Recommender Systems for MOOCs: A Systematic Literature Survey  (January 1, 2012 – July 12, 2019)

Khalid, Asra; Lundqvist, Karsten; Yates, Anne

doi:10.19173/irrodl.v21i4.4643

Cited by 27 publications

(30 citation statements)

References 98 publications

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“…However, there are several challenges and opportunities in implementing those techniques in the context of course RS as we described in Section 1. Although there are several systematic review papers [116][117][118] published on specific learning platforms such as; MOOCs (Massive Open Online Courses) and e-Learning RS, still lacks systematic literature reviews for course RS in general [119]. Hence, primary focus of this research is to investigate current and emerging approaches applied in course RS in the recent research literature and also to outline the avenues for the future research.…”

Section: Systematic Review Processmentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

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In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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Section: Systematic Review Processmentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

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“…This study also reported that the dominant perspective published by public media was about the role of MOOC in revolutionizing higher education, which was likely to boost the public's acceptance of the changes in existing higher education structures. focused on how technology and artificial intelligence can be used to improve teaching and learning in MOOCs was also documented (Adham et al, 2016;Khalid et al, 2020;Lundqvist et al, 2020).…”

Section: Research On Moocs In New Zealandmentioning

confidence: 99%

MOOCs and Education Access: Evidence from New Zealand

Cao¹

2021

Preprint

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Massive Open Online Courses (MOOCs) are free online courses available for anyone to enrol in and are aimed at unlimited participation (Siemens, 2013). One aim of MOOCs is to increase the accessibility of tertiary education to people who would not usually have access to it. The past decade has seen a rapid development of these courses with a stream of research focused on this new teaching innovation. While there has been research on how MOOCs are developed and why students enrol in MOOCs, there has been relatively less research focused on the accessibility to students in different countries, including those that could be considered less economically developed. This study used a mixed-methods research approach to examine the relationship between MOOCs and the education access of these courses for students, internationally. Quantitative and qualitative data were gathered from both the students' and lecturers' perspectives of two MOOCs taught in a New Zealand university. Quantitative data on the students' demographics and motivations were provided by the EdX platform. These data indicated that most students enrolled in the two MOOCs were from developed countries. Correlation analysis and multivariate regression were used to examine the relationship between students' participation rate and development level in the country where the students were studying. The statistical results indicate that students from developed countries were more likely to enrol in the MOOCs than students from developing countries. Given that MOOCs aim to engage diverse groups of students globally, qualitative data in the form of interviews were used to address the following questions: What are the lecturers' motivations to teach these MOOCs; which groups of students did the lecturers target in their teaching; and how did the lecturers adapt the courses to cater for the learners' diversity? The lecturers anticipated that most MOOCs students were from developed countries and had tertiary degrees. Although the lecturers applied several approaches to engage diverse groups of students, their demographics were largely aligned with the lecturers' expectations. The role of the MOOCs in increasing accessibility to higher education for students who were traditionally excluded was limited.

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“…With the continuous development of big data technology, MOOC resource recommendation algorithms have emerged. With the help of recommendation technologies in the fields of e-commerce, tourism routes, and social networks, many excellent MOOC resource recommendation algorithms have been proposed [6,7]. Traditional courses only have dozens or hundreds of students, but a MOOC can hold more than 100,000 people at most.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

2021

Complexity

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In view of the poor recommendation performance of traditional resource collaborative filtering recommendation algorithms, this article proposes a collaborative filtering recommendation model based on deep learning for art and MOOC resources. This model first uses embedding vectors based on the context of metapaths for learning. Embedding vectors based on the context of metapaths aggregate different metapath information and different MOOCs may have different preferences for different metapaths. Secondly, to capture this preference drift, the model introduces an attention mechanism, which can improve the interpretability of the recommendation results. Then, by introducing the Laplacian matrix into the prior distribution of the hidden factor feature matrix, the relational network information is effectively integrated into the model. Finally, compared with the traditional model using the scoring matrix, the model in this article using text word vectors effectively alleviates the impact of data sparsity and greatly improves the accuracy of prediction. After analyzing the experimental results, compared with other algorithms, the resource collaborative filtering recommendation model proposed in this article has achieved better recommendation results, with good stability and scalability.

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Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

Cited by 27 publications

References 98 publications

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

MOOCs and Education Access: Evidence from New Zealand

Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

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