A Predictive Model using Machine Learning Algorithm in Identifying Students Probability on Passing Semestral Course

要約

【タイトル】機械学習アルゴリズムを用いた学生の学期科目合格確率の予測モデル

【要約】
– この研究は、学期の初期段階で履修する科目の合格確率を予測するための予測モデルの開発を目的としている。
– CRISP-DM（Cross-Industry Standard Process for Data Mining）の方法論を使用し、高い精度と正確性を持つ、意思決定に役立つ予測モデルを開発した。
– データマイニング技術の分類、アルゴリズムの決定木を使用して研究を行い、開発したモデルによる留年確率の予測の精度は0.7619、精密度は0.8333、再現性は0.8823、f1スコアは0.8571であった。
– この予測モデルの精度や実用性により、教育関係者は生徒の弱点を特定し、効果的な学習プロセスを改善し、教育成果を改善することができる。
– 今後は、学生の人口統計情報の追加、より多くのデータの研究、場合によっては手動処理を加えた予測方法などが必要とされる。

要約(オリジナル)

This study aims to determine a predictive model to learn students probability to pass their courses taken at the earliest stage of the semester. To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveying knowledge and uplifting students academic performance, the proponent applies and strictly followed the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This study employs classification for data mining techniques, and decision tree for algorithm. With the utilization of the newly discovered predictive model, the prediction of students probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score, which shows that the model used in the prediction is reliable, accurate, and recommendable. Considering the indicators and the results, it can be noted that the prediction model used in this study is highly acceptable. The data mining techniques provides effective and efficient innovative tools in analyzing and predicting student performances. The model used in this study will greatly affect the way educators understand and identify the weakness of their students in the class, the way they improved the effectiveness of their learning processes gearing to their students, bring down academic failure rates, and help institution administrators modify their learning system outcomes. Further study for the inclusion of some students demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI