Impact of Several Low-Effort Cheating-Reduction Methods in a CS1 Class
Impact of Several Low-Effort Cheating-Reduction Methods in a CS1 Class
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CS1 课程中几种省力的减少作弊方法的影响
DOI：
10.1145/3545945.3569731
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gordon, Chelsea
中科院分区：
文献类型：
--
作者：
Vahid, Frank;Downey, Kelly;Pang, Ashley;Gordon, Chelsea
在编程入门课(CS1)中作弊是一个众所周知的问题。已经提出了各种方法来减少作弊，但许多方法都很耗时、资源密集，或者不适合大班。我们引入了一种课堂干预，其中有6种低努力的常见方法来减少作弊：(1)在学期开始几周后，用20-30分钟讨论学术诚信，(2)要求进行诚信测试，明确应该做什么和不应该做什么，(3)允许学生撤回提交的程序，(4)提醒学生期中诚信和被抓到的后果，(5)在课堂上展示指导工具(包括相似性检查器，花费时间的统计数据，以及访问学生的完整编码历史)，(6)规范帮助并指示学生找到帮助资源。通过对7个恒定实验室和1名教师授课100人组的相似性检验结果进行手工评估，对于两个干预前和两个干预组，涉嫌作弊减少了62%(从30.5%下降到11.5%)。由于人工评估可能会有偏见且耗时，我们开发了两个自动编码行为指标：每个实验室的编程时间和代码高度相似的学生百分比，这可能表明发生了多少作弊。花费的时间增加了56%(7分钟到10.9分钟)，代码高度相似的学生的百分比下降了48%(38.5%到20%)。后来，我们与另一名教师和不同的实验室重复了这一干预，取得了类似(事实上，更强的)结果，时间增加了84%(13分钟到24分钟)，时间减少了66%(55.5%到19%)。对于P<；0.0001，所有的发现都具有统计学意义。
Cheating in introductory programming classes (CS1) is a well-known problem. Various methods have been suggested to reduce cheating, but many are time-consuming, resource intensive, or don't scale to large classes. We introduced a class intervention having 6 low-effort commonly-suggested methods to reduce cheating: (1) Discussing academic integrity for 20-30 minutes, several weeks into the term, (2) Requiring an integrity quiz with explicit do's and don'ts, (3) Allowing students to retract program submissions, (4) Reminding students mid-term about integrity and consequences of getting caught, (5) Showing instructor tools in class (including a similarity checker, statistics on time spent, and access to a student's full coding history), (6) Normalizing help and pointing students to help resources. Via manual evaluation of similarity checker results on 7 held-constant labs with one instructor teaching 100-student sections, for two pre-intervention and two intervention sections, suspected-cheating reduced 62% (30.5% down to 11.5%). Because manual evaluation could be biased and is time consuming, we developed two automated coding-behavior metrics per lab -- time spent programming, and % of students with highly-similar code -- that may suggest how much cheating is happening. Time spent increased by 56% (7 min to 10.9 min), and % of students with highly-similar code dropped 48% (38.5% to 20%). We later repeated the intervention with a second instructor and different labs and achieved similar (in fact, even stronger) results, with time rising 84% (13 min to 24 minutes) and % dropping 66% (55.5% to 19%). All findings were statistically significant with p < 0.0001.
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DOI：
--
发表时间：
2017
期刊：
IFAC Symposium on Advances in Control Education
影响因子：
--
作者：
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通讯作者：
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影响因子：
2.3
作者：
L. Williams;R. Upchurch
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影响因子：
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DOI：
--
发表时间：
1987
期刊：
Technical Symposium on Computer Science Education
影响因子：
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作者：
Ernest Ferguson
通讯作者：
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DOI：
--
发表时间：
2017
期刊：
Annual Conference on Innovation and Technology in Computer Science Education
影响因子：
--
作者：
Judithe Sheard;Simon;M. Butler;K. Falkner;Michael Morgan;A. Weerasinghe
通讯作者：
A. Weerasinghe