Stakeholders' psychological factors affecting E-learning readiness in higher education community in Sri Lanka

Herath, Chaminda P.; Thelijjagoda, Samantha; Gunarathne, W. K. T. M.

doi:10.1109/umedia.2015.7297449

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…• 7 se concentram puramente no conceito de self-paced learning (HERATH et al, 2015a;HERATH et al, 2015b;GULATI, 2013;MAINA e KIHORO, 2017;CHEN et al, 2017;AMANDU et al, 2013;PEART et al, 2017); Também foi feito um levantamento dos conceitos e metodologias secundariamente citados, isto é, fora do tema central abordado. Nesse contexto, MOOCs foram citados 4 vezes (HERATH et al, 2015a;BAE et al, 2015;CUNNINGHAM, 2016;LENNON et al, 2015), hybrid learning foi mencionado em 3 artigos (KHLAISANG e LIKHITDAMRONGKIAT, 2015;KAPOOR et al, 2014;ALMAHASHEER, 2016), blended learning em 2 (LENNON et al, 2015;ZHANG et al, 2016), gamification em 1 (SCHULZ et al, 2014) e a ideia de e-learning metacognitivo apareceu em 1 artigo (GULATI, 2013).…”

Section: Resultsunclassified

“…• 15 fizeram uma abordagem genérica, aplicando somente conceitos e metodologias, e não utilizaram diretamente um sistema computacional de apoio ao ensino ou aprendizagem (HERATH et al, 2015a;GULATI, 2013;BAE et al, 2015;MAINA e KIHORO, 2017;SCHULZ et al, 2014, OYELERE et al, 2016, OKAZ, 2015ZHIYU, 2013;HENDRADJAYA et al, 2014;SHAHABADI e UPLANE, 2015;SCHULZ et al, 2015;LENNON et al, 2015;OOI et al, 2018;DEBNATH et al, 2014;PEART et al, 2017);…”

Section: Resultsunclassified

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Ferramentas Tecnológicas e Metodologias de Apoio à Aprendizagem Personalizada no Ensino Superior: uma Revisão Sistemática

Morais

Conceição

2019

Inform. educ.

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Tecnologias educacionais e e-learning são facilitadores da personalização do processo de aprendizagem e da promoção do self-paced learning. O objetivo deste estudo é analisar os desafios e os pontos positivos das implementações e modelos que abordam esses conceitos e propor funcionalidades para uma ferramenta de apoio à aprendizagem. Para isso, foi feita uma revisão sistemática da literatura seguindo a metodologia de Kitchenham. Foram selecionados 31 artigos de 4 bases internacionais, os quais foram classificados segundo conceitos, metodologias, plataformas, pontos positivos e desafios apresentados. A análise indicou o blended learning como metodologia mais abordada, a flexibilidade como ponto positivo mais citado e a falta de interação como maior desafio encontrado. Os resultados mostraram que há elementos de e-learning presentes em universidades do mundo todo e que ferramentas tecnológicas de apoio são eficientes na personalização e melhoria do processo de aprendizagem.

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Section: Resultsunclassified

Ferramentas Tecnológicas e Metodologias de Apoio à Aprendizagem Personalizada no Ensino Superior: uma Revisão Sistemática

Morais

Conceição

2019

Inform. educ.

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“…The findings revealed that 38.29% of studies focused on this realm. The dimension of those studies determines the factors that affect e-learning readiness and develop models (Herath et al, 2015; Rohayani et al, 2015; Babie et al, 2016; Ali et al, 2018; Al-araibi et al, 2018; Sáiz-Manzanares et al, 2019), covers the perspective from teachers and students (Akaslan and Law, 2011; Minnaar 2011; Gonza´lez, 2012; Alkhalaf et al, 2012; Allan et al, 2013; Azeiteiro et al, 2015; Fridrichová, 2015; Al-Samarraie et al, 2017), measures the impact and compares the readiness at countries level (Saekow and Samson, 2011; Bøe, 2018; Al-araibi et al, 2018; Mirabolghasemi et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

E-learning adoption in higher education: A review

Baig

Shuib

Yadegaridehkordi

2021

Information Development

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In recent years, e-learning has become pivotal in higher education sectors. Researchers are correlating novel approaches with e-learning to facilitate education. However, despite the increase in e-learning research, there is still a lack of comprehensive literature analysis of e-learning in the higher education sector. Thus, this study aims to conduct a systematic literature review of the literature on e-learning in higher education. This study classifies the selected studies according to the focus of the study, utilizes a theoretical model and framework, and research methods. Also, it presents limitations and future research directions of e-learning in the higher education sector. A systematic approach is conducted, and a total of 47 relevant articles published between the year 2011 and 2019 were selected based on the inclusion and exclusion criteria. The findings on selected studies focus on the adoption, acceptance, readiness, and user insight, as well as e-learning expansion and challenges in the higher education sector. This study also classified theoretical models and frameworks based on their usage in the pre-adoption, adoption, and post-adoption stages. The findings revealed that most of the theoretical models and frameworks were used at the post-adoption stage. Nevertheless, this study revealed that most of the current studies in this domain were conducted using a quantitative research approach. Finally, this study highlighted limitations and presented possible future research directions as a guide for further enhancement in e-learning and higher education studies.

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“…Another study in [33] investigated critical factors that influence students' preparedness to use e-learning systems in higher education, focusing on Wayamba University in Sri Lanka. The study results indicated that learners' willingness to participate in e-learning activities significantly affects their readiness to use the system.…”

Section: Related Workmentioning

confidence: 99%

E-Learning Readiness Assessment Using Machine Learning Methods

Zine,

Harrou,

Terbeche

et al. 2023

Sustainability

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Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model’s five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students’ abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students’ e-learning readiness.

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Stakeholders' psychological factors affecting E-learning readiness in higher education community in Sri Lanka

Cited by 6 publications

References 3 publications

Ferramentas Tecnológicas e Metodologias de Apoio à Aprendizagem Personalizada no Ensino Superior: uma Revisão Sistemática

Ferramentas Tecnológicas e Metodologias de Apoio à Aprendizagem Personalizada no Ensino Superior: uma Revisão Sistemática

E-learning adoption in higher education: A review

E-Learning Readiness Assessment Using Machine Learning Methods

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