“细胞”和学习分析:“细胞和生物膜的化学组成”模块中的用户性能分析

A. P. Macedo, E. Galembeck
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引用次数: 0

摘要

介绍:学习分析工具旨在分析数据,使学习过程的理解;它与大规模在线开放课程(MOOC)的结合为研究学习效果带来了机会,一旦MOOC用户产生了可以用来了解他们与内容和环境之间关系的数字足迹。目的:分析“细胞化学成分和生物膜”模块中“细胞”用户的表现,旨在找出可能影响其表现的主要点。材料和方法:“细胞和生物膜的化学成分”是慕课“细胞”的一个模块,这是一门由Unicamp教育技术实验室开发的3D细胞虚拟模型课程。该模块由文本内容、挑战、训练游戏、考核四个部分组成。“The Cell”有一个嵌入式统计分析模块(SAM),这是一个收集数据的LA工具,并有专注于学生表现和学习过程的分析工具。结果与讨论:2015年11月至2018年11月,SAM在模块中注册了2858次交互,涉及1114名用户。进行训练游戏的平均时间为3.45分钟,低于和高于平均时间的用户的平均得分差异显著:分别为54.10分和70.44分。在评估环节中也发现了类似的结果,完成任务更快的用户平均得分为48.83分,而花费更多时间的用户平均得分为52.83分。参加和未参加训练游戏的用户的平均评分分别为51.20分和36.75分。结论:开展活动的平均时间与绩效直接相关;训练游戏作为评估的准备,反映在笔记中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"The Cell" and Learning Analytics: Users Performance Analysis in the Module "Chemical Composition of the Cell and Biological Membranes"
INTRODUCTION: Learning Analytics tools aim to analyze data that allows the understanding of the learning process; its association with Massive Open Online Courses (MOOC) brings an opportunity for research into learning effectiveness, once MOOCs users produces digital footprints that can be used to understand their relationship with the content and environment. OBJECTIVES: To analyze the performance of users of "The Cell" in the module "Cell Chemical Composition and Biological Membranes" aiming to identify the major points that may have influenced their performance. MATERIALS AND METHODS: "Chemical Composition of the Cell and Biological Membranes" is a module of the MOOC "The Cell", a course designed into a 3D cell virtual model developed by the Laboratory of Educational Technologies of the Unicamp. The module is composed of textual content, challenge, training game, and assessment. "The Cell" has an embedded Statistical Analysis Module (SAM), an LA tool that collects data and has analysis tools focused on the student’s performance and learning process. RESULTS AND DISCUSSION: Between November of 2015 and November of 2018, SAM has registered 2858 interactions involving 1114 users in the module. The average time to perform the training game was 3.45 minutes, and there was a significant difference in the scores average comparing users who did the activity below and above the average time: 54.10 and 70.44 points, respectively. A similar result was noticed for the assessment session, users who complete the task faster scored 48.83 on average, while users who take more time scored 52.83 on average. The average score in the assessment of users who did or did not took the training game was 51.20 and 36.75, respectively. CONCLUSION: The average time spent to carry out the activities is directly related to performance; the training game acts as a preparation for the assessment, reflected in the note.
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