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Evektivitas Model Project Based Learning Berbantuan Multi-Platfrom Terhadap Kemampuan Spatial Dan Representasi Is Soleha, Ruvatul Nida; Netriwati, Netriwati; Ambarwati, Riyama; Ayuni Suri, Indah Resti; Nendra, Fadly
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 8, No 2 (2026): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v8i2.26575

Abstract

The Role of Anthropomorphism in Shaping Students’ Emotional Attachment to AIED: A Triangular Theory of Love Approach Asmi Ulfiah; Al Haytsam Mappaita; Aprilianti Nirmala S; Pramudya Asoka Syukur; Andi Baso Kaswar; Riyama Ambarwati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.263

Abstract

In the digital learning era, Artificial Intelligence in Education (AIED) functions not only as an academic support tool but is also becoming an object of emotional attachment among students. While such attachment may enhance learning motivation, it also raises concerns about emotional dependence and its implications for students’ social and emotional well-being. This study investigates the effects of commitment, enthusiasm, emotional closeness, and anthropomorphic perceptions on students’ emotional dependence on AIED. A quantitative cross-sectional survey was conducted with 109 university students in Makassar using a 1–5 Likert-scale questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model explained 62.7% of the variance in emotional dependence on AI (R² = 0.627), indicating moderate to strong explanatory power. Emotional closeness (β = 0.324; t = 2.893; p = 0.004) and anthropomorphic perception (β = 0.440; t = 4.871; p < 0.001) significantly increased emotional dependence, whereas commitment to continued AI use (β = 0.092; t = 0.883; p = 0.377) and enthusiasm toward AI (β = 0.081; t = 0.901; p = 0.367) were not significant predictors. These findings suggest that emotional dependence is driven more by affective engagement and the perception of AI as socially human-like than by cognitive motivation or usage intention. AIED interaction therefore extends beyond functional support into a relational experience resembling interpersonal connection. Given the limited geographic scope, future studies should involve broader populations and employ mixed-method approaches to deepen understanding of emotional dynamics in AIED use.
Advancing mathematical representation abilities through scientifically-oriented contextual learning modules in junior secondary education Rizki Wahyu Yunian Putra; Riyama Ambarwati; Abi Fadila
Journal of Advanced Sciences and Mathematics Education Vol. 5 No. 1 (2025): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v5i1.763

Abstract

Background: Many students face difficulties in expressing mathematical ideas due to the absence of instructional materials that connect lessons with real-world contexts. Applying a contextual approach aligned with scientific inquiry may improve students’ conceptual understanding and engagement.Aims: This research seeks to develop and assess the effectiveness of contextual teaching modules designed using a scientific framework to strengthen the mathematical representation skills of junior secondary learners.Methods: Adopting the ADDIE instructional design model, this study utilized a Research and Development (R&D) methodology involving 87 seventh-grade students from two Indonesian schools. Data collection included expert validation instruments, learner feedback surveys, and pretest-posttest measurements, with analysis based on validity, practicality, and effect size metrics.Results: Expert evaluations confirmed high validity, with average ratings of 3.71 for content and 3.73 for media. Student feedback indicated high engagement across both trial groups (mean scores above 3.3). Effect size analysis showed substantial learning gains, with Cohen’s d values of 0.82 and 0.96, indicating strong impact on students’ mathematical representation ability.Conclusion: The contextual modules developed through this study, when implemented with a scientific approach, were validated as effective tools for improving students’ ability to represent mathematical concepts. These outcomes underscore the value of integrating contextual and inquiry-driven strategies into teaching practices to make abstract content more accessible and meaningful in mathematics education.