Re-engineering the multiple choice question exam for social work

Heron, Gavin; Lerpiniere, Jennifer

doi:10.1080/13691457.2012.691873

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Yet the use of MCQs is also widely criticised, for promoting rote learning, fact memorization and guessing rather than higher order skills (McCoubrie, ). MCQs are used less and considered less suitable in discursive subjects (Heron & Lerpiniere, ; McKenna, ) such as arts and humanities compared to their widespread use in Science, Technology, Engineering, Maths and Medicine (STEMM) subjects. Yet literature also suggests that the MCQ format can effectively nurture critical thinking and deeper learning (Brady, ; Draper, ; Nicol, ).…”

Section: Introductionmentioning

confidence: 99%

A discursive question: Supporting student‐authored multiple choice questions through peer‐learning software in non‐STEMM disciplines

Snow¹,

Wilde²,

Denny³

et al. 2018

Brit J Educational Tech

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Peer‐learning that engages students in multiple choice question (MCQ) formulation promotes higher task engagement and deeper learning than simply answering MCQ’s in summative assessment. Yet presently, the literature detailing deployments of student‐authored MCQ software is biased towards accounts from Science, Technology, Engineering, Maths and Medicine (STEMM) subjects, rather than discursive subjects or disciplines where content may contain fewer absolute facts and objective metrics and more nuance. We report on qualitative and quantitative findings from a semester‐long deployment of a peer‐learning software package (PeerWise) in a 140‐student course on Interaction Design. PeerWise enables students to author, rate and comment upon their peers’ MCQ questions. The platform was enthusiastically adopted as a revision aid, yet overall question quality was poor and students expressed difficulty in translating the discursive nature of the course content into MCQs with only one correct answer. In addressing these shortcomings, this paper offers specific recommendations to instructors of more discursive subjects using student‐led MCQ authoring platforms, and further, how platforms such as PeerWise may be adapted to better suit disciplines characterised by discursive content. We propose alternative approaches to moderation and two suggestions for potential amendments to the software itself.

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Section: Introductionmentioning

confidence: 99%

A discursive question: Supporting student‐authored multiple choice questions through peer‐learning software in non‐STEMM disciplines

Snow¹,

Wilde²,

Denny³

et al. 2018

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…Moreover, the certainty of knowledge is an essential component to represent the belief of the learner as the quality of the knowledge [2][3][4][5][6]. For instance, confidence can encourage a deeper understanding of the material [7] and can increase reflection and justification of the answers [8]. Consequently, the answers of learners represent their understanding, and the con-fidence in their answer indicates the degree of their understanding, such as the different degrees of the understanding between a learner who is sure in the correct answer and a learner who is unsure in the correct answer.…”

Section: Introductionmentioning

confidence: 99%

Correctness- and Confidence-Based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging

Pailai

Wunnasri

Hayashi

et al. 2018

Lecture Notes in Computer Science

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In this paper, we present an adaptive feedback of Kit-Build concept map with confidence tagging (KB map-CT) for improving the understanding of learners in a reading situation. KB map-CT is a digital tool that supports the concept maps strategy where learners can construct concept maps for representing their understanding as learner maps and can identify their confidence in each proposition of the learner maps as a degree of their understanding. Kit-Build concept map (KB map) has been already realized the propositional level automatic diagnosis of the learner maps. Therefore, KB map-CT can utilize both correctness and confidence information for each proposition to design and distinguish feedback, that is, (1) correct and confident, (2) correct and unconfident, (3) incorrect and confident, and (4) incorrect and unconfident. An experiment was conducted to investigate the effectiveness of the adaptive feedback. The results suggest that learners can revise their maps after receiving feedback appropriately. In "correct and unconfident" case, adaptive feedback is useful to improve the confidence. In the case of "incorrect and confident," improvement of the propositions was the same ratio with the case of "incorrect and unconfident." The results of the delay test demonstrate that learners can retain their understanding and confidence one week later.

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“…Several researchers mentioned the confidence in the various situations. For instance, the confidence can encourage a deeper understanding of the material [9], and also increase reflection and justification of the answers [10]. Consequently, the answer of learners represents their understanding, and the confidence on their answer indicates the degree of understanding.…”

Section: Introductionmentioning

confidence: 99%

Kit-Build Concept Map with Confidence Tagging in Practical Uses for Assessing the Understanding of Learners

Pailai¹,

Wunnasri²,

Hayashi³

et al. 2018

ijacsa

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Abstract-An answer of a learner can be interpreted as a learning evidence for demonstrating the understanding of the learner, while a confidence on the answer represents the belief of the learner as the degree of understanding. In this paper, we propose Kit-Build concept map with confidence tagging. KitBuild concept map (KB map in short) is a digital tool for supporting a concept map strategy where learners can create the learning evidence, and the instructor can access the correctness and confidence information of learners. The practical uses were conducted for demonstrating the valuable of correctness and confidence information in the lecture class. The correctness information was visualized in the control classes, while the correctness and confidence information were visualized in the experiment classes. The observed evidence illustrates that the different information was used for selecting and ordering the supplementary content when the system visualized the different information. The normalized learning gains and effect size demonstrate the different learning achievements between control-and experiment-classes. The results suggest that the confidence information of learner affects the instructor behaviors, which is the positive changing behavior for improving the understanding of their learners. The results of questionnaire suggest that the KB map with confidence tagging is an accepted mechanism for representing the learner's understanding and their confidence. The instructors also accepted that the confidence information of learners is valuable information for recognizing the learning situation.Keywords-Kit-Build concept map; confidence tagging; effect of confidence information; behavior changing of instructor

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Re-engineering the multiple choice question exam for social work

Cited by 4 publications

References 29 publications

A discursive question: Supporting student‐authored multiple choice questions through peer‐learning software in non‐STEMM disciplines

A discursive question: Supporting student‐authored multiple choice questions through peer‐learning software in non‐STEMM disciplines

Correctness- and Confidence-Based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging

Kit-Build Concept Map with Confidence Tagging in Practical Uses for Assessing the Understanding of Learners

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