Users’ Learning Pathways on Cross-site Open Educational Resources

Sunar, Ayşe Saliha; Novak, Erik; Mladenić, Dunja

doi:10.5220/0009391600840095

Cited by 4 publications

(3 citation statements)

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“…In a world populated by MOOCs, many powerful educational resources that can become enablers of our future generations are gated behind licensing walls that limit their reach and impact. OERs provide an excellent remedy for this situation by relaxing copyright restrictions, leading to mass adoption among both learning resource creators [18] and consumers [19]. Currently, new AI-enabled tools that discover and index such collections are emerging [20,21], and these solutions are addressing the vital need for scalable content understanding that can support large OER collections.…”

Section: Opportunities and Challenges In Scaling Personalised Educationmentioning

confidence: 99%

Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

et al. 2022

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Educational recommenders have received much less attention in comparison with e-commerce- and entertainment-related recommenders, even though efficient intelligent tutors could have potential to improve learning gains and enable advances in education that are essential to achieving the world’s sustainability agenda. Through this work, we make foundational advances towards building a state-aware, integrative educational recommender. The proposed recommender accounts for the learners’ interests and knowledge at the same time as content novelty and popularity, with the end goal of improving predictions of learner engagement in a lifelong-learning educational video platform. Towards achieving this goal, we (i) formulate and evaluate multiple probabilistic graphical models to capture learner interest; (ii) identify and experiment with multiple probabilistic and ensemble approaches to combine interest, novelty, and knowledge representations together; and (iii) identify and experiment with different hybrid recommender approaches to fuse population-based engagement prediction to address the cold-start problem, i.e., the scarcity of data in the early stages of a user session, a common challenge in recommendation systems. Our experiments with an in-the-wild interaction dataset of more than 20,000 learners show clear performance advantages by integrating content popularity, learner interest, novelty, and knowledge aspects in an informational recommender system, while preserving scalability. Our recommendation system integrates a human-intuitive representation at its core, and we argue that this transparency will prove important in efforts to give agency to the learner in interacting, collaborating, and governing their own educational algorithms.

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Section: Opportunities and Challenges In Scaling Personalised Educationmentioning

confidence: 99%

Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

et al. 2022

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“…During this rapid transition, OERs could be a great resource for academics to prepare their online teaching materials. However, the lecturers may not know how to reach them or how to integrate them into their courses, as this is an issue that educational technologists are concerned with even before the outbreak (Chotto & Rivera, 2017;Ganapathy et al, 2015;Sunar et al, 2020).…”

Section: Related Work: Oers and Emergency Remote Teachingmentioning

confidence: 99%

Analysis on Use of Open Educational Resources during EmergencyRemote Education due to Covid-19: A Case Study in Turkey

2022

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During the mass curfews, travel bans, school shutdowns, face-to-face education was discontinued, and many universities had to urgently switch to online education. Academics, most of whom are not familiar with digital pedagogy, had to adapt their lectures to online learning. The aim of this study is to analyze how Open Educational Resources (OERs) are used in practice during emergency remote teaching (ERT) and whether this influences the regular practice of academics on a global level and in Turkey in the longer term. Methodologically, we adopt a mixed-methods approach in two stages: (i) an empirical study conducted in Turkey to find out what prior knowledge and experience academics have with OERs and how they use OERs during ERT; (ii) a complimentary desk study on the global situation of OER use. Our results show that academics who did not know about OERs before the pandemic are still hesitant to use them, even though they have prior experience with online teaching. In addition, academics with higher rank and academics in architecture, philology, and arts have the most negative opinion about campus education being fully accessible online.

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“…And OER could be relative to learning pathways in MOOCs. General OER Recommender systems [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [30], [30], [30], [30], [42] , [44], [45] , [46], [49], [50], [51], [52], [53], [54], [56], [57], [58], [59] [100], [104], [111], [113], [114], [126], [127], [129], [131] OER in MOOCs [28],…”

Section: Figmentioning

confidence: 99%

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

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background: In recent years, recommender systems (RS) have grown in popularity among learning and education stakeholders, while open educational resources (OER) have begun to be deployed to improve the education field. The selection of OERs should not be haphazard; rather, it should be based on the learning objectives and (RS) commonly utilized to overcome the abundance of OER. Objective: The aim of this paper is to examine the current advances in OER recommenders and the different commonly used recommendation approaches and technical features, to position our future research alongside the existing. Method: A systematic literature review with a deductive approach using the grounded theory technique, combining quantitative and qualitative analysis by adopting open coding (inductive and deductive) for content analysis. Databases: we employed Google Scholar as a search engine to crawl data. Results: The findings are presented according to the (Bernard, H. Russell .2000) process. We present the results of the analysis using quotes from interviews that illuminate the theory (exemplars). Conclusion: The review reveals an emerging interest in the area; nevertheless, hybrid interactive systems; chatbot-based, dedicated to course creators are lacking. Further and future research is needed

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