A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments

Abstract

Despite a growing development of virtual laboratories which use the advantages of multimedia and the Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically tailored to each student needs. The virtual teaching process would be well adapted if an artificial tutor (integrated into the lab) could identify the correct acquired knowledge. The training approach could be more personalised if the tutor is also able to recognise the erroneous learner’s knowledge and to suggest a suitable sequence of pedagogical activities to improve significantly the level of the student. This chapter proposes a knowledge representation model which judiciously serves the remediation process to students’ errors during learning activities via a virtual laboratory. The chapter also presents a domain knowledge generator authoring tool which attempts to offer a user-friendly environment that allows modelling graphically any subject-matter domain knowledge according to the proposed knowledge representation and remediation approach. The model is inspired by artificial intelligence research on the computational representation of the knowledge and by cognitive psychology theories that provide a fine description of the human memory subsystems and offer a refined modelling of the human learning processes. Experimental results, obtained thanks to practical tests, show that the knowledge representation and remediation model facilitates the planning of a tailored sequence of feedbacks that considerably help the learner.