Analisis Pembelajaran dalam Pendidikan di Indonesia: Tinjauan Sistematis tentang Model Prediktif dan Strategi Intervensi
DOI:
https://doi.org/10.52436/1.jpti.2251Kata Kunci:
intervensi pendidikan, learning analytics, prediksiAbstrak
Perkembangan learning analytics telah mendorong pemanfaatan data pembelajaran untuk mendukung pengambilan keputusan berbasis data dalam pendidikan. Meskipun berbagai penelitian mengenai model prediktif telah berkembang pesat, kajian yang secara khusus mensintesis implementasi learning analytics pada konteks pendidikan di Indonesia masih terbatas. Penelitian ini bertujuan menganalisis perkembangan model prediktif, faktor-faktor yang memengaruhi keberhasilan akademik, serta strategi intervensi pendidikan berbasis learning analytics melalui pendekatan Systematic Literature Review (SLR) dengan pedoman PRISMA. Penelusuran literatur dilakukan pada basis data Scopus dan Google Scholar untuk publikasi tahun 2022–2026 sehingga diperoleh 20 artikel yang memenuhi kriteria inklusi. Hasil sintesis menunjukkan bahwa algoritma berbasis ensemble learning seperti Random Forest, XGBoost, dan Stacking Ensemble memberikan performa prediksi yang relatif lebih konsisten, sedangkan faktor akademik, aktivitas pada Learning Management System (LMS), motivasi belajar, serta kondisi sosial ekonomi menjadi prediktor utama keberhasilan akademik. Selain itu, sebagian besar penelitian mengembangkan Early Warning System untuk mendukung intervensi akademik secara lebih dini dan tepat sasaran. Kontribusi penelitian ini adalah menyajikan sintesis komprehensif mengenai perkembangan implementasi learning analytics di Indonesia, mengidentifikasi tantangan implementasi, serta memberikan rekomendasi bagi pengembangan penelitian dan sistem pendukung keputusan pendidikan berbasis data.
Unduhan
Referensi
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