Leveraging explainability for discussion forum classification: Using confusion detection as an example

Du, Hanxiang; Xing, Wanli

doi:10.1080/01587919.2022.2150145

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…3) assessment and evaluation (Crompton & Burke, 2023;Du & Xing, 2023;Whitelock et al, 2014); and 4) adaptive systems and personalization (Fariani et al, 2023;Iniesto et al, 2023). These four common AIED typologies were also used in the subsequent meta-review of Bond et al (2024) in order to classify which AIED approaches were most commonly used.…”

Section: Supporting Distance Learners With Aimentioning

confidence: 99%

What distance learning students want from an AI Digital Assistant

Rienties,

Domingue,

Duttaroy

et al. 2024

Distance Education

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With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education.While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a twostep, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students' perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalization and accessibility, together with emotional and social support. Students concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

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Section: Supporting Distance Learners With Aimentioning

confidence: 99%

What distance learning students want from an AI Digital Assistant

Rienties,

Domingue,

Duttaroy

et al. 2024

Distance Education

View full text Add to dashboard Cite

show abstract

Exploring influential factors in peer upvoting within social annotation

Li,

Huang,

Lin

et al. 2024

Brit J Educational Tech

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Upvotes serve important purposes in online social annotation environments. However, limited studies have explored the influential factors affecting peer upvoting in online collaborative learning. In this study, we analysed the factors influencing students' upvotes received from their peers as 91 participants utilized Perusall, an online social annotation system, for collaborative reading. The participants were asked to collaboratively annotate 29 reading materials in a semester. We collected student reading behaviours and analysed their annotations with a text‐mining tool of Linguistic Inquiry and Word Count (LIWC). Moreover, conditional inference tree was used to determine the relative importance of explanatory factors to the upvotes students received. The results showed that the high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations than those who did not receive upvotes. The two groups of students had no significant differences in the upvotes given to others, as well as cognitive activities and positive emotions involved in annotations. Moreover, the number of annotations was the determining factor in predicting the upvotes that one could receive in social annotation activities. This study has significant practical implications regarding providing interventions in social annotation‐based collaborative reading.Practitioner notesWhat is already known about this topic Social annotations enhance students' reading experience, facilitate knowledge sharing and collaboration, promote high‐quality learning interactions and ultimately lead to improved performance. In social annotation environments, receiving upvotes from peers is not only a type of feedback but also a form of motivation, social interaction and social validation. No study has explored the influential factors in peer upvoting within social annotation‐based learning. What this paper adds This study was the first to examine social annotations through the lens of the community of inquiry framework. We investigated the relationships between students' cognitive and social presence in their annotations and the upvotes they received in an online social annotation environment. Our study revealed the strategies for obtaining upvotes from peers in social annotation‐based learning environments. Implications for practice and/or policy The high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations compared to the low‐upvote group. The two groups of students did not show significant differences in the upvotes they gave to others or in the cognitive activities and positive emotions involved in annotations. The number of annotations was the primary factor predicting the number of upvotes received in the collaborative reading. This study could inform the design of future online social annotation systems to better support collaborative learning and peer interaction.

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Leveraging explainability for discussion forum classification: Using confusion detection as an example

Cited by 2 publications

References 33 publications

What distance learning students want from an AI Digital Assistant

What distance learning students want from an AI Digital Assistant

Exploring influential factors in peer upvoting within social annotation

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