Developing an agent-based adaptive system for scaffolding self-regulated inquiry learning in history education

This article presents a methodology for modelling the development of self-regulated learning skills in the context of computer-based learning environments using a combination of tracing techniques. The user-modelling techniques combine statistical and computational approaches to assess skill acquisition, practice, and refinement with the MetaHistoReasoning tool, a single-agent system that supports inquiry-based learning in the domain of history. Data were collected from twenty-two undergraduate students during a 4-h session where user interactions were logged by the system. A logistic regression model predicted user performance in relation to a skill categorization task with 75 % accuracy. The manner in which users apply the skills that are acquired is then assessed through a rule-based reasoning system that allows the pedagogical agent to adapt instruction. The results show that the model allows the agent to detect instances when skills are inappropriately applied as well as what type of goal that is pursued by students. We discuss the implications of these user-modelling techniques in terms of sequencing instructional content and using the tutoring agent to deliver several types of discourse moves in order to enhance learning.