Descriptive Analytics in an Undergraduate Mathematics Education MOOC Course at a University of Technology: A Review of the Algebra Component

R. Naidoo
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Abstract

The study explores the learning of algebra in Mathematics 101 offered as Massive Open Online Courses (MOOCs) by using descriptive learning analytics. Delineated benefits of utilising learning analytics include improving course offerings, student outcomes, curriculum development and instructor effectiveness. Quantitative analysis was performed on overall mathematics scores for the population of 158 students. Qualitative analyses were performed on 40 randomly selected students’ examination responses to 11 algebra itemised questions to determine if deep, intermediate or surface learning had taken place. The results indicated 63 students passed the overall Mathematics 101 course but only 37 students passed the algebra section of the examination. The qualitative analysis exhibited four items of deep learning, one item of intermediate learning and six items of surface learning. The quantitative and qualitative analyses indicate that a review of the learning material and online pre-test and post-test data is necessary. Improvement of the discussion forum and tracking of students’ responses should be frequently monitored by online tutors. It is recommended that a community of inquiry model be established within the ODL context and in discussion forums so that student errors are timeously diagnosed.
描述分析在一所科技大学的本科数学教育MOOC课程:对代数部分的回顾
本研究利用描述性学习分析方法探讨了大规模在线开放课程《数学101》中代数的学习。利用学习分析的好处包括改善课程设置、学生成绩、课程开发和教师效率。对158名学生的总体数学成绩进行了定量分析。对40名随机选择的学生进行了定性分析,以确定他们是否进行了深度、中级或表面学习。结果显示,有63名学生通过了数学101的全部课程,但只有37名学生通过了考试的代数部分。定性分析显示深度学习有4个项目,中级学习有1个项目,表层学习有6个项目。定量和定性分析表明,对学习材料和在线测前和测后数据的回顾是必要的。在线导师应该经常监控论坛的改进和学生反馈的跟踪。建议在ODL上下文中和论坛中建立一个查询社区模型,以便及时诊断学生的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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