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All Journal International Journal of Advances in Intelligent Informatics Jurnal Eduscience (JES)
Mannix, Ilma Alpha
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Computer Science & IT Education Other

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CONSTRUCTIVISM IN ONLINE AND HYBRID LEARNING BEFORE AND AFTER COVID-19: A SYSTEMATIC LITERATURE REVIEW Pratama, Faiz Rizqullah; Santoso, Harry Budi; Junus, Kasiyah; Michael, Jahns; Mannix, Ilma Alpha; Athaya, Hisyam
JURNAL EDUSCIENCE Vol 12, No 4 (2025): Jurnal Eduscience (JES), (Authors from Malaysia, Thailand, and Indonesia)
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jes.v12i4.7314

Abstract

Purpose – Accelerated by the COVID-19 epidemic, the move to online and hybrid learning settings has underlined the need for constructivist ideas in contemporary education. Digital education methods fit well with constructivism, which stresses active, student-centred learning via interaction and teamwork. This study explores the evolving application of constructivism in online and hybrid learning environments before and after the pandemic, identifying key opportunities, challenges, and factors influencing its effectiveness.Methodology – Covering 2016 to 2024, the article used a Systematic Literature Review (SLR) methodology using the Kitchenham technique. Literature selection guided by the PICOC framework, Boolean search techniques, and quality evaluations led to 69 chosen papers, including three primary phases: planning, execution, and reporting.Findings – The study discovered that online education employing Learning Management Systems (LMS), virtual reality (VR), and collaborative technologies has progressively integrated constructivism. These technologies enable peer cooperation, inquiry-based learning, and problem-based learning. The study revealed continuous problems despite these advances, including technological challenges, instructor readiness, and student participation. The efficacy of the constructivist method was strongly affected by social and technical factors like access to technology and collaboration dynamics.Contribution – The research provides an insightful analysis of how constructivism changes to fit the evolving educational environment. It gives teachers, legislators, and technology creators strategic ideas to create engaging, efficient, and inclusive digital-era learning environments.
Academic expert finding using BERT pre-trained language model Mannix, Ilma Alpha; Yulianti, Evi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1497

Abstract

Academic expert finding has numerous advantages, such as: finding paper-reviewers, research collaboration, enhancing knowledge transfer, etc. Especially, for research collaboration, researchers tend to seek collaborators who share similar backgrounds or with the same native languages. Despite its importance, academic expert findings remain relatively unexplored within the context of Indonesian language. Recent studies have primarily relied on static word embedding techniques such as Word2Vec to match documents with relevant expertise areas. However, Word2Vec is unable to capture the varying meanings of words in different contexts. To address this research gap, this study employs Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art contextual embedding model. This paper aims to examine the effectiveness of BERT on the task of academic expert finding. The proposed model in this research consists of three variations of BERT, namely IndoBERT (Indonesian BERT), mBERT (Multilingual BERT), and SciBERT (Scientific BERT), which will be compared to a static embedding model using Word2Vec. Two approaches were employed to rank experts using the BERT variations: feature-based and fine-tuning. We found that the IndoBERT model outperforms the baseline by 6–9% when utilizing the feature-based approach and shows an improvement of 10–18% with the fine-tuning approach. Our results proved that the fine-tuning approach performs better than the feature-based approach, with an improvement of 1–5%.  It concludes by using IndoBERT, this research has shown an improved effectiveness in the academic expert finding within the context of Indonesian language.
Co-Authors Athaya, Hisyam Evi Yulianti Harry Budi Santoso Junus, Kasiyah Michael, Jahns Pratama, Faiz Rizqullah