Recommendation Systems in Education: A Systematic Mapping Study
10 Jan 2018-pp 937-947
TL;DR: The results provide some findings regarding how recommendation systems can be used to support main areas in education, what approaches techniques or algorithms recommender systems use and how they address different issues in the academic world.
Abstract: Several researchers study recommendation systems to assist users in the retrieval of relevant goods and services, mostly used in e-commerce. However, there is limited information of the impact of recommender systems in other domains like education. Thus, the objective of this study is to summarize the current knowledge that is available as regards recommendation systems that have been employed within the education domain to support educational practices. By performing a systematic mapping study, a total of 44 research papers have been selected, reviewed and analyzed from an initial set of 1181 papers. Our results provide some findings regarding how recommendation systems can be used to support main areas in education, what approaches techniques or algorithms recommender systems use and how they address different issues in the academic world. Moreover, this work has also been useful to detect some research gaps and key areas where further investigation should be performed, like the introduction of data mining and artificial intelligence in recommender system algorithms to improve personalization of academic choices.
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TL;DR: This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System, which can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query.
79 citations
TL;DR: The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as to detect any gaps in this area for future research work.
Abstract: Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.
42 citations
11 Feb 2021
TL;DR: In this paper, the authors presented the state of the art of methodologies used in course Recommender Systems along with the summary of the types of data sources used to evaluate these techniques.
Abstract: 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.
22 citations
01 Mar 2023
TL;DR: In this article , the authors applied AI-enabled personalized video recommendations to stimulate students' learning motivation and engagement during a systems programming course in a flipped classroom setting, and quantitatively measured students' engagement based on their learning profiles in a learning management system.
Abstract: The flipped classroom approach is aimed at improving learning outcomes by promoting learning motivation and engagement. Recommendation systems can also be used to improve learning outcomes. With the rapid development of artificial intelligence (AI) technology, various systems have been developed to facilitate student learning. Accordingly, we applied AI-enabled personalized video recommendations to stimulate students' learning motivation and engagement during a systems programming course in a flipped classroom setting. We assigned students to control and experimental groups comprising 59 and 43 college students, respectively. The students in both groups received flipped classroom instruction, but only those in the experimental group received AI-enabled personalized video recommendations. We quantitatively measured students’ engagement based on their learning profiles in a learning management system. The results revealed that the AI-enabled personalized video recommendations could significantly improve the learning performance and engagement of students with a moderate motivation level.
18 citations
01 Dec 2019
TL;DR: It is shown that there is a big potential to implement a personalised recommender system in e-learning based on the students learning style, and an ontology of material contentbased on the different learning styles is proposed.
Abstract: Online shopping has become an important part of lifestyle nowadays. Despite their many practical advantages, the users of online shopping systems can be overwhelmed with the abundant information about the goods they want to buy. While some users start their search with a preference for certain items or manufacturers, others may find it difficult to narrow down the range of options being offered. The recommender system can assist the users to filter the information and show the most relevant items to the users. Despite being very popular in ecommerce area, research on recommender systems for education is still underexplored. Similar to the users of ecommerce system, some students may also feel overwhelmed by the available choices of material contents offered by the elearning system in which, it does not always suit to their learning style. This is important as some experts in educational psychology suggest that students need to learn by following their personal learning style. We propose an implementation design of e-learning recommender system based on a logic approach, APARELL (Active Pairwise Relation Learner), which has been implemented for used car sales domain. There is an opportunity to apply the same procedure for e-learning system to help the student to choose the best material according to their preferences. We also propose an ontology of material content based on the different learning styles. In this paper, we show that there is a big potential to implement a personalised recommender system in e-learning based on the students learning style.
16 citations
Cites background from "Recommendation Systems in Education..."
...A recent study of a systematic mapping study to investigate the use of recommendation systems in education by Rivera, Tapia-Leon and Lujan-Mora [1], shows that personalisation is one of the most main issue addressed by recommender system in education....
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References
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TL;DR: The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners and European researchers appear to be the leading exponents of systematic literature reviews.
Abstract: Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners.
2,843 citations
TL;DR: There was a need to provide an update of how to conduct systematic mapping studies and how the guidelines should be updated based on the lessons learned from the existing systematic maps and SLR guidelines.
Abstract: Context Systematic mapping studies are used to structure a research area, while systematic reviews are focused on gathering and synthesizing evidence. The most recent guidelines for systematic mapping are from 2008. Since that time, many suggestions have been made of how to improve systematic literature reviews (SLRs). There is a need to evaluate how researchers conduct the process of systematic mapping and identify how the guidelines should be updated based on the lessons learned from the existing systematic maps and SLR guidelines. Objective To identify how the systematic mapping process is conducted (including search, study selection, analysis and presentation of data, etc.); to identify improvement potentials in conducting the systematic mapping process and updating the guidelines accordingly. Method We conducted a systematic mapping study of systematic maps, considering some practices of systematic review guidelines as well (in particular in relation to defining the search and to conduct a quality assessment). Results In a large number of studies multiple guidelines are used and combined, which leads to different ways in conducting mapping studies. The reason for combining guidelines was that they differed in the recommendations given. Conclusion The most frequently followed guidelines are not sufficient alone. Hence, there was a need to provide an update of how to conduct systematic mapping studies. New guidelines have been proposed consolidating existing findings.
1,598 citations
03 Dec 2002
TL;DR: The use of web mining techniques are suggested to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process.
Abstract: A recommender system in an e-learning context is a software agent that tries to "intelligently" recommend actions to a learner based on the actions of previous learners. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. These recommendation systems have been tried in e-commerce to entice purchasing of goods, but haven't been tried in e-learning. This paper suggests the use of web mining techniques to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process. These techniques are considered integrated web mining as opposed to off-line web mining used by expert users to discover on-line access patterns.
412 citations
TL;DR: The results of the comparative study show the effectiveness of the proposed model in that students who performed personalized collaborative e-learning activities achieved better course results, and encourage the further application of the model to other computer science courses.
Abstract: Blended learning models that combine face-to-face and online learning are of great importance in modern higher education. However, their development should be in line with the recent changes in e-learning that emphasize a student-centered approach and use tools available on the Web to support the learning process. This paper presents research on implementing a contemporary blended learning model within the e-course “Hypermedia Supported Education”. The blended model developed combines a learning management system (LMS), a set of Web 2.0 tools and the E-Learning Activities Recommender System (ELARS) to enhance personalized online learning. As well as incorporating various technologies, the model combines a number of pedagogical approaches, focusing on collaborative and problem-based learning, to ensure the achievement of the course learning outcomes. The results of the comparative study show the effectiveness of the proposed model in that students who performed personalized collaborative e-learning activities achieved better course results. These findings encourage the further application of the model to other computer science courses.
73 citations
12 Dec 2008
TL;DR: This study analyzes the requirement of a web-based e-learning recommendation system, and divides the system workflow into five sections: data collection, data ETL, model generation, strategy configuration, and service supply.
Abstract: E-learning recommendation system helps learners to make choices without sufficient personal experience of the alternatives, and it is considerably requisite in this information explosion age. In our study, the user-based collaborative filtering method is chosen as the primary recommendation algorithm, combined with online education. We analyze the requirement of a web-based e-learning recommendation system, and divide the system workflow into five sections: data collection, data ETL, model generation, strategy configuration, and service supply. Moreover, an architecture is proposed, based on which further development can be accomplished. In this architecture, there are seven modules, and four of them are core modules: recommendation models database, recommendation system database, recommendation management, data/model management.
66 citations