A Gamified Solution to the Cold-Start Problem of Intelligent Tutoring System

Pian, Yang; Lu, Yu; Huang, Yuqi; Bittencourt, Ig Ibert

doi:10.1007/978-3-030-52240-7_68

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Besides the mixed group, the self-group also significantly outperformed the control group in choosing and rating restaurants. While gamification has been proposed to motivate users to make more contributions to recommender systems ( Herpich et al., 2017 ; Pian et al., 2020 ), the self-gaming design has been proven that it is better for motivating participants than the social design in this study's context. In addition, the mixed design is effective since it combines the effects of the self and social elements.…”

Section: Analysis and Resultsmentioning

confidence: 89%

“…In this context, the self-competitive design outperformed the social-competitive design, where significant effects were found only for the self-competitive design. Although several studies apply gamification elements in recommender systems ( Neeraj et al., 2017 ; Pian et al., 2020 ) and gamification has been shown to increase participation in recommender systems ( Jugovac and Jannach, 2017 ), they do not compare the effects of different gamification approaches. In our context, the impact of self-competitive is better than that of social-competitive in terms of supporting recommender systems.…”

Section: Discussionmentioning

confidence: 99%

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Competitive gamification in crowdsourcing-based contextual-aware recommender systems

Lin

Ding

2023

International Journal of Human-Computer Studies

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Section: Analysis and Resultsmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

Lin

Ding

2023

International Journal of Human-Computer Studies

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Combining intelligent tutoring systems and gamification: a systematic literature review

2023

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Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Oliveira

Barba²,

Corrin³

2021

AJET

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Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of time, place, platform, and form. In this paper we introduce the iCollab platform, an adaptive environment where learning activities are moderated through conversation with an intelligent agent who can operate across multiple web-based platforms, integrating formal and informal learning opportunities. Fifty-eight undergraduate computer science students were randomly assigned to either an intervention or control group for the 12 weeks of the pilot study. Learning analytics were used to examine their interactions with iCollab, while their course performance investigated the impact of using iCollab on learning outcomes. Results from the study showed a high level of interaction with iCollab, especially social interaction, indicating an interweaving of formal learning within their informal network spaces. These findings open up new possibilities for ways that SLEs can be designed to incorporate different factors, improving the ability of the system to provide adaptive and personalised learning experiences in relation to context and time.

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A Gamified Solution to the Cold-Start Problem of Intelligent Tutoring System

Cited by 3 publications

References 8 publications

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

Competitive gamification in crowdsourcing-based contextual-aware recommender systems

Combining intelligent tutoring systems and gamification: a systematic literature review

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

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