Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Unram Journal of Community Service (UJCS) Journal of Pedagogi
Reny Syafrida
Universitas Darma Persada
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Implementation of Micro Learning to Improve Digital Literacy among High School Students David Darwin; Reny Syafrida; Hin Goan Gunawan; Thalita Fitria Nuryanti; Egi Jenal Mutakin
Unram Journal of Community Service Vol. 6 No. 4 (2025): December
Publisher : Pascasarjana Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ujcs.v6i4.1361

Abstract

Digital literacy has become an essential competency for secondary school students as they navigate the rapid flow of online information. Preliminary identification at SMA Negeri 67 Jakarta indicated that most students still demonstrated low levels of digital literacy, particularly in evaluating information and using learning applications effectively. This community service program implemented a micro-learning model as a structured, concise, and accessible instructional approach delivered through students' digital devices. The method consisted of needs analysis, micro-learning content development, training sessions, individual mentoring, and evaluation through pre- and post-tests and classroom observations. The results showed a substantial improvement in students' digital literacy, with average scores increasing from 55 to 78. The distribution of student scores also shifted positively, marked by a significant reduction in low-score categories and an increase in high-achievement groups. Beyond cognitive gains, the program enhanced student engagement, independence, and collaborative skills during learning activities. These findings indicate that micro-learning is an effective and contextually appropriate approach for strengthening digital literacy in secondary education, and it holds strong potential for further development as a sustainable digital learning strategy.
The Use of Artificial Intelligence Chatbots Based on Natural Language Processing (NLP) to Improve Mandarin Speaking Skills David Darwin; Febi Nur Biduri; Reny Syafrida
Journal of Pedagogi Vol. 3 No. 4 (2026): Journal of Pedagogi - August
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/86f9bb47

Abstract

Mandarin is one of the most demanding languages for adult foreign learners to speak fluently, owing to its tonal system, syllable-based homophony, and the absence of alphabetic correspondence between sound and script. Conventional classroom instruction rarely supplies the volume of individualized, low-stakes oral practice that tonal accuracy and fluency require. Artificial intelligence (AI) chatbots built on natural language processing (NLP) offer a scalable alternative, simulating conversational partners that can listen, transcribe, evaluate, and respond to spoken language in real time. This article reports a systematic literature review synthesizing 25 empirical and conceptual studies, published between 2021 and 2026, on the use of NLP-based AI chatbots to improve second-language speaking skills, with particular attention to Mandarin/Chinese-as-a-foreign-language contexts. Following a PRISMA-informed search and screening procedure across Google Scholar, Scopus, ERIC, and major computer-assisted language learning journals, studies were thematically synthesized. The review finds consistent evidence that chatbot-mediated practice improves oral fluency, pronunciation, and willingness to communicate while reducing speaking anxiety, but that this evidence is concentrated in English-as-a-foreign-language contexts; direct evidence on Mandarin tone acquisition remains scarce. The review's novelty lies in explicitly separating general EFL findings from the small Mandarin-specific evidence base and mapping NLP subcomponents onto discrete Mandarin speaking sub-skills. Implications for chatbot design and future research are discussed.
Co-Authors David Darwin Egi Jenal Mutakin Febi Nur Biduri Hin Goan Gunawan Thalita Fitria Nuryanti