Assessing Large-Project Courses

Author:

Vasilevskaya Maria1,Broman David2,Sandahl Kristian1

Affiliation:

1. Linköping University, Sweden

2. KTH Royal Institute of Technology and University of California, Berkeley, California

Abstract

In a modern computing curriculum, large-project courses are essential to give students hands-on experience of working in a realistic software engineering project. Assessing such projects is, however, extremely challenging. There are various aspects and trade-offs of assessments that can affect course quality. Individual assessments may fairly grade individuals, but may lose focus of the project as a group activity. Extensive teacher involvement is necessary for objective assessment, but may affect the way that students work. Continuous feedback to students can enhance learning, but may be hard to combine with fair assessment. Most previous work focuses on some specific assessment aspect; in this article, we present an assessment model that consists of a collection of assessment activities, each covering different aspects. We have applied, developed, and improved these activities during a 7yr period. To evaluate the usefulness of the model, we perform questionnaire-based surveys over a 2yr period. Furthermore, we design and execute an experiment that studies to what extent students can perform fair peer assessment and to what degree the assessments of students and teachers agree. We analyze the results, discuss findings, and summarize lessons learned.

Funder

Linköpings Universitet

Kungliga Tekniska Högskolan

University of California Berkeley

Publisher

Association for Computing Machinery (ACM)

Subject

Education,General Computer Science

Cited by 12 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. Team Harmony before, during, and after COVID-19;Proceedings of the 2022 ACM SIGPLAN International Symposium on SPLASH-E;2022-11-29

2. Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments;Sensors;2022-09-26

3. Learners reflection on collaborative project in project-oriented problem-based learning for software engineering courses;AIP Conference Proceedings;2022

4. Evaluating Commit, Issue and Product Quality in Team Software Development Projects;Proceedings of the 52nd ACM Technical Symposium on Computer Science Education;2021-03-03

5. Developing an Interdisciplinary Data Science Program;Proceedings of the 52nd ACM Technical Symposium on Computer Science Education;2021-03-03

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