Learning Content Recommender System for Instructors of Programming Courses

Chau, Hung; Barria-Pineda, Jordan; Brusilovsky, Peter

doi:10.1007/978-3-319-93846-2_9

Cited by 9 publications

(12 citation statements)

References 7 publications

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“…Though every aspect has to some extent, influences e-learning, earlier research findings show that the organization and technological aspect mainly influenced the e-learning implementation (Chau et al, 2017;Priatna, Maylawati, Sugilar, & Ramdhani, 2020). Hence, system quality can be gauged based on the perceived usefulness, sustainable functioning, trustworthiness, data sharing, and integrated feedback features (Taat & Francis, 2020).…”

Section: Quality Of E-system and E-learningmentioning

confidence: 99%

“…The strategic aspects inducing the approval of e-learning between the students are helpfulness, teachers' abilities, perceived eminence of the e-learning management system, the shared content, and accessible procedural supports (Chau, Barria-Pineda & Brusilovsky 2017). Bauwen et al (2020) noted that students assess the e-learning system's overall performance regarding the benefits, usefulness, response time, and simplified integrated features to receive academic content (Taat & Francis, 2020;Bauwen et al, 2020).…”

Section: Quality Of E-system and E-learningmentioning

confidence: 99%

See 1 more Smart Citation

Escaping the COVID-19 Pandemic with High-Speed E-Learning: Capabilities, Engagement, and Quality in Malaysian Higher Education

Shehzadi¹,

Raza²,

Zaman³

2021

Media Watch

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Despite growing optimism, the COVID-19 pandemic has overstayed with its new 'delta variant' sweeping across continents. Consequently, higher education institutions across the globe have strategically switched to e-learning platforms to escape the massive uncertainties triggered by the ongoing global pandemic. Hence, the attraction and engagement of millions of new e-learners towards technology-enabled virtual learning environments are highly dependent on critical factors such as quality of e-service, e-information, and e-system, etc., for e-learning success. To address these emerging challenges and to bridge the research gap, the present study empirically explored the effects of e-learning communicat ion networks, includ ing the quality of e-service, e-information, and e-system, on e-learning in Malaysian Higher Education. Notably, the mediating mechanism of e-learner's engagement in the processual and holistic model of e-learning quality was also examined. Drawing on study data of 450 e-learners and utilizing structural equational modeling (SEM), the findings established that e-learning quality is significantly influenced by e-service quality, e-system quality, and e-information quality, as mediated through e-learner's engagement. The study implications include new empirical evidence on boosting e-learning quality during the pandemic-induced global education crisis and identifying the critical drivers of communication technologies in higher education (i.e., quality of e-service, e-information, and e-system) to better interpret the changing ingredients of e-learner's engagement.

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Section: Quality Of E-system and E-learningmentioning

confidence: 99%

Section: Quality Of E-system and E-learningmentioning

confidence: 99%

Escaping the COVID-19 Pandemic with High-Speed E-Learning: Capabilities, Engagement, and Quality in Malaysian Higher Education

Shehzadi¹,

Raza²,

Zaman³

2021

Media Watch

View full text Add to dashboard Cite

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“…Both works illustrated the presented algorithms' effectiveness in predicting learners' preferences. Additionally, Chau et al (2018) assisted instructors with recommendations on the most relevant material to teach. Reciprocal recommendations have been investigated by Labarthe et al (2016) and Potts et al (2018).…”

Section: Educational Recommender Systems In the Artificial Intelligence Contextmentioning

confidence: 99%

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Marras

Boratto

Ramos

et al. 2021

Int J Artif Intell Educ

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Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities.

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“…Tal estratégia falha em escalabilidade, visto que as anotações exigem esforço manual e, muitas vezes, os problemas não são anotados com os metadados necessários. [Chau et al 2017] realizaram uma análise estrutural de códigos-fonte feitos por alunos ao longo de uma disciplina de programação. A partir dela, foi desenvolvido um assistente de conteúdo que tem o objetivo de recomendar aos instrutores tópicos que devem ser cobertos dentro de uma disciplina de programação com temas necessários para criar uma unidade curricular adequada para a disciplina.…”

Section: Trabalhos Relacionadosunclassified

“…A partir dela, foi desenvolvido um assistente de conteúdo que tem o objetivo de recomendar aos instrutores tópicos que devem ser cobertos dentro de uma disciplina de programação com temas necessários para criar uma unidade curricular adequada para a disciplina. Diferente de [Chau et al 2017] e [Paula et al 2014], nosso foco está na recomendação de problemas e não tópicos. Ademais, além dos códigos, os métodos deste estudo são construídos com base nos enunciados dos problemas e nas interações dos alunos com o JO.…”

Section: Trabalhos Relacionadosunclassified

Recomendação Automática de Problemas em Juízes Online Usando Processamento de Linguagem Natural e Análise Dirigida aos Dados

Júnior¹,

Pereira²,

Oliveira³

et al. 2020

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

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Estudantes regularmente enfrentam dificuldades para encontrar problemas adequados em juízes online devido ao grande volume de exercícios disponíveis nesses sistemas. Nesse sentido, este estudo propõe e avalia métodos para recomendação automática de problemas em juízes online, em que as recomendações são realizadas a partir de um problema alvo, resolvido anteriormente pelo aluno. Um total de 324 recomendações foram avaliadas por 15 estudantes e 3 professores de programação, utilizando uma abordagem duplamente cega. Como resultado, mostrou-se que as recomendações dadas pelos métodos propostos foram mais adequadas do que as do baseline em termos de esforço empregado e sucesso obtido (maior taxa de acerto e menor taxa de erro e desistência). Por fim, acredita-se que os métodos de recomendação podem ser usados para auxiliar professores a criarem listas de exercícios.

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Learning Content Recommender System for Instructors of Programming Courses

Cited by 9 publications

References 7 publications

Escaping the COVID-19 Pandemic with High-Speed E-Learning: Capabilities, Engagement, and Quality in Malaysian Higher Education

Escaping the COVID-19 Pandemic with High-Speed E-Learning: Capabilities, Engagement, and Quality in Malaysian Higher Education

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Recomendação Automática de Problemas em Juízes Online Usando Processamento de Linguagem Natural e Análise Dirigida aos Dados

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