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All Journal Al Ishlah Jurnal Pendidikan Syntax Literate: Jurnal Ilmiah Indonesia Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Journal on Education Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Jurnal Pendidikan dan Konseling Jurnal Syntax Transformation jurnal syntax admiration DEIKTIS: Jurnal Pendidikan Bahasa dan Sastra al-fikrah: Jurnal Manajemen Pendidikan International Journal of Educational Research Excellence (IJERE) International Journal of Community Service (IJCS) Innovative: Journal Of Social Science Research Jurnal Pendidikan Siber Nusantara Jurnal Pengabdian Masyarakat dan Riset Pendidikan JOP Edumaspul: Jurnal Pendidikan Journal of Pedagogi International Journal of Teaching and Learning (INJOTEL) International Journal of Language and Ubiquitous Learning Journal of Social Science Utilizing Technology
Muh. Safar
Universitas Muhammadiyah Bone
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Prediction of Indonesian Learning Achievement Using Machine Learning Models Muh. Safar; Nina Anis
International Journal of Language and Ubiquitous Learning Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijlul.v3i1.1921

Abstract

Background. Student learning achievement is one of the important indicators in assessing the effectiveness of education. Various factors such as student attendance and socioeconomic status have been known to affect learning outcomes. However, the influence of access to technology in the context of education in Indonesia has not been studied in depth. In today's digital era, access to technology is an important aspect that can support or hinder the learning process of students. Purpose. This study aims to analyze the influence of student attendance, socioeconomic status, and access to technology on student learning achievement. In addition, this study also aims to test the accuracy of machine learning models in predicting student exam results based on these variables. Method. This study uses a quantitative approach with the application of machine learning models, including linear regression and decision trees. The data used includes students' test scores, attendance levels, socioeconomic status, and access to technology devices and networks. Results. The results of the analysis showed that student attendance, socioeconomic status, and access to technology had a significant influence on learning achievement. The machine learning model applied is able to predict students' exam results with a high level of accuracy, demonstrating the effectiveness of this approach in educational analysis. Conclusion. This study emphasizes the importance of external factors, especially access to technology, in predicting student learning achievement. A more inclusive education policy is needed by expanding access to technology and educational facilities, in order to support the equitable distribution of learning quality in all circles.
Language Learning through AI Chatbots: Effectiveness and Cognitive Load Analysis Muh. Safar; Dini Anggraheni
Journal of Social Science Utilizing Technology Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i3.1346

Abstract

Background. The use of AI chatbots in language learning has gained popularity due to their accessibility and ability to provide interactive practice. However, concerns regarding their effectiveness in improving language skills and the potential cognitive load they place on learners remain underexplored. With the increasing integration of AI in education, understanding how these tools impact language acquisition is critical for optimizing learning outcomes. Purpose. This study aims to evaluate the effectiveness of AI chatbots in language learning and analyze the cognitive load experienced by learners during chatbot interactions. Method. A mixed-methods approach was used, combining quantitative assessments of language proficiency before and after using an AI chatbot for a three-week period, with qualitative interviews to assess learner perceptions of cognitive load. Participants included 60 university students learning a new language. Results. The results indicate that AI chatbots significantly improved language skills, particularly in conversational fluency and vocabulary acquisition. However, a moderate level of cognitive load was reported by learners, primarily due to the need to simultaneously engage in real-time conversation and process feedback. While the cognitive load was not overwhelming, it varied based on individual learner characteristics, such as prior language proficiency and familiarity with AI tools. Conclusion. In conclusion, AI chatbots offer an effective method for enhancing language learning, particularly in improving fluency and vocabulary. However, managing cognitive load is crucial to maximizing their educational potential. Further research is recommended to explore adaptive chatbot designs that can tailor interactions to individual learner needs.
Co-Authors A. Fitriani A. Fitriani A. Muh. Taufiq A. Nurhabibi Marwil Agus Mukholid Agusalim Agusalim Agustinus Lewerissa Akmal Hamsa Almira Ulimaz Ambarita, Monica Roito Andi Srimularahmah Arief Setyo Nugroho Armita Permatasari Arum Putri Rahayu AUGUSTA DE JESUS MAGALHAES Baktiar Nasution Baso Intang Sappaile Cindi Citra Siwi Hanayanti Corolina Sasabone Debi Febianto Diana Sartika Didi Sudrajat Dini Anggraheni Dwi Syukriady Emanuel Bai Samuel Kase Endang Fatmawati Eneng Martini Faisal Kemal Fakhry Amin Fryan Sopacua Fujiono Fujiono Grace Citra Dewi Idrus Idrus Ihramsari Akidah Ilwandri Ilwandri Indra Tjahyadi Irna Fitriana Jasiah Jasiah Jihan Jihan Kaniah Kaniah Khalid Rahman Laros Tuhuteru Lestari Budianto Loso Judijanto Luis Santos M. Saufi Mas'ud Muhammadiah Mas’ud Muhammadiyah Mihrab Afnanda Moza Suzana MS. Viktor Purhanudin Muhammad Asdar Muhammad Ihsan Dacholfany Nina Anis Novelti Nur Indah Sari Nur Lilis Nuraini Nuramila Nuramila Nurmaya Medopa Nurwakia Nurwakia Peni Susapti R. Nurhayati Ratna Susanti Ria Agustina Ridayani Ridayani Rulik Endarwati Rustiyana, Rustiyana Sabil Mokodenseho Samuel Mamonto Sarniyati Sarniyati Sasabone, Carolina Sembiring, Darmawanta Septiana Wulandari Siti Shofiah Sitriah Salim Utina Solissa, Everhard Markiano Sri Yunita Ningsih St. Maryam Aras Suarnita Suarnita Sudadi Sukini Sukini Sumarni Rumfot Suud Sarim Karimullah Suwarni Suyatmo Suyuti Suyuti Syafruddin Syafruddin syahrul, muhammad Tabelessy, Novita Thrisia Monica Tomi Apra Santosa Tri Amanat Triyani Triyani Usmaedi Usmaedi Wahyu Windayanti Yoesoep Edhie Rachmad Yohanis Hukubun Yosep Heristyo Endro Baruno Yustina Sri Ekwandari