Intelligent Recommendation System for Course Selection in Smart Education

Jia-jun, Lin; Pu, Haitao; Li, Yibin; Lian, Jian

doi:10.1016/j.procs.2018.03.023

Cited by 80 publications

(49 citation statements)

References 4 publications

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“…Many papers have taken advantage of this to make predictions about students' behavior or to develop security measures to detect network intrusions. For this reason, some papers used more specific methods in their data collection and analysis techniques: Decision Tree [14][15][16], Random Tree [17], Random Forest [15,17,18], Artificial Neural Network (ANN) [15,18], Convolution Neural Networks (CNNs) [12,19], Naïve Bayes [15,20,21], K-means Clustering [20,22], k-Nearest Neighbor (K-NN) [21,23] and others [24][25][26][27] including Bayesian Network, Graph-based Clustering, Local Binary Patterns Histograms and Multimedia and Agents based Question Answering System (MAQAS) (see Table 4).…”

Section: Search Resultsmentioning

confidence: 99%

Smart Education: A Review and Future Research Directions

Martín

Alario-Hoyos

Kloos

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Research and development often move forward based on buzzwords. New terms are coined to summarize new developments, often with several interpretations and without a formal definition. The term Smart Education has been coined to represent a move forward in technology-enhanced education, but what is behind it? Does it represent something essentially different from the educational technologies used before? In this paper, we do a systematic literature review to understand how this term is used, what the technologies behind it are, and what promises are made. We conclude that although the term is fuzzy, there are indeed several developments available today that can make educational technologies much more adapted to the learner and therefore underpin the learning in a smarter way.

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Section: Search Resultsmentioning

confidence: 99%

Smart Education: A Review and Future Research Directions

Martín

Alario-Hoyos

Kloos

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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“…If later teacher A chooses a scenario from the library that teacher B has not chosen yet, the user filtration system will offer it to the second teacher, since it is likely that this scenario will be suitable for this teacher too. The collaborative filtering method is considered to be a digital form of word-of-mouth communication based on the fact that people tend to choose things recommended to them by other people who they know and trust [11][12].…”

Section: Methodsmentioning

confidence: 99%

Improving the efficiency of developing meta-subject scenarios in the Moscow Electronic School by means of educational analytics

Lavrenova

Yarmakhov

2021

SHS Web Conf.

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One of the problems connected with the development of meta-subject skills in school students is the lack of publicly available educational materials with a focus on such skills. A possible solution is the formation of a collection of meta-subject materials stored in the library of learning scenarios of the Moscow Electronic School — now there are over 40,000 learning scenarios that have undergone moderation. This article presents the results of research that identified a cluster of teachers most inclined to create meta-subject scenarios and suggested recommendations for motivating teachers to create such scenarios. To achieve this purpose, a sample of authors of such scenarios published by the Moscow Electronic School were analyzed and clustered with the help of machine learning methods. As a result of this work, a gradient boosting algorithm was developed, which produced the best results. The clusters of users described as a result of the application of the algorithm followed five main behavior strategies in terms of the activity related to the creation of new scenarios. Teachers that are most likely to create meta-subject scenarios show interest in their colleagues’ scenarios not only in their subject but also in other academic disciplines taught at school, willingness to copy and customize them. To develop teachers’ readiness for the creation of meta-subject scenarios, it is recommended to conduct teacher training including their introduction to the best practices of developing such scenarios presented by the Moscow Electronic School. The research results are used in the development of a recommender system enabling easier search and navigation among the scenarios published by the Moscow Electronic School.

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“…In our review, a few publications proposed pure ML techniques for course recommendation [141,142]. Some selected studies used Randomwalk-based approach [143], SLM [144], Decision tree expression [145], Gaussian Mixture Model and capsule network [146].…”

Section: Data Mining (Dm) Approachesmentioning

confidence: 99%

“…Egbers et al [142] Preliminary work is presented here. clustering results reveals that students with good marks study quicker while students with lower marks need longer time to complete and could recommend study part-time to minimize the dropping out rate Lin et al [144] Suitable for recommending courses to existing students as initial course selection of a specific student is used in sparseness aggregation strategy. Evaluations were conducted on small set of data.…”

Section: Hybrid Approachmentioning

confidence: 99%

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

Guruge

Kadel

Halder

2021

Data

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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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Intelligent Recommendation System for Course Selection in Smart Education

Cited by 80 publications

References 4 publications

Smart Education: A Review and Future Research Directions

Smart Education: A Review and Future Research Directions

Improving the efficiency of developing meta-subject scenarios in the Moscow Electronic School by means of educational analytics

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

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