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(959) APPLYING DEDICATED PSYCHOLOGICAL QUESTIONNAIRES VS EDUCATIONAL DATA MINING TO IDENTIFY STUDENTS LEARNING STYLES

机译:(959)申请专用心理调查问卷与教育数据挖掘以识别学生学习风格

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The paper aims to analyse application of dedicated psychological questionnaires and educational data mining (EDM) to identify students’ learning styles and thus to create conditions to personalise learning. Dedicated psychological questionnaires could help us to establish individual probabilistic suitability indexes for each analysed student and each learning activity in e.g. Virtual Learning Environment (VLE) to identify which learning activities are the most suitable for particular student. Students’ learning styles-based probabilistic suitability index shows the level of suitability of given learning content, activity or environment to particular student. The higher is probabilistic suitability index the better learning activity fits particular student’s needs. Using appropriate EDM methods and techniques, we could analyse what particular learning activities (and appropriate VLE tools) were practically used by these students earlier, and to what extent. After that, the data on practical use of VLE-based learning activities or tools should be compared with students’ probabilistic suitability indexes. In the case of any noticeable discrepancies, students’ profiles and accompanied probabilistic suitability indexes should be identified more precisely, and students’ personal leaning paths in VLE should be corrected according to new identified data. Thus, using EDM, we could noticeably enhance students’ learning quality and effectiveness. In the paper, first of all, related research review is provided. Second, methodology to personalise learning using both dedicated psychological questionnaires and educational data mining methods and techniques to identify students’ learning styles is presented. Third, some real-life examples of applying both methods using Felder-Silverman Learning Styles Model are presented. The paper is concluded by the statement that the best way to exactly identify students’ learning styles is consistent application of both dedicated psychological questionnaires and educational data mining.
机译:本文旨在分析专门的心理调查问卷和教育数据挖掘(EDM)来识别学生的学习方式,从而创造个性化学习的条件。专业的心理调查问卷可以帮助我们为每位分析的学生和每次学习活动建立个性的概率适宜性指标。虚拟学习环境(VLE)以确定哪些学习活动最适合特定的学生。学生的学习款式的概率适宜性指数显示给特定学生给予学习内容,活动或环境的适用性水平。概率较高的概率适用性指数越高,学习活动更好地适合学生的需求。使用适当的EDM方法和技术,我们可以分析这些学生的特定学习活动(和适当的VLE工具),这些学生几乎是在多大程度上使用的。之后,应将基于VLE的学习活动或工具的实际使用数据与学生的概率适当指标进行比较。在任何明显的差异的情况下,应更准确地确定学生的简档和伴随概率适用性指标,并且应根据新的已识别数据纠正学生在VLE中的个人倾斜路径。因此,使用EDM,我们可以明显增强学生的学习质量和有效性。本文首先,提供了相关的研究审查。其次,介绍了使用专用的心理调查问卷和教育数据挖掘方法和技术来个性化学习的方法,以确定学生的学习风格。第三,介绍了使用Felder-Silverman学习风格模型应用两种方法的一些现实生活示例。本文的陈述是究竟识别学生学习风格的最佳方式,这是专门的心理问卷和教育数据挖掘的一致应用。

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