Deep Learning Transforms Education Evaluation in Singapore, Indonesia Still Focused on Exam Results
Artificial intelligence is reshaping how countries evaluate learning, but progress remains uneven across Southeast Asia. A 2026 comparative study by Siti Zaenab, Achmad Imam Bashori, and Iksan Kamil Sahri from Institut Al-Fithrah Surabaya shows that Singapore has systematically integrated deep learning into modern education evaluation, while Indonesia continues to rely heavily on outcome-based assessments with limited technological integration. Published in the International Journal of Contemporary Sciences, the findings highlight why evaluation reform matters for improving learning quality in the digital era.
The researchers examined how deep learning—an advanced branch of artificial intelligence capable of analyzing large and complex datasets—is used in education evaluation systems in both countries. Their analysis matters because education systems worldwide are shifting away from one-time exams toward continuous, data-informed evaluation that supports student development. Countries that fail to adapt risk widening learning gaps and missing the benefits of digital transformation.
Why Education Evaluation Is Changing
Across the globe, education evaluation is no longer defined only by final test scores. Governments and schools increasingly recognize that how students learn is as important as what they score. International policy discussions, including those led by the OECD, emphasize the use of learning data to guide teaching strategies, personalize instruction, and support long-term competency development.
Deep learning plays a growing role in this shift. Unlike basic digital tools, deep learning systems can identify learning patterns, detect early signs of difficulty, and generate adaptive feedback. In practice, this means evaluation can function as a continuous support mechanism rather than a final judgment.
Indonesia and Singapore offer a revealing contrast. Both countries promote digital education policies, but they differ sharply in how far those policies translate into modern evaluation systems.
How the Research Was Conducted
The study used a qualitative comparative research design. Instead of surveys or experiments, the authors analyzed:
- National education policy documents from Indonesia and Singapore
- International reports, including OECD publications
- Peer-reviewed academic literature on artificial intelligence and education
The analysis focused on evaluation paradigms, the role of deep learning, system readiness, and policy environments between 2019 and 2024. By comparing these sources, the researchers identified recurring themes and structural differences in how each country applies technology to education evaluation.
Key Findings: Two Very Different Approaches
The comparison reveals a clear divide between Indonesia and Singapore.
1. Evaluation Paradigm
- Indonesia: Evaluation remains largely outcome-oriented, centered on exams and summative assessments. Learning data is mostly used for reporting and administration.
- Singapore: Evaluation is process-oriented, embedded in daily teaching and learning. Data is used to understand student progress and inform decisions at classroom and policy levels.
2. Use of Technology
- Indonesia: Digital tools mainly support online tests and score management. Deep learning is not systematically used to analyze learning processes.
- Singapore: Deep learning supports learning analytics, early detection of learning difficulties, and adaptive feedback tailored to individual students.
3. Integration of Deep Learning
- Indonesia: Integration is limited and partial, often dependent on isolated projects or individual initiatives.
- Singapore: Integration is systematic and sustainable, aligned with curriculum design and teacher development.
4. Policy Support and System Readiness
- Indonesia: Digital transformation policies exist, but implementation faces infrastructure gaps, uneven educator competence, and weak integration between evaluation and learning analytics.
- Singapore: Long-term, coherent education policies ensure strong alignment between technology, evaluation, and professional development.
What the Findings Mean for Education Quality
The study shows that technology alone does not modernize education evaluation. Policy coherence, system readiness, and human resource capacity are decisive factors. Countries that treat evaluation as a continuous learning process are better positioned to benefit from deep learning and other AI-based tools.
In Singapore, deep learning functions as an analytical instrument that supports pedagogical decisions. Teachers and institutions can respond more precisely to student needs, making learning more personalized and sustainable.
In Indonesia, the dominance of outcome-based evaluation limits the potential of learning data. When evaluation is seen mainly as a final measurement, deep learning remains an administrative add-on rather than a driver of instructional improvement.
Siti Zaenab and her colleagues emphasize that meaningful adoption of artificial intelligence requires a shift in evaluation mindset. As they argue in their analysis, deep learning is most effective when supported by formative evaluation practices and integrated education policies at the system level.
Implications for Policymakers and Educators
The findings offer several practical lessons for Indonesia and other developing education systems:
- Gradual integration of learning analytics: Schools can start by using existing digital learning data to support formative evaluation before adopting more advanced AI systems.
- Policy reform beyond digitalization: Education policies should encourage the use of evaluation data to improve learning processes, not just to digitize exams.
- Teacher capacity building: Educators need training to interpret and use learning analytics effectively, ensuring technology enhances pedagogy rather than replacing it.
More broadly, the study reinforces that evaluation reform is central to education quality. Systems that remain exam-centered may struggle to keep pace with the demands of 21st-century learning.
Author Profiles
- Siti Zaenab, M.Pd., Lecturer and researcher at Institut Al-Fithrah Surabaya. Her expertise includes education evaluation, education policy, and digital transformation in learning systems.
- Achmad Imam Bashori, M.Pd., Academic at Institut Al-Fithrah Surabaya, specializing in modern education and learning innovation.
- Iksan Kamil Sahri, M.Pd., Researcher at Institut Al-Fithrah Surabaya with a focus on education policy analysis and technology-driven evaluation.
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