Recommendation Systems in Education: A Systematic Mapping Study

Rivera, Abdón Carrera; Tapia-Leon, Mariela; Luján‐Mora, Sergio

doi:10.1007/978-3-319-73450-7_89

Cited by 63 publications

(40 citation statements)

References 16 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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“…In [Cui et al 2018], the authors discuss the difficulty of effectively evaluating recommendation systems, but they are limited to the conventional metrics, such as accuracy and diversity, and did not consider specific characteristics of the educational area. Besides, several questions about ERS applications were analyzed in [Rivera et al 2018], such as educational areas covered, approaches used to generate recommendations, and which platform is used. The evaluation method was also investigated, describing whether they used surveys, case studies, or experiments.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Educational Recommendation Systems: a systematic mapping

Marante

Silva

Gomes

et al. 2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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Recommendation systems (RS) have been used in many scenarios, from entertainment to health. Inside the RS area, Educational Recommendation Systems (ERS) are becoming popular, been used for different types of recommendations such as recommending materials, exercises, and learning paths. As ERS works in a different scenario of classics RS, ERS requires specific evaluation metrics. However, the task of evaluating ERS is difficult once the educational field has its features to be analyzed. To help other researchers in this field, this work presents a systematic mapping on methods used for evaluating ERS. This study analyzed 91 papers of the last five years and provide an overview of the main methodologies, subject, metrics, and trends in the evaluation of ERS.

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“…Regarding actors/actresses, these tags are not included explicitly. Instead, the authors link the movies of MovieLens dataset with their corresponding web pages at Internet Movie Database (IMDb) 5 from the u.item file of each MovieLens dataset, and extract actors/actresses from the IMDB database. One movie in 100K variation contains at least 1 and at most 45 actors/actresses while one movie in MovieLens 1M consists of at least 2 and at most 235 actors/actresses.…”

Section: A Datasetsmentioning

confidence: 99%

“…Recommender systems (RSs) have been developed to generate meaningful recommendations any products or items to a group of users that might get their attention. RSs [1], [2] are now widely use in research [3], industry [4], and education community [5], [6], where many approaches have been developed for improving recommendations. Many real world examples of recommendation operation can be found for books on Amazon [7], music on Spotify [8], activities on social media [9], [10], services on Twitter [11], [12], or movies on Netflix [13].…”

Section: Introductionmentioning

confidence: 99%

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Duong-Trung¹,

Nguyen²,

Ha³

et al. 2020

IJACSA

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A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. contentbased filtering. Therefore, the systems should come with predetermined information either by users or by movies. One interesting research question should be asked: "what if this information is missing or not manually manipulated?" The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our approach has eliminated the effect of the coldstart problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute adequate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.

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Recommendation Systems in Education: A Systematic Mapping Study

Cited by 63 publications

References 16 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

Evaluating Educational Recommendation Systems: a systematic mapping

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

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