Academic achievement prediction in secondary education by decision tree analysis

I. Villarrasa-Sapiña, X. García-Massó, Encarnación Liébana, Gonzalo Monfort Torres
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Abstract

The aim of the present study was to develop a predictive model of academic achievement (school success or failure) by applying a decision tree analysis. A cross-sectional study was carried out to design a system for the early detection of academic failure. 219 adolescents (aged 14 to 16) participated and information on their socioeconomic status, body mass index (BMI) percentile, physical activity, leisure time spent in front of screens, enjoyment, hope, anger, anxiety, boredom, behavioral engagement, emotional engagement, cognitive engagement, self-perceived school performance and intention to go to university was collected as input variables in decision tress analysis. 6 failure and 3 success groups were found able to predict academic performance. Good accuracy was obtained in the training (80.11 %) and validation (81.40 %) datasets of the decision tree. It is possible to predict academic failure or success by assessing weight status, physical activity, anger and hope during school attendance, intention to go to university and self-perceived school performance.
通过决策树分析预测中学教育的学业成绩
本研究旨在通过应用决策树分析法,建立一个学业成绩(学业成功或失败)预测模型。我们开展了一项横断面研究,以设计一个早期发现学业失败的系统。研究收集了 219 名青少年(14 至 16 岁)的社会经济状况、身体质量指数(BMI)百分位数、体育活动、在屏幕前花费的休闲时间、乐趣、希望、愤怒、焦虑、无聊、行为参与、情感参与、认知参与、自我感觉的学业成绩和上大学的意愿等信息,作为决策树分析的输入变量。结果发现,6 个失败组和 3 个成功组能够预测学习成绩。决策树的训练数据集(80.11%)和验证数据集(81.40%)都获得了良好的准确性。通过评估体重状况、体育活动、就学期间的愤怒和希望、上大学的意向以及自我感觉的学习成绩,可以预测学习成绩的失败或成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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