Complementing educational recommender systems with open learner models

Abdi, Solmaz; Khosravi, Hassan; Sadiq, Shazia; Gašević, Dragan

doi:10.1145/3375462.3375520

Cited by 50 publications

(29 citation statements)

References 18 publications

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“…For instance, transparent and open learner models (OLMs) have been the centre of LA and EDM communities which can significantly benefit MMLA research. As shown by Abdi et al (2020), complementing educational recommender systems (ERSs) with open learner models (OLMs) can have a positive impact on user's perceptions and engagement based on the results generated from a randomized controlled experiment. Similarly, as exemplified in recent research (Cukurova, Zhou, et al, 2020) a transparent model that predicts learners' collaborative problem-solving competencies from video data can be preferred over high performing yet non-transparent models.…”

Section: Ethical Considerations Of Learning Analyticsmentioning

confidence: 99%

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Alwahaby¹,

Cukurova²,

Papamitsiou³

et al. 2021

Preprint

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There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

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Section: Ethical Considerations Of Learning Analyticsmentioning

confidence: 99%

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Alwahaby¹,

Cukurova²,

Papamitsiou³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Educational recommender system has received relatively less attention compared to other research domain such as entertainment (music, movies) or e commerce. Recommending learning materials to students have shown to positively impact students learning outcomes (see [108][109][110][111][112]). The recent study of [113] that recommend learning contents to be adjusted has shown that the future of e-learning heavily relies or is associated with recommender systems in which learning systems would be designed to recommend or provide learners with personalized learning materials based on their contexts, skill, and various other information that defines or identifies a learner.…”

Section: Discussionmentioning

confidence: 99%

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

et al. 2021

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Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.

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“…The literature suggests that the use of explainable AI (XIA) [61] is not always wanted or necessary [11]. However, the use of machine learning algorithms with black-box outcomes seems to be particularly inadequate for educational settings where educators strive to provide extensive feedback to enable learners to develop their own Much of the existing work on the need for open and XIA models in education has been conducted in the field of open learner models [9] where models are often opened through visualisations, as an important means of supporting learning through various systems such as learning analytics dashboards [10,54], intelligent tutoring systems [53], educational recommender systems [3], and adaptive learning platforms [34] (please see Section 3.3 for further discussion on use of explainable AI in education). In terms of learnersourcing systems, the problem of assessing quality of learnersourced contributions has been referred to or studied in previous work [19,25,47,63]; however the focus has generally been on maximising accuracy rather than explainability.…”

Section: Assessing the Quality Of Learnersourced Content With Accuratmentioning

confidence: 99%

“…Authors concluded that the quality of explanations provided by humans is still perceived as superior and they observed that the better the explanation the better the recommendation is perceived to be by users. Some initial work on the development of transparent recommender systems have been conducted in the learning analytics community [3], but overall, the topic is still under-developed and under-researched and. So how can learnersourcing systems couple analytics and recommendations to empower instructors with actionable and explainable insights to guide student learning?…”

Section: Empowering Instructors With Actionable and Explainable Insightsmentioning

confidence: 99%

Charting the Design and Analytics Agenda of Learnersourcing Systems

Khosravi

Demartini

Sadiq

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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Learnersourcing is emerging as a viable learner-centred and pedagogically justified approach for harnessing the creativity and evaluation power of learners as experts-in-training. Despite the increasing adoption of learnersourcing in higher education, understanding students' behaviour while engaged in learnersourcing and best practices for the design and development of learnersourcing systems are still largely under-researched. This paper offers data-driven reflections and lessons learned from the development and deployment of a learnersourcing adaptive educational system called RiPPLE, which to date, has been used in more than 50-course offerings with over 12,000 students. Our reflections are categorised into examples and best practices on (1) assessing the quality of students' contributions using accurate, explainable and fair approaches to data analysis, (2) incentivising students to develop high-quality contributions and (3) empowering instructors with actionable and explainable insights to guide student learning. We discuss the implications of these findings and how they may contribute to the growing literature on the development of effective learnersourcing systems and more broadly technological educational solutions that support learner-centred learning at scale. CCS CONCEPTS• Applied computing → Computer-assisted instruction; Interactive learning environments; • Human-centered computing → Collaborative and social computing systems and tools.

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Complementing educational recommender systems with open learner models

Cited by 50 publications

References 18 publications

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Charting the Design and Analytics Agenda of Learnersourcing Systems

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