Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Smart Society: Community Service and Empowerment Journal AI and Developmental Insights in Education (AIDIE)
Khoirunnisa Imama
UIN Raden Intan Lampung
Humanities Education Languange, Linguistic, Communication & Media Social Sciences Other

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Enhancing Mathematical Representation Skills through the MID Model: Does Cognitive Style Matter? Santi Widyawati; Siti Qomariyah; Fredi Ganda Putra; Khoirunnisa Imama; Cahniyo Wijaya Kuswanto
Smart Society Vol. 5 No. 1 (2025): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v5i1.657

Abstract

The ability to represent mathematical ideas is a fundamental component of mathematical understanding and communication. However, many students still struggle to express mathematical concepts in varied forms such as visual, symbolic, or verbal representations. This study aims to examine the effectiveness of the Meaningful Instructional Design (MID) learning model in improving students’ mathematical representation skills, while also considering the role of students’ cognitive styles. A quasi-experimental research design was implemented involving two groups: an experimental group taught using the MID model and a control group taught using conventional expository methods. The participants were selected using a cluster random sampling technique from a population of junior high school students. Data were collected through a validated mathematical representation test and a cognitive style questionnaire. The collected data were then analyzed using two-way ANOVA with a 5 percent level of significance, following prerequisite tests for normality and homogeneity using SPSS version 25 and Microsoft Excel. The results showed that students who learned through the MID model exhibited significantly higher mathematical representation skills than those taught using the expository model. Furthermore, students’ cognitive styles also had a significant main effect on their representation abilities. Despite these main effects, the interaction between the learning model and cognitive style was not statistically significant. These findings indicate that the MID model is a robust instructional approach that can enhance students’ mathematical representation skills across different cognitive style profiles. The study highlights the importance of adopting meaningful learning frameworks that prioritize concept-building and student engagement, regardless of learners’ individual differences. 
Educational-Stage Profiles of Learners’ Trust in Artificial Intelligence Across Secondary and Higher Education Contexts Fredi Ganda Putra; Khoirunnisa Imama
AI and Developmental Insights in Education Vol. 2 No. 1 (2026): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v2i1.155

Abstract

Trust in artificial intelligence (AI) is increasingly relevant to educational technology adoption, yet evidence remains limited on how learners at different educational stages evaluate AI systems. This cross-sectional survey examined AI trust among 412 students from secondary schools and universities in Lampung Province, Indonesia. The study assessed AI trust, AI literacy, prior AI experience, perceived AI transparency, and perceived institutional AI integration policy. Descriptive analyses, independent-samples t-tests, hierarchical multiple regression, bootstrapped mediation, and moderated mediation were used to estimate educational-stage differences and conditional indirect associations. Higher education students reported higher composite AI trust than secondary school students (M = 70.63 vs. M = 57.04, p < .001, d = 1.21). AI literacy and perceived transparency were positively associated with AI trust after controlling for gender, educational stage, and prior AI experience. Perceived transparency partially accounted for the association between AI literacy and trust (indirect effect = 0.17, 95% CI [0.11, 0.24]), and institutional AI integration policy strengthened the AI literacy-transparency pathway. Because the design was cross-sectional and based on self-report data, the findings should be interpreted as associational rather than causal or developmental evidence. The study suggests that AI literacy curricula should explicitly develop learners’ ability to evaluate transparency, uncertainty, and appropriate reliance when using AI-supported educational tools.
Co-Authors Cahniyo Wijaya Kuswanto Fredi Ganda Putra Santi Widyawati Siti Qomariyah