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All Journal Didaktika: Jurnal Kependidikan Journal of Educational Technology and Learning Creativity Recursive Journal of Informatics
Ayu Permata Sari
Universitas Pendidikan Indonesia
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Other

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Validity and Reliability of Computational Thinking Scales for Undergraduate Students: Indonesian Adaptation Rizki Hikmawan; Mumu Komaro; Ayi Suherman; Ayu Permata Sari
Didaktika: Jurnal Kependidikan Vol. 14 No. 3 Agustus (2025): Didaktika Jurnal Kependidikan
Publisher : South Sulawesi Education Development (SSED)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58230/27454312.2910

Abstract

Computational Thinking (CT) is the foundational discipline of informatics. Meanwhile, the Computational Thinking Scale (CTS) is a widely recognized instrument for measuring CT. However, despite its widespread recognition, only a limited number of studies have examined the reliability and construct validity of CTS among Indonesian students. Furthermore, linguistic equivalence across all CTS items is essential to avoid misinterpretation. Therefore, this study aims to adapt the CTS instrument into Bahasa Indonesia and conduct both reliability and validity testing. A total of 551 undergraduate students participated in this research. The statistical methods employed include Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The results revealed that 9 out of 29 CTS items failed to meet the required criteria. The final model showed strong fit indices (CFI = 0.95, TLI = 0.96, RMSEA = 0.047, SRMR = 0.049), along with acceptable validity and reliability across all dimensions. These findings affirm that the Indonesian version of CTS is a robust and valid tool for assessing students' CT Skills.
Integrating Artificial Intelligence and Science Education to Support Computational Thinking: A Multi-Model Benchmarking Study in Primary Numeracy Suprih Widodo; Muhamad Akda Fathul Barri; Ayu Permata Sari; Hapizah Hapizah; Intan Sari Rufiana; Sumarni Sumarni; Zuriani Mustaffa
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.3397

Abstract

Purpose of the study: This study investigates the multidisciplinary integration of artificial intelligence, learning analytics, cognitive psychology, and science education to evaluate three Large Language Model (LLM) configurations. It aims to optimize an adaptive digital scaffolding framework for primary computational thinking (CT) and scientific reasoning under tight latency constraints. Methodology: Deployed via Python 3.11 within an automated benchmarking ecosystem, OpenAI gpt-5-mini, Google gemini-3.5-flash, and Meta llama-3.3-70b-versatile (Groq LPU) were evaluated across 15 Bebras tasks (135 structured API interactions). The multidisciplinary validation applied content and psycholinguistic triage to analyze the interface between technical inference latency and the continuity of students' scientific inquiry processes. Main Findings: Meta Llama-3.3-70b achieved optimal performance with a 0.2687s latency, maximizing the Student Waiting Threshold (SWT) compliance margin to support uninterrupted scientific schema construction. OpenAI GPT-5 Mini exhibited superior Socratic instruction adherence (6.9% failure) but introduced a 2.3764s latency overhead. Gemini 3.5 Flash truncated crucial pedagogical contexts due to its constrained 3-token output distribution. Novelty/Originality of this study: This work introduces a multidisciplinary engineering blueprint that bridges hardware-level computing optimization with technology-enhanced science education. By formalizing a latency-constrained routing protocol, it establishes a theoretical model demonstrating how infrastructure responsiveness directly safeguards the cognitive sustainability of scientific reasoning and problem-solving sequences in primary STEM learning contexts.
Human-Centered AI in Practice: Peer Facilitators as Technostress Reducers in a Mixed-Methods LMS Study Ilham Agung Pambudi; Nuur Wachid Abdul Majid; Muhamad Akda Fathul Barri; Ayu Permata Sari
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.64092

Abstract

Purpose: While the rapid integration of generative artificial intelligence (AI) offers significant pedagogical potential, it often outpaces the psychological scaffolding that anxious learners require, creating a severe affective barrier known as technostress. To address this critical gap, this study investigates whether structured peer facilitation, embedded within a Learning Management System (LMS) based AI course, can effectively reduce undergraduate students' AI anxiety and strengthen their conceptual understanding. Methods/Study design/approach: An explanatory sequential mixed-methods design was conducted involving 33 undergraduate students encountering generative AI instruction for the first time. Quantitative data encompassing AI anxiety, conceptual understanding, and technical self-efficacy were collected pre- and post-intervention, followed by analysis using Wilcoxon signed-rank tests and Spearman's rank correlation. Subsequently, qualitative data from open-ended evaluations were examined through reflexive thematic analysis to contextualize and deeply explain the quantitative shifts. Result/Findings: The intervention yielded a statistically significant reduction in AI anxiety (Z = -2.151, p = .032) alongside a significant improvement in conceptual understanding (Z = -1.990, p = .047). Although technical self-efficacy and career projection improved, the changes did not reach statistical significance. Furthermore, facilitator evaluation exhibited a significant negative correlation with anxiety reduction (rs = -.389). Thematic analysis confirmed that peer facilitators effectively acted as technical translators and technostress reducers, establishing the psychological safety necessary for learning. Novelty/Originality/Value: This research uniquely repositions peer mediation as the vital affective "more capable other" within Vygotsky's Zone of Proximal Development, prioritizing the resolution of emotional barriers before cognitive engagement with AI tools. The study extends Human-Centered AI (HCAI) theory by delivering robust empirical evidence that pairing generative AI modules with human peer facilitation creates a synergistic learning ecosystem. This provides educators with a scalable, practical solution for mitigating technostress in modern digital education.
Co-Authors Ayi Suherman Hapizah Hikmawan, Rizki Ilham Agung Pambudi Intan Sari Rufiana Muhamad Akda Fathul Barri Mumu Komaro Nuur Wachid Abdul Majid Sumarni Zuriani Mustaffa