A review on Recommender Systems for course selection in higher education

Lynn, Nathan; Emanuel, Andi Wahju Rahardjo

doi:10.1088/1757-899x/1098/3/032039

Cited by 16 publications

(11 citation statements)

References 21 publications

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“…In a qualitative study of Lynn and Emanuel (2021), the different recommender systems were surveyed and evaluated. The research focused on four major approaches such as Hybrid Recommendation, Knowledge-Based Recommendation, Content-Based Recommender methods and Collaborative filtering (CF).…”

Section: Related Studiesmentioning

confidence: 99%

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Sabado

2023

IJCSR

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Purpose -This paper discusses a new approach combining the network graph theory on minimum spanning tree and ensemble-based machine learning algorithm for weight generation to solve an optimization problem for common university advising processes. University advising requires an expert's validation to identify an optimal set of courses that can be recommended to students to finish their program of study at an earliest time while giving heavy considerations on the volume of workload for a given semester.Method -The study was analyzed utilizing the historical dataset from students' academic profiles from a local university from the Philippines. The process undergoes a set of weight generation, MST model application and ensemble scoring method.Results -The proposed algorithm was validated using the expert validated advising results which yielded to 92% accuracy.Conclusion -This paper attempts to develop a solution to the tedious process of student advising through representing the curriculum in a directed graph and implementing a minimum spanning tree algorithm with ML-based weight edges. 1724Recommendations -To address the potential reasons on the detected accuracies, it is further recommended to include all other constraints such as accommodating corequisites between courses.Practical Implications -The proposed algorithm can be implemented in a web-based application or integrated in an enrollment information system for a more justifiable appreciation of the envisioned process improvement.

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Section: Related Studiesmentioning

confidence: 99%

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Sabado

2023

IJCSR

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“…It can exploit the knowledge about the learning domain to offer the best solution to the learners [153]. The BF systems are mainly established on learner requirements and include only an essential quantity of historical information [175]. Therefore, they are usually successful at handling cold-start problems.…”

Section: Rssmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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“…Os sistemas de recomendac ¸ão são algoritmos que auxiliam nas escolhas de atividades a serem exploradas por um indivíduo. Estes sistemas podem se basear em diversos fatores, como por exemplo preferências e histórico dos indivíduos [7]. São aplicados em diversos servic ¸os, como em streaming de músicas e filmes, e.g.…”

Section: Trabalhos Relacionadosunclassified

“…São várias as abordagens de recomendac ¸ão já utilizadas para a selec ¸ão de disciplinas, sendo elas filtragem colaborativa, baseada em conhecimento, baseada em conteúdo e métodos híbridos [7]. Alguns trabalhos focam na predic ¸ão de notas de disciplinas para recomendac ¸ão, de modo a selecionar as disciplinas Top-n [10], [11].…”

Section: Trabalhos Relacionadosunclassified

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Um sistema imuno-inspirado de recomendação de disciplinas para cursos superiores

Braz¹,

Silva²

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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A utilização dos sistemas de recomendação em aplicações acadêmicas pode servir para auxiliar o aluno a selecionar disciplinas de acordo com suas habilidades, interesses e desempenho em disciplinas cursadas anteriormente. No presente trabalho propomos a implementação de um sistema de recomendação baseado em sistema imunológico artificial, tomando como base um banco de dados com informações acadêmicas de alunos de Engenharia de Redes de Comunicação.A utilização do sistema imunológico artificial confere uma diversidade na vizinhança que é tida como base para fazer a recomendação, devido a maior diversidade entre os vizinhos que tenham alguma semelhança ao aluno alvo, é esperada uma abrangência maior nas opções de disciplinas que podem ser recomendadas.Experimentos foram conduzidos para avaliar o desempenho geral da abordagem proposta.Estudos iniciais mostraram que comparada a técnica básica de filtragem colaborativa, há um expressivo aumento do número de recomendações aliado a uma maior precisão, podemos inferir que a rede formada pelo AIS leva a obtenção de uma vizinhança que tem abrangência maior do conjunto de matérias relevantes.

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A review on Recommender Systems for course selection in higher education

Cited by 16 publications

References 21 publications

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Personalisation methods in e‐learning‐A literature review

Um sistema imuno-inspirado de recomendação de disciplinas para cursos superiores

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