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

Guruge, Deepani B.; Kadel, Rajan; Halder, Sharly Joana

doi:10.3390/data6020018

Cited by 38 publications

(25 citation statements)

References 126 publications

(244 reference statements)

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“…The similarities between the tasks of recommender systems in the consumer domain and the challenges of online education have led to many adaptations, especially for the personalization of learning processes (Drachsler et al, 2015). Examples of successful adaptation include finding relevant learning content (Deschênes, 2020), entire courses (Guruge et al, 2021), or the optimal sequence of learning content and activities (Kerres & Buntins, 2020).…”

Section: Educational Recommender Systemsmentioning

confidence: 99%

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Hemmler

Rasch²

2022

TechTrends

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Educational recommender systems offer benefits for workplace learning by tailoring the selection of learning activities to the individual’s learning goals. However, existing systems focus on the learner as the primary stakeholder of learning processes and do not consider the organization’s perspective. We conducted a systematic review to develop a categorization of workplace learning goals for multi-stakeholder recommender systems. Out of an initial set of 13,198 publications exported from databases, a final sample of 34 key publications was identified, according to predefined inclusion criteria. Content analysis and reflective exchange were deployed to synthesize workplace learning goals investigated in the key publications. We identified five categories of workplace learning goals that can be arranged along a dimension from intrinsic (goals set exclusively by the learner) to external (goals set exclusively by the organization). Our categorization provides a common language for multi-stakeholder recommender systems incorporating both the learner’s and the organization’s perspectives.

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Section: Educational Recommender Systemsmentioning

confidence: 99%

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Hemmler

Rasch²

2022

TechTrends

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“…A survey on this topic can be found in Guruge et al. (2021). Other related problems such as in Qi et al.…”

Section: Composite Retrieval Problemmentioning

confidence: 99%

“…The goal is to recommend to students courses that not only help satisfy the constraints, but are also desirable. A survey on this topic can be found in Guruge et al (2021). Other related problems such as in Qi et al (2016), where the authors study the problem of recommending a package to a group of users and in Guo and Ishikawa (2011), where the authors propose to solve the problem of combining queries (common in information retrieval) from a multi-objective perspective have also been proposed in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Local search algorithms for the composite retrieval problem

Moyano

Zabala

Gatica

et al. 2022

Int Trans Operational Res

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In this paper, we address a problem arising in information retrieval (IR) called composite retrieval problem (CRP) of diverse and complementary bundles. The CRP aims to group items into bundles and then select a subset of such bundles, so that we can maximise the similarity of the items within a bundle and, simultaneously, can maximise the complementarity of the selected bundles. To this end, the CRP approach considers the existing relations among items' attributes, leading to the selection of bundles that satisfy users' expectations without the needing for any refining query and, thus, improving the searching experience, with respect to traditional IR approaches. In this study, we propose three efficient yet straightforward algorithms, namely Local Search, Iterative Local Search and Variable Neighbourhood Search. Further, two different neighbourhood moves are evaluated at each algorithm. Although the first neighbourhood move is focused on the exploitation of the nearby search space, the second one is focused on the exploration of larger portions of the search space. All these algorithms are applied to two real-world publicly available instances and compared to the state-of-the-art algorithms in CRP. Obtained results suggest that combining both neighbourhood moves leads to better results in terms of both, the complementarity of the bundles and similarity of the items. Further, results show that, after statistical analysis, the proposed algorithms are significantly better, for the vast majority of the experiments performed in this study, when compared to the state-of-the-art algorithms in CRP.

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“…A number of surveys are conducted in the domain of eLearning RS [45][46][47][48], RS in general [49][50][51], review of the factors that affecting MOOC quality [52], but to the best of our knowledge only 3 survey focuses on MOOCRS [11,15,53]. Sunar et al [11] classified 40 selected studies between 2011 and 2014 based on the needs (why RS are required), proposals (the studies that involved funded projects for the personalization of online education) and implementations (studies with approaches for implementing personalization of MOOC).…”

Section: B Relevant Surveysmentioning

confidence: 99%

“…Features Remarks The state of the art in the methodologies of course Recommender Systems-A review of recent research (2021) [45] Review of the studies performed between 2016 to June 2020. Different recommendation approaches are used in detail.…”

Section: Table 1 Relevant Surveys Reference Surveymentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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

Cited by 38 publications

References 126 publications

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Local search algorithms for the composite retrieval problem

A Systematic Mapping Review on MOOC Recommender Systems

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