Affiliations: Department of Computer Science, National Chengchi
University, Taiwan. E-mail: [email protected]
Abstract: Composite concepts result from the integration of multiple basic
concepts by students to form highlevel knowledge, so information about how
students learn composite concepts can be used by instructors to facilitate
students' learning, and the ways in which computational techniques
can assist the study of the integration process are therefore intriguing for
learning, cognition, and computer scientists. We provide an exploration of this
problem using heuristic methods, search methods, and machine-learning
techniques, while employing Bayesian networks as the language for representing
the student models. Given experts' expectation about students and
simulated students' responses to test items that were designed for
the concepts, we try to find the Bayesian-network structure that best
represents how students learn the composite concept of interest. The
experiments were conducted with only simulated students. The accuracy achieved
by the proposed classification methods spread over a wide range, depending on
the quality of collected input evidence. We discuss the experimental
procedures, compare the experimental results observed in certain experiments,
provide two ways to analyse the influences of Q-matrices on the experimental
results, and we hope that this simulation-based experience may contribute to
the endeavours in mapping the human learning process.