Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

Pi-Shan Hsu (Ching Kuo Institute of Management and Health, Taiwan) and Te-Jeng Chang (National Taiwan Normal University, Taiwan)
Copyright: © 2013 |Pages: 14
DOI: 10.4018/978-1-4666-2032-2.ch015
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Abstract

The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects who attended IC3 Mentor were converted into Characteristic Learning Effort (CLE) curves through the algorithms of learning effort. By evaluating CLE curves and questionnaire survey reports, the findings indicate that the learning effort algorithm is verified to be an effective real-time and non-interfering diagnostic technique. Furthermore, CLE curve is proven to be an effective user-friendly tool for learners and instructors in diagnosing learning progress under adaptive e-learning context. The CLE curve generated by the algorithm of learning effort is a visualized graphic tool which can be applied in the adaptive e-learning platform of education and industry fields.
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Introduction

The level of a learner’s expertise influences mental effort directly according to Cognitive Load Theory (CLT). Therefore, many diagnostic techniques of assessing expertise related factors had been developed over past decades. The literature review of past studies on diagnostic techniques of assessing expertise under static and dynamic conditions was conducted in order to identify the requirements and dimensions of a new dynamic diagnostic technique of assessing expertise in the instructional process of adaptive e-learning. Under adaptive e-learning context a learner’s learning condition is required to be assessed in a dynamic, real-time, and non-interfering approach which is not fulfilled by those diagnostic techniques presented in past studies.

Hence, a new diagnostic technique was designed to be suitable for adaptive e-learning context by improving the imperfections of previous diagnostic techniques. The concept of efficiency has been used by the previous diagnostic technique of assessing expertise. Two indicators of the efficiency, which are effort and performance, have to be assessed in a dynamic, real-time and non-interfering approach in order to achieve a dynamic, real-time and non-interfering based diagnostic technique. Performance was developed as a qualified indicator for a dynamic, real-time and non-interfering based diagnostic technique, but effort has been an unsolved issue according to past studies.

Based on the algorithm of learning effort developed in the new diagnostic technique (Hsu et al., 2009), both indicators of learning efficiency are assessed in a dynamic, real-time and non-interfering approach. Hence, the purpose of this research is to validate the algorithm of learning effort under adaptive e-learning context in order to verify that it is feasible to diagnose learners’ learning effort in a dynamic, real-time, and non-interfering status. An experiment situated adaptive e-learning context was applied on 165 university students in Taiwan. The experimental results were converted into characteristic learning effort curves by the algorithm of learning effort in a dynamic, real-time, and non-interfering approach. The characteristic learning effort curves were evaluated together with the questionnaire survey reports in order to verify the effectiveness of algorithm of learning effort in diagnosing learners’ learning progress in terms of the interaction between learning effort and performance.

Characteristic learning effort curve was proved by this research to be an effective and user-friendly tool in diagnosing learners’ learning progress. Therefore, both learners and instructors are able to diagnose learning progress in a dynamic, real-time and non-interfering process. It supports the real-time communication between learners and instructors to improve learning.

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