Exploring (Collaborative) Generation and Exploitation of Multiple Choice Questions: Likes as Quality Proxy Metric

• Published in: Education sciences 2022-05, Vol.12 (5), p.297
• Main Author: Söbke, Heinrich
• Format: Article
• Language: English
• Subjects: Classrooms; collaborative content generation; Collaborative learning; Computer & video games; Educational evaluation; game-based learning; Learner Engagement; Learning Activities; Learning Processes; MCQ; mobile learning; Motivation; Online instruction; quiz app; Skill Development; Software utilities; Students; user-generated content
• ISSN: 2227-7102; 2227-7102
• DOI: 10.3390/educsci12050297

Multiple Choice Questions (MCQs) are an established medium of formal educational contexts. The collaborative generation of MCQs by students follows the perspectives of constructionist and situated learning and is an activity that fosters learning processes. The MCQs generated are—besides the learning processes—further outcomes of collaborative generation processes. Quality MCQs are a valuable resource, so that collaboratively generated quality MCQs might also be exploited in further educational scenarios. However, the quality MCQs first need to be identified from the corpus of all generated MCQs. This article investigates whether Likes distributed by students when answering MCQs are viable as a metric for identifying quality MCQs. Additionally, this study explores whether the process of collaboratively generating MCQs and using the quality MCQs generated in commercial quiz apps is achievable without additional extrinsic motivators. Accordingly, this article describes the results of a two-stage field study. The first stage investigates whether quality MCQs may be identified through collaborative inputs. For this purpose, the Reading Game (RG), a gamified, web-based software aiming at collaborative MCQ generation, is employed as a semester-accompanying learning activity in a bachelor course in Urban Water Management. The reliability of a proxy metric for quality calculated from the ratio of Likes received and appearances in quizzes is compared to the quality estimations of domain experts for selected MCQs. The selection comprised the ten best and the ten worst rated MCQs. Each of the MCQs is rated regarding five dimensions. The results support the assumption that the RG-given quality metric allows identification of well-designed MCQs. In the second stage, MCQs created by RG are provided in a commercial quiz app (QuizUp) in a voluntary educational scenario. Despite the prevailing pressure to learn, neither the motivational effects of RG nor of the app are found in this study to be sufficient for encouraging students to voluntarily use them on a regular basis. Besides confirming that quality MCQs may be generated by collaborative software, it is to be stated that in the collaborative generation of MCQs, Likes may serve as a proxy metric for the quality of the MCQs generated.