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

机译:实时无干扰诊断技术的学习努力算法验证

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摘要

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 platformfulfilling 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.
机译:这项研究的目的是验证学习努力的算法,该算法是一种新的基于实时和无干扰的诊断技术的指标。满足智能导师系统要求的自适应电子学习平台IC3 Mentor被应用于165名大学生。通过学习努力算法,将参加IC3导师的受试者的学习记录转换为特征学习努力(CLE)曲线。通过评估CLE曲线和问卷调查报告,研究结果表明,学习努力算法已被证明是一种有效的实时且无干扰的诊断技术。此外,事实证明,CLE曲线对于学习者和教师在自适应电子学习环境下诊断学习进度而言是一种有效的用户友好工具。通过学习努力算法生成的CLE曲线是一种可视化的图形工具,可以应用于教育和工业领域的自适应电子学习平台。

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