Dimension Extraction Analysis Of Student Performance On Problems

We present a preliminary investigation into applying dimension extraction methods from coevolutionary algorithm theory to the analysis of student-problem performance in a computer programming instruction context. Specifically, we explore using the dimension extraction coevolutionary algorithm (DECA) from revolution and co-optimization theory (Bucci 2007), which identifies structural relationships amongst learners and tests by constructing a geometry encoding how learner performance can be distinguished in fundamentally different ways. While DECA was developed for software learners and tests, its foundational ideas can in principle be applied to data generated by human students taking real tests. Here we apply DECA\u27s dimension-extraction algorithm to student-problem data from four semesters of an introduction to programming course where students used an online software tutor to solve a number of predesigned problems. Dimension extraction reveals structures (dimensions) that partially align with the concepts originally designed into the problems. Preliminary results suggest the structure DECA reveals is consistent when the set of students is varied