Study on student performance estimation, student progress analysis, and student potential prediction based on data mining

Abstract

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On Data Protection Regulations, Big Data and Sledgehammers in Higher Education

“…Higher Education institutions have not been alien to the discussions, as some scholars argued that gathering as much information as possible about university students, professors and administration staff could enable deep analysis and, thereby, proactive actions in student attention, course planning and resource management [4][5][6]. In this line, there have been numerous studies in the recent past about predicting student outcomes using Artificial Intelligence (AI) techniques [7][8][9][10][11][12][13][14][15][16][17] and it is generally assumed that the more abundant the data, the more accurate the predictions.…”

“…Our experiment consisted in performing a scan of hyperparameters for a Multi-Layer Perceptron (MLP) neural network, in search for the configuration that attained the greater accuracy in predicting academic outcomes from the socio-economic data. We chose the MLP for being one of the best understood machine learning models, commonly used in the related literature [18,19]; its best configuration would be used as a benchmark for the comparison of other techniques, including the ones used in References [7][8][9][10][11][12][13][14][15][16][17] and more advanced neural network schemes. However, the scan of hyperparameters revealed no correlations or dependencies between the input variables and the chosen metrics in any case, showing that-at least for the UPS and alike settings-there is no actual gain from applying machine learning techniques on extensive socio-economic data.…”

“…In relation to these precedents, plus those of References [14][15][16][17], our work involves the most extensive and fine-grained dataset of student records (30 times as many as the average from above). Besides, whereas many others have focused on comparing different algorithms and techniques with single configurations or, at the most, a few tens of combinations of hyperparameters, we are the first to conduct an extensive scan (more than 12,000 configurations) to make the most of one technique that ranked well in the literature, namely the Multi-Layer Perceptron.…”

Penerapan Metode K-Nearest Neighbor Dan Information Gain Pada Klasifikasi Kinerja Siswa

“…Dengan menganalisis kinerja siswa, program strategis dapat direncanakan dengan baik selama masa studi mereka di sebuah institusi (Ibrahim & Rusli, 2007). Namun, pekerjaan yang ada tidak menyediakan alat analisis yang cukup untuk menganalisis bagaimana siswa melakukan, faktor mana yang akan mempengaruhi kinerjanya, dengan cara mana siswa dapat membuat kemajuan, dan apakah siswa memiliki potensi untuk melakukan yang lebih baik (Yang & Li, 2018). Informasi kemajuan pembelajaran siswa menjadi salah satu faktor penilaian seorang pendidik dalam menganalisis kinerja siswa.…”

“…Informasi kemajuan pembelajaran siswa menjadi salah satu faktor penilaian seorang pendidik dalam menganalisis kinerja siswa. Namun cara tersebut tidaklah efektif karena informasi kemajuan pembelajaran siswa tidak cukup sebagai indikator para siswa dan pendidik untuk melakukan perbaikan dalam pengajaran dan pembelajaran (Yang & Li, 2018).…”