for which it scans code for errors.
Members:
Joshua Bautista
Jaime Anson