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Singgih Subiyantoro
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Cognitive Development Journal
Published by Edutech Publishing Media
ISSN : -     EISSN : 30258693     DOI : https://doi.org/10.32585/cognitive
Core Subject : Education,
Other
This journal presents scientific articles that discuss various cognitive aspects, including thinking processes, memory, language, problem-solving, perception, and intelligence. The journal serves as a platform for researchers to share the latest findings, theories, research methods, and empirical discoveries in the field of cognitive development.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 22 Documents
 1   2   3 
Preparing Indonesian Primary School Teachers for Deep Learning: Readiness, Challenges, and Institutional Support Subiyantoro, Singgih; Musa, Mohamad Zain; Efendi, Agus
Cognitive Development Journal Vol. 2 No. 2 (2024): Cognitive Development Journal
Publisher : Edutech Publishing Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/cognitive.v2i2.44

Abstract

The growing influence of deep learning technologies in everyday life has created new demands for educational systems worldwide, prompting efforts to introduce basic concepts of artificial intelligence at earlier stages of learning. In Indonesia, while digital literacy initiatives are gaining momentum, the preparedness of primary school teachers to teach deep learning concepts remains largely unexplored. This study aims to investigate the readiness of Indonesian primary school teachers to teach fundamental deep learning principles, identify the challenges they face, and assess the level of institutional support available to them. A mixed methods design was employed, combining quantitative survey data from 215 teachers with qualitative insights gathered through focus group interviews. The survey measured readiness across technical, pedagogical, and institutional dimensions, while thematic analysis was used to explore deeper experiences and perspectives. The findings reveal that while teachers express strong enthusiasm and recognize the importance of introducing deep learning, their technical understanding and pedagogical confidence remain moderate. Access to training opportunities and digital infrastructure emerged as critical enablers, whereas a lack of curricular guidance and institutional support was cited as major barriers. Teacher motivation is not lacking; rather, systemic support must be strengthened to turn readiness into effective practice. Preparing teachers for deep learning education requires comprehensive strategies that integrate professional development, curriculum design, and infrastructure improvement. This study enriches the limited literature on AI education at the primary level in developing countries and offers practical recommendations for policymakers and educational stakeholders seeking to build future-ready classrooms.
Pre-Service Teachers and Computational Thinking: Designing Meaningful Learning in Higher Education Krisdianto Hadiprasetyo; Exacta, Annisa Prima; Muhammad Zain Musa; Salvador V. Briones II
Cognitive Development Journal Vol. 2 No. 2 (2024): Cognitive Development Journal
Publisher : Edutech Publishing Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/cognitive.v2i2.48

Abstract

This study aims to understand the students’ computational thinking skills in statistics. The type of research is descriptive with a qualitative approach. The data collection techniques in this study include 1) Tests; 2) Interviews; 3) Documentation; and 4) Validation Sheets. The data analysis in this study involves: 1) Data condensation; 2) Data presentation; 3) Verification; and 4) Conclusion drawing. The validity of the data in this study is ensured using the technique of triangulation. Subjects were selected using purposive sampling. The instruments used were two statistical problem-solving questions. The results showed that in solving the first and second questions, the respondents could address the problems using the components of Computational Thinking, starting with decomposition, abstraction, and algorithm tasks. However, the pattern recognition component was not evident in the problem-solving process, even though some respondents gave incorrect answers. This was because the respondents did not fully understand the questions. They only read the questions once or twice, so the information was not fully comprehended. Additionally, the respondents only considered the simplest path and overlooked more complex paths in solving the second question. Students can carry out abstraction and algorithmic tasks, but they still struggle with decomposition and pattern recognition. Keywords: student, mathematics, statistics, computational thinking, ability

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