Generating Practice Questions as a Preparation Strategy for Introductory Programming Exams

Denny, Paul

doi:10.1145/2676723.2677253

Cited by 20 publications

(7 citation statements)

References 16 publications

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“…A common use of question repositories in PeerWise by students is for review and practice purposes leading up to summative tests and exams. Prior work has shown that answering activity in PeerWise typically increases rapidly before a test [38], and that answering questions is strongly predictive of subsequent test performance [39], [40]. Instructors are also able to make use of the questions, for example by reviewing a question repository to identify topics that are challenging for students, or by selecting high-quality questions for use on summative tests and exams.…”

Section: Crowdsourcing Systems a Peerwisementioning

confidence: 99%

Lessons Learned From Four Computing Education Crowdsourcing Systems

et al. 2023

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Crowdsourcing is a general term that describes the practice of many individuals working collectively to achieve a common goal or complete a task, often involving the generation of content. In an educational context, crowdsourcing of learning materials -where students create resources that can be used by other learners -offers several benefits. Students benefit from the act of producing resources as well as from using the resources. Despite benefits, instructors may be hesitant to adopt crowdsourcing for several reasons, such as concerns around the quality of content produced by students and the perceptions students may have of creating resources for their peers. While prior work has explored crowdsourcing concerns within the context of individual tools, lessons that are generalisable across multiple platforms and derived from practical use can provide considerably more robust insights. In this perspective article, we present four crowdsourcing tools that we have developed and used in computing classrooms. From our previous studies and experience, we derive lessons which shed new light on some of the concerns that are typical for instructors looking to adopt such tools. We find that across multiple contexts, students are capable of generating high quality learning content which provides good coverage of key concepts. Although students do appear hesitant to engage with new kinds of activities, various types of incentives have proven effective. Finally, although studies on learning effects have shown mixed results, no negative outcomes have been observed. In light of these lessons, we hope to see a greater uptake and use of crowdsourcing in computing education.INDEX TERMS contributing student pedagogy, crowdsourcing, crowdsourcing systems, learnersourcing

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Section: Crowdsourcing Systems a Peerwisementioning

confidence: 99%

Lessons Learned From Four Computing Education Crowdsourcing Systems

et al. 2023

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“…Collaborative question generation improves self-confidence and critical thinking, sheds light on different ways to solve a problem, supports ideation, exhilarates students, facilitates rapid peer-evaluation, and improves deep learning [64,19,76,25,16,65,14,6,27,47,45,4,8,17,60,49,22]. Creating question stems, correct/wrong choices, and justifications for each choice involves critical thinking and evaluation that result in deep learning [25,49,20].…”

Section: Collaborative Question Generationmentioning

confidence: 99%

“…This motivation typically declines over time [33,31]. Different studies have incentivized students' contributions through course grades or paying them as subjects of the study [76,14,25,29,20,49,38]. Gamification, through a serious board game, is also proposed [77].…”

Section: Motivation In Collaborative Learningmentioning

confidence: 99%

QMaps: Engaging Students in Voluntary Question Generation and Linking

YeckehZaare

Barghi²,

Resnick

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Generating multiple-choice questions is known to improve students' critical thinking and deep learning. Visualizing relationships between concepts enhances meaningful learning, students' ability to relate new concepts to previously learned concepts. We designed and deployed a collaborative learning process through which students generate multiple-choice questions and represent the prerequisite knowledge structure between questions as visual links in a shared map, using a variation of Concept Maps that we call "QMap." We conducted a four-month study with 19 undergraduate students. Students sustained voluntary contributions, creating 992 good questions, and drawing 1, 255 meaningful links between the questions. Through analyzing self-reports, observations, and usage data, we report on the technical and social design features that led students to sustain their motivation.

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“…Gamification is the application of game-design elements principles in non-game contexts (De-Marcos, et al 2014;Robson, et al 2015;Nehring et al 2018). It employs game design elements to achieve different goals including improving user engagement (Hamari, 2015;Nehring et al 2018), user behaviour (Reddy, 2018), organizational productivity (Zichermann & Cunningham, 2011) and learning (Denny, 2015;Gooch et al, 2016;Cózar-Gutiérrez, et al 2016). Gamification has been shown to increase learners' engagement with course materials and improve their motivation, learning participation and collaboration (Denny, 2015;Dicheva et al 2015;Nehring et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Engaging children with educational content via Gamification

Nand

Baghaei

Casey

et al. 2019

Smart Learn. Environ.

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Gamification is the application of game mechanisms in non-gaming environments with the objective of enhancing user experience. In this paper, we investigate the effectiveness of gamification in educational context, i.e. teaching numeracy at a primary school level. We study the appealing characteristics of engaging computer games from children's point of view, and investigate whether embedding the proposed characteristics into an educational tool enhances children's learning. The main characteristics we identify are levels of difficulties, feedback from the current level, and graphical presentation. They were then embedded into a Java-based open source programme based on "Who wants to be a millionaire" TV show, with the aim of teaching children numeracy (level 5 New Zealand curriculum). Two versions were created: feature enriched game (FEG) with all the features enabled and feature devoid game (FDG) with no extra features. We present the results of an evaluation study done with primary school children (n = 120) over a period of two weeks. The effectiveness of the educational tool was measured using a pre-test and a post-test, as well as other indicators such as the frequency and duration of interaction. Results show that the FEG version was more effective in enhancing children's learning and they found it more engaging.

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Generating Practice Questions as a Preparation Strategy for Introductory Programming Exams

Cited by 20 publications

References 16 publications

Lessons Learned From Four Computing Education Crowdsourcing Systems

Lessons Learned From Four Computing Education Crowdsourcing Systems

QMaps: Engaging Students in Voluntary Question Generation and Linking

Engaging children with educational content via Gamification

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