A Machine Learning Model to Predict Standardized Tests in Engineering Programs in Colombia

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-08-02 DOI:10.1109/RITA.2023.3301396

Misorly Soto-Acevedo;Alfredo Miguel Abuchar-Curi;Rohemi Alfredo Zuluaga-Ortiz;Enrique J. Delahoz-Domínguez

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Abstract

Forecasting of Standardized Test Results for engineering students through Machine Learning This research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytics approach based on three stages was developed: first, analysis and debugging of the database; second, multivariate analysis; and third, machine learning techniques. The results show an association between the performance levels in the Highschool test and the university test results. In addition, the machine learning algorithm that adequately fits the research problem is the Generalized Linear Network Model. For the training stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.810, 0.820, 0.813, and 0.827, respectively; in the evaluation stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.820, 0.820, 0.827 and 0.813 respectively.

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预测哥伦比亚工程项目标准化测试的机器学习模型

本研究开发了一个模型来预测哥伦比亚国家工程专业标准化考试的结果。这项研究使预测每个学生的成绩成为可能，从而制定强化策略来提高学生的表现。为此，本文提出了一种基于三个阶段的学习分析方法:首先，对数据库进行分析和调试;二是多变量分析;第三，机器学习技术。结果表明，高中考试成绩水平与大学考试成绩之间存在关联。此外，适合研究问题的机器学习算法是广义线性网络模型。在训练阶段，模型的准确率(Accuracy)、AUC (AUC)、灵敏度(Sensitivity)和特异性(Specificity)分别为0.810、0.820、0.813和0.827;在评价阶段，该模型的准确率(Accuracy)、AUC (AUC)、灵敏度(Sensitivity)和特异性(Specificity)分别为0.820、0.820、0.827和0.813。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.