Effect of open student models on self-assessment, problem selection and learning

Student modeler, as the central component of Intelligent Tutoring Systems (ITSs), has been formed to assist in systems’ decision making for students’ learning. The ITS can adapt its pedagogical actions to provide personalized learning feedback by analyzing students’ knowledge represented in the student modeler. It is well-known that viewing individual Open Student Models (OSMs) can help students to reflect on their own learning progress and enhance their meta-cognitive skills, such as self-assessment and problem selection. It is also shown that better meta-cognitive skills lead to better learning outcomes. By knowing their strengths and weaknesses in the corresponding domain through inspecting the OSM, students can develop a more effective and efficient way of learning. On one hand, the OSM can provide detailed information about the student state of knowledge. On the other hand, it is important for any instructional method to effectively and efficiently utilize the limited human working memory which directly impacts the design of OSM. It is shown that the performance of instructional systems can drop because of the under load or overload of the learner’s cognitive capacity. Our aim here is to study the effect of the type and amount of information presented to student in their OSM on their learning outcome, self-assessment skills and their problem selection skills as well as the motivation to utilize these meta-cognitive skills. We picked a problem-solving environment called EER-Tutor, which is a web-enhanced ITS that supports university students in learning conceptual database modelling using the Enhanced Entity-Relationship model (EER), as our test bed for our study. We designed a new strategy for presenting information about the student’s progress via OSM and problem selection page, and we evaluated the impact of this new presentation on student’s learning and meta-cognitive skills by running a classroom experiment