Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Berkala Ilmiah Pendidikan Fisika Chemistry Education Practice
Suci Rizkina Tari
Universitas Syiah Kuala
Education Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Tracking Problem-Solving Behavior in One-Dimensional Kinematics: A Study of Physics Education Undergraduates Suci Rizkina Tari; Fitria Herliana; Fadiya Haya
Berkala Ilmiah Pendidikan Fisika Vol 14, No 2 (2026): JUNE 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/bipf.v14i2.26058

Abstract

A qualitative study was employed to explore undergraduate Physics Education students’ behavior during problem-solving of one-dimensional kinematics. Six undergraduate students from Physics Education program were chosen as the participants. The main data for this research were gathered through the think-aloud technique, which involved recording participants’ verbalizations, transcribing, and coding the data. This analysis was further supported by observations, interviews, and students’ written responses. The study identified twenty-four distinct behaviors commonly exhibited by students when solving problems related to one-dimensional kinematics. These behaviors included initial reading, repeated reading, strategic reading, organizing information, clarifying the problem context, drawing a visual representation, recalling relevant knowledge, identifying the goal, selecting the appropriate formula, identifying the variable, developing the plan, formulating a sub-plan, linking the concept, considering the formula, considering the variable, checking the plan, checking the computation, estimating the answer, recognizing error, evaluating the plan, reflecting on themselves, performing basic calculation, performing algebra, and presenting or writing down the solution. These behaviors were categorized into eight broader groups, which allowed for a clearer interpretation of students' behavior during problem-solving. The eight categories of behavior revealed in this study align with Polya’s four-step problem-solving model and Schoenfeld’s theory of metacognition. The results offer meaningful insights into the ways students interpret, plan, and evaluate solutions to physics problems. The findings also provide important implications for physics education by offering insights that lecturers can use to design learning instruction that explicitly supports the development of students’ problem-solving skills. Future research is suggested to design targeted interventions that support the development of expert-like problem-solving in undergraduate physics education based on the results of this study.
Validation of Pre-Service Chemistry Teachers’ Acceptance and Use Of Generative Artificial Intelligence Scale: Confirmatory Factor Analysis Wahyuni Adam; Suci Rizkina Tari; Hilman Qudratuddarsi; Meili Yanti; Eli Meivawati
Chemistry Education Practice Vol. 9 No. 1 (2026): Edisi Mei
Publisher : FKIP University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/cep.v9i1.11992

Abstract

The rapid development of generative artificial intelligence (GenAI) is transforming educational practices by enabling new forms of knowledge construction, problem-solving, and instructional support. Its growing integration into academic contexts has raised important questions about how future teachers perceive and adopt these technologies. This study aims to examine pre-service chemistry teachers’ attitudes and use of GenAI by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theory of Planned Behavior (TPB) within a validated framework. A quantitative cross-sectional survey was conducted with 240 Generation Z pre-service teachers using a structured questionnaire covering constructs such as performance expectancy, effort expectancy, social norms, facilitating conditions, habit, attitude, perceived behavioral control, behavioral intention, and AI use. Confirmatory Factor Analysis (CFA) was applied to test both first-order and second-order models. The findings indicate that the proposed model achieved acceptable goodness-of-fit and strong reliability and validity across constructs. Performance expectancy, effort expectancy, and social norms significantly influenced attitudes and behavioral intention, while facilitating conditions and perceived behavioral control supported AI use. Behavioral intention emerged as the strongest predictor of use. Overall, the study highlights that AI adoption is shaped by interconnected psychological, social, and contextual factors, emphasizing the need for holistic teacher education strategies.
Co-Authors Eli Meivawati Fadiya Haya Fitria Herliana Hilman Qudratuddarsi Wahyuni Adam