Treffer: Pengaruh Penggunaan Platform CodeEasy terhadap Tingkat Pemahaman Mahasiswa dalam Pembelajaran Data Science Python.

This study aims to test the hypothesis that the use of the self-paced learning platform CodeEasy can improve students’ understanding in the Data Science Python course. CodeEasy is an asynchronous learning environment that provides code-based exercises with automated assessment through test cases, along with instant feedback to promote active student engagement. The study employed a one-group pretest–posttest design involving 28 students from the Business Information Systems Study Program at Politeknik Negeri Malang during the even semester of 2024/2025. Paired Sample t-test analysis showed a significant increase in students’ understanding scores (pretest mean = 32.34%; posttest mean = 80.38%; t(27) = 9.078, p < 0.001), with a large effect size (Cohen’s d = 1.72), supporting the research hypothesis. These results are indicative, as the lack of a control group limits causal conclusions and generalization. This study provides preliminary evidence of the effectiveness of an autograding platform in supporting self-paced programming learning and suggests further applications using a controlled design. [ABSTRACT FROM AUTHOR]

Penelitian ini bertujuan menguji hipotesis bahwa penggunaan platform pembelajaran mandiri CodeEasy dapat meningkatkan pemahaman mahasiswa pada mata kuliah Data Science Python. CodeEasy merupakan lingkungan belajar asinkron yang menyediakan latihan berbasis kode dengan penilaian otomatis melalui test case, serta umpan balik instan untuk mendorong keterlibatan aktif mahasiswa. Penelitian menggunakan desain one-group pretest–posttest dengan 28 mahasiswa Program Studi Sistem Informasi Bisnis, Politeknik Negeri Malang, semester genap 2024/2025. Analisis Paired Sample t-test menunjukkan peningkatan signifikan skor pemahaman mahasiswa (rata-rata pretest = 32,34%; posttest = 80,38%; t (27) = 9,078, p < 0,001), dengan ukuran efek besar (Cohen's d = 1,72), yang mendukung hipotesis penelitian. Hasil ini bersifat indikatif, mengingat desain tanpa kelompok kontrol membatasi kesimpulan kausalitas dan generalisasi. Penelitian ini memberikan bukti awal mengenai efektivitas platform autograding dalam mendukung pembelajaran mandiri pemrograman, dan menyarankan penerapan lebih lanjut dengan desain kontrol serta integrasi Explainable AI (XAI) untuk meningkatkan transparansi evaluasi kode dan personalisasi umpan balik. [ABSTRACT FROM AUTHOR]

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