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Proceedings ArticleDOI

A Course Recommender System based on Graduating Attributes

TL;DR: Experimental results using correlation thresholding and the nearest neighbors approach show that a course recommendation system for students based on the assessment of their "graduate attributes" can be effective when an active neighborhood of 10-15 students is used and that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm.
Abstract: Assessing learning outcomes for students in higher education institutes is an interesting task with many potential applications for all involved stakeholders (students, administrators, potential employers, etc.). In this paper, we propose a course recommendation system for students based on the assessment of their "graduate attributes" (i.e. attributes that describe the developing values of students). Students rate the improvement in their graduating attributes after a course is finished and a collaborative filtering algorithm is utilized in order to suggest courses that were taken by fellow students and rated in a similar way. An extension to weigh the most recent ratings as more important is included in the algorithm which is shown to have better accuracy than the baseline approach. Experimental results using correlation thresholding and the nearest neighbors approach show that such a recommendation system can be effective when an active neighborhood of 10-15 students is used and show that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm.
Citations
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Journal ArticleDOI
TL;DR: A hybrid RS that combines Collaborative Filtering and Content-based Filtering using multiple criteria related both to student and course information to recommend the most suitable courses to the students is presented.
Abstract: The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help students to choose courses that suit to their personal interests and their academic performance. This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF) using multiple criteria related both to student and course information to recommend the most suitable courses to the students. A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration which include both the most relevant criteria and the configuration of the rest of parameters. The experimental study has used real information of Computer Science Degree of University of Cordoba (Spain) including information gathered from students during three academic years, counting on 2500 entries of 95 students and 63 courses. Experimental results show a study of the most relevant criteria for the course recommendation, the importance of using a hybrid model that combines both student information and course information to increase the reliability of the recommendations as well as an excellent performance compared to previous models.

42 citations

Journal ArticleDOI
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

Journal ArticleDOI
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

Proceedings Article
01 Jul 2019
TL;DR: The course recommender system that is developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands is presented, which aims to complement academic advising and help students make better-informed course selections.
Abstract: Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we have developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands. It aims to complement academic advising and help students make better-informed course selections. The system recommends courses whose content best matches the student’s academic interests, issues warnings for courses that are too advanced given the student’s academic background and, in the latter case, suggests suitable preparatory courses. We base the course recommendations on a topic model fitted on course descriptions, and the warnings on a sparse predictive model for grade based on students’ past academic performance and level of academic expertise. Preparatory courses consist of courses whose content has the best preparatory value according to the predictive model. We find that course recommendations are relevant for a wide range of academic interests present in the student population and that students found recommendations for courses at other departments especially helpful. The preparatory courses often lack coherence with the target course and need to be improved.

20 citations


Cites background from "A Course Recommender System based o..."

  • ...[1] address the issue of recommending courses that help students overcome their deficiencies whilst accounting for changes over time....

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Proceedings Article
01 Jul 2018
TL;DR: A genetic algorithm is proposed which automatically discovers the importance of the different criteria assigning weights to each one of them and which is the combination of multiple criteria which provides better results.
Abstract: This paper describes a multiple criteria approach based on a hybrid method of Collaborative Filtering (CF) and ContentBased Filtering (CBF) for discovering the most relevant criteria which could affect the elective course recommendation for university students. In order to determine which factors are the most important, it is proposed a genetic algorithm which automatically discovers the importance of the different criteria assigning weights to each one of them. We have carried out an in-depth study using a real data set with more than 1700 ratings of Computer Science graduates at University of Cordoba. We have used different proposals and different weights for each criterion in order to discover what is the combination of multiple criteria which provides better results.

17 citations


Cites background from "A Course Recommender System based o..."

  • ...More recently, both the competences provided to students and their relevance in their recommendation [4, 1] and the application of semantic analysis [11] has been adressed....

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References
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Journal ArticleDOI
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

9,873 citations


"A Course Recommender System based o..." refers background in this paper

  • ...This can also be considered as “Multicriteria Ratings” in which there exist multiple rating for each criteria of the item (Adomavicius and Tuzhilin, 2005)....

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  • ...Such a direction would require to go beyond the traditional recommendation representation of users× ratings and adopt a “multicriteria rating” approach (Adomavicius and Tuzhilin, 2005)....

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Proceedings ArticleDOI
22 Oct 1994
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Abstract: Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

5,644 citations


"A Course Recommender System based o..." refers background in this paper

  • ...The idea of CF methods lies on the fact that people who agreed in their evaluations for past items are likely to agree again for future items (Resnick et al., 1994)....

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Posted Content
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Abstract: Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.

4,883 citations

Proceedings ArticleDOI
17 Oct 2000
TL;DR: This paper investigates several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of producing useful re ommendations to ustomers and devise and apply their ombinations on the authors' data sets to ompare for re Ommendation quality and performan e.
Abstract: Re ommender systems apply statisti al and knowledge disovery te hniques to the problem of making produ t re ommendations during a live ustomer intera tion and they are a hieving widespread su ess in E-Commer e nowadays. In this paper, we investigate several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of produ ing useful re ommendations to ustomers. In parti ular, we apply a olle tion of algorithms su h as traditional data mining, nearest-neighbor ollaborative ltering, and dimensionality redu tion on two di erent data sets. The rst data set was derived from the web-pur hasing transa tion of a large Eommer e ompany whereas the se ond data set was olle ted from MovieLens movie re ommendation site. For the experimental purpose, we divide the re ommendation generation pro ess into three sub pro esses{ representation of input data, neighborhood formation, and re ommendation generation. We devise di erent te hniques for di erent sub pro esses and apply their ombinations on our data sets to ompare for re ommendation quality and performan e.

1,913 citations


"A Course Recommender System based o..." refers background in this paper

  • ...Results have shown (Sarwar et al., 2000) that two techniques can effectively determine how many students will be included in the active student neighbourhood: Correlation thresholding and best nneighbours with common courses threshold (direct application of the k-nearest neighbours algorithm)....

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Journal ArticleDOI
01 Nov 2010
TL;DR: The most relevant studies carried out in educational data mining to date are surveyed and the different groups of user, types of educational environments, and the data they provide are described.
Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

1,723 citations


"A Course Recommender System based o..." refers background in this paper

  • ...There have been many data mining systems developed in education (Romero and Ventura, 2010) and especially on how recommender systems can be utilized for suggesting courses (O’Mahony and Smyth, 2007) or master programs (Surpatean et al., 2012)....

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  • ...There have been many data mining systems developed in education (Romero and Ventura, 2010) and especially on how recommender systems can be utilized for suggesting courses (O’Mahony and Smyth, 2007) or master programs (Surpatean et al....

    [...]