Learners’ Performance in a MOOC on Programming

Author:

Feklistova LidiaORCID,Lepp MarinaORCID,Luik PiretORCID

Abstract

In every course, there are learners who successfully pass assessments and complete the course. However, there are also those who fail the course for various reasons. One of such reasons may be related to success in assessment. Although performance in assessments has been studied before, there is a lack of knowledge on the degree of variance between different types of learners in terms of scores and the number of resubmissions. In the paper, we analyse the performance in assessments demonstrated by non-completers and completers and by completers with different engagement levels and difficulty-resolving patterns. The data have been gathered from the Moodle statistics source based on the performance of 1065 participants, as regards their completion status, the number of attempts made per each programming task and quiz, and the score received per quiz. Quantitative analysis was performed with descriptive statistics and non-parametric tests. Non-completers and completers were similar in resubmissions per quiz, but the former, expectedly, made more resubmissions per programming task and received lower quiz scores. Completers made more attempts per task than per quiz. They could provide a correct solution with a few resubmissions and receive good scores already at a pragmatic engagement level. At the same time, the increased use of help sources in case of difficulties was also associated with a higher number of attempts and lower quiz scores received. The study may have implications in understanding the role of assessments in dropouts and how completers with different engagement and difficulty-resolving patterns cope with assessments.

Publisher

MDPI AG

Subject

Public Administration,Developmental and Educational Psychology,Education,Computer Science Applications,Computer Science (miscellaneous),Physical Therapy, Sports Therapy and Rehabilitation

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Utilizing the Constrained K-Means Algorithm and Pre-Class GitHub Contribution Statistics for Forming Student Teams;Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1;2024-07-03

2. How Pre-class Programming Experience Influences Students' Contribution to Their Team Project: A Statistical Study;Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1;2024-03-07

3. Correlating Students' Class Performance Based on GitHub Metrics: A Statistical Study;Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1;2023-06-29

4. Attitude Towards the Deployment of Information Technology Programs in the Massive Open Online Course (MOOC) Environment;Studies in Systems, Decision and Control;2023

5. Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level;2022 IEEE Learning with MOOCS (LWMOOCS);2022-09-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3