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All Journal Jurnal Pendidikan Matematika Jurnal Infinity AKSIOMA: Jurnal Program Studi Pendidikan Matematika Al Ishlah Jurnal Pendidikan Jurnal SOLMA JURNAL PENDIDIKAN TAMBUSAI Journal on Education JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Jurnal Pendidikan dan Konseling Educatif: Journal of Education Research Journal of Education and Learning Mathematics Research (JELMaR) Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Daengku: Journal of Humanities and Social Sciences Innovation Edukasia: Jurnal Pendidikan dan Pembelajaran Innovative: Journal Of Social Science Research Jurnal Pendidikan Terapan Educatif: Journal of Education Research SEMINAR NASIONAL SOSIAL, SAINS, PENDIDIKAN, HUMANIORA Jurnal Infinity
Swasti Maharani
Universitas PGRI Madiun
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Learn and Play Activities to Support Early Childhood Teacher’s Understanding of Electrical Energy Concepts Ihtiari Prastyaningrum; Alisa Alfina; Swasti Maharani
Jurnal Pendidikan Terapan Vol 4, No 2 May (2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jupiter.v4i2.935

Abstract

Purpose – This study aims to explore and strengthen early childhood education (ECE) teachers’ understanding of fundamental electrical energy concepts, including sources of electrical energy, energy conversion, and simple electrical circuits. It also seeks to equip teachers with the ability to design Learn and Play–based science activities that are concrete, exploratory, and grounded in children’s daily experiences. Methods – The research employed a mixed-methods approach, combining quantitative testing and qualitative phenomenological analysis. The study involved 20 ECE teachers from six institutions in Madiun City and Madiun Regency. The intervention was carried out across three phases: basic training, mentoring on theoretical and practical concepts, and pedagogical assistance. Data were obtained through pre-tests, post-tests, observation, learning plan assessments, and in-depth interviews. Findings – Results revealed significant improvements in teachers’ conceptual understanding and pedagogical readiness. Teachers demonstrated enhanced literacy regarding electrical energy, increased confidence in explaining basic scientific concepts, and improved skills in designing interactive Learn and Play activities. The mentoring phases also showed that teachers could connect personal experiences with scientific explanations, leading to more meaningful learning preparations and effective classroom implementation. Research implications – Strengthening teachers’ scientific literacy through structured mentoring can reduce misconceptions in early childhood science learning. The model used in this study may serve as a reference for ECE professional development programs focusing on science education. Originality – This study offers a unique integration of Learn and Play activities with phenomenological exploration of teachers’ personal experiences related to electrical phenomena. It fills the research gap on electrical concept literacy among early childhood teachers, a topic rarely examined in previous studies.
Predicting Student Academic Success Using Machine Learning Models: A Learning Analytics Approach in Higher Education Arief Hidayat; Swasti Maharani; Dendi Pratama; Ramadiani Ramadiani; S Sujito; Addy Septyawan; Dian Wardiana Sjuchro
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4881

Abstract

Rapid deployment of digital learning technologies in the higher education sector has created immense amounts of educational data that could be leveraged to enhance student success and institutional effectiveness. Nevertheless, student dropout, poor academic performance, and lack of retention continue to plague universities across the world. In most cases, identification of academically struggling students is often late since existing models are largely reactive. Therefore, there is need for development of advanced learning analytics models that are able to forecast student performance in higher education institutions. The current study seeks to create an artificial neural network (ANN)-based learning analytics framework to predict student success in higher education institutions. A predictive analytical approach based on quantitatively evaluating a sample of 1,000 undergraduate students was used in the current study. Various attributes used to evaluate the students included demographic information, academic performance, LMS activity, and learning behaviors. Learning analytics indicators used in the model included previous GPA, attendance rate, assignment completion rate, quiz scores, logins per week, learning hours per week, discussion engagement, engagement index, interaction scores, and learning consistency. In the analysis, the model was validated and tested against accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, and cross validation tests. Results showed that accuracy, precision, recall, F1-Score, and ROC-AUC of the ANN model were 92.8%, 91.4%, 93.7%, 92.5%, and 0.96, respectively. Based on these outcomes, previous GPA, attendance rate, assignment completion rate, and various engagement indicators were found to be the strongest predictors of student success in college. On the theoretical front, contributions of this study include AI-assisted student performance and behavior prediction. Practically, a sophisticated warning system was developed in this study to assist in effective academic advisement and planning for student retention and academic improvement strategies.
Co-Authors Addy Septyawan Addy Septyawan Adriyanto Moha Agung Nasrulloh Saputro, Agung Nasrulloh Ahmad Zul Fakar Alisa Alfina Anissa Teguh Saputri Annisa Nurhidayati Mu'arif Annisa Swastika Ardhya Pramesthi Regita Iriandre Arief Hidayat Aulia Rahma Ayu Apriliya Cinthyadewi Chika Yuliana Darmadi Darmadi Dendi Pratama Dian Wardiana Sjuchro Dwi Muklisum Dwi Ulfa Nurdahlia Edi Sudarto Edy Suprapto Evi Muafiah Fatriya Adamura Febrina Ridha Amalia Fidya Nurhuda Sukma Tama Fitriana Fitriana Franky Alfrits Oroh Haidar Ali Asnawi Hendrisa Rizqie Romandoni Husnul Khotimah Ica Endang Lestari Ihtiari Prastyaningrum Ika Krisdiana Indah Ika Ratnawati Indra Puji Astuti Ismail Djakaria Kurnia Sekarsari Laila Apriliana Lilis Asriani Putri Lingga Nico Pradana Luwes Handayani Majid Majid Meinanda Putri Zalsabella Mufidah Mufidah Muhammad Noor Kholid Muhammad Yusuf Afifullah Mutiara Suci Rahmadhani Nadhif Muhammad Mumtaz Perry Zakaria Putri Ayu Megawati Putri Nur Azizah Rahmi Moh. Amin Ramadiani - Riski Novitasari S Sujito Sanusi Sanusi Sanusi Sardulo Gembong Septa Devi Auliatul Alifah Setyansah, Reza Kusuma Sri Sunarni Syafrudin Katili Syahri Alqurni Syntia Rizki Nur Safitri Tiara Citra Maharani Firdaus Titin Masfingatin Toto Nusantara Tri Andari Tri Hariyati Nur Indah Sari Tulus Nindi Agustin Tutik Sulistyo Andayani Vera Dewi Susanti Viona Adinda Rosalina Wasilatul Murtafiah Widia Yunita Widia Yunita Winda Putri Novyani Yehas Affan Kirana Yohannes Eko Prasetyo Zeni Fadlila Agustina