A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments

A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments

Mehdi Najjar (Interdisciplinary Research Center on Emerging Technologies, University of Montreal, Canada)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/978-1-60960-818-7.ch420
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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.
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1. Introduction

Virtual laboratory-based learning is likely to have a profound impact on the whole area of education by affecting the way we learn, what we know and where we acquire knowledge. In the educational community a large amount of enthusiasm has been engendered for highly interactive virtual laboratories, exploiting the multimedia features and the web advantages for a remote teaching purpose and which behave according to laws and constraints of subject-matter domains, permitting the student to experience the nature of those domains through free/guided exploration or scaffolding adaptive learning.

The original notion of scaffolding assumed that a more knowledgeable tutor helps an individual learner, providing him/her with exactly the help s/he needs to move forward. A key element of scaffolding is that the tutor provides appropriate support based on an ongoing diagnosis of the learner’s current level of understanding. This requires that the tutor should not only have a thorough knowledge of the task and its components, the subgoals that need to be accomplished, but should also have knowledge of the student’s capabilities that change as the instruction progresses.

Considerable success has been achieved in the development of software scaffolding that has been employed within interactive learning environments and virtual labs to offer a means of enabling learners to achieve success beyond their own independent ability (Hammerton & Luckin, 2001). Jackson et al. (1996) showed that an approach which attempts to design adaptable learning environments which offer learners guidance and tools to make decisions for themselves also should address the importance of maintaining the fine balance between system guidance and learner control. However, evidence from other researches into learners' use of scaffolding assistance has indicated that less able and knowledgeable learners are ineffective at selecting appropriately challenging tasks and seeking appropriate quantities of support and guidance (Luckin & du Boulay, 1999; Wood, 1999). This led, among others, to explore the way that Vygotsky’s Zone of Proximal Development (Vygotsky, 1986) can be use in the design of learner models.

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