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Classification and prediction based data mining algorithms to predict students' introductory programming performance

机译：基于分类和预测的数据挖掘算法可预测学生的入门编程表现

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摘要

Data mining has been successfully implemented in the business world but, its use in higher education is still comparatively new. Predicting students' performance becomes more challenging due to the huge volume of data in educational databases. This paper focus on predicting introductory programming performance of first year bachelor students in Computer Application course by a predictive data mining model using classification based algorithms. The collected data contains the students' demographics, grade in introductory programming at college, and grade in introductory programming at test which contains 60 questions. Collected data was applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also drawn for all five classifiers and also this paper showcases the importance of Prediction and Classification based data mining algorithms in the field of programming education and also presents some promising future lines. It could bring the benefits and impacts to students, educators and the academic institutions.
机译：数据挖掘已在商业世界中成功实施，但在高等教育中的使用仍相对较新。由于教育数据库中的海量数据，预测学生的表现变得更具挑战性。本文着重于通过基于分类算法的预测数据挖掘模型来预测计算机应用课程中一年级本科生的入门编程表现。收集的数据包含学生的人口统计资料，大学入门课程的年级以及测试中入门课程的年级，其中包含60个问题。使用WEKA将收集的数据应用于各种分类算法，例如多层感知，朴素贝叶斯，SMO，J48和REPTree。结果，将基于所有分类算法生成统计信息，并对所有五个分类器进行比较，以预测准确性并在所有分类器中找到性能最佳的分类算法。在本文中，还为所有五个分类器绘制了知识流模型，并且本文还展示了基于预测和分类的数据挖掘算法在程序设计教育领域中的重要性，并提出了一些有希望的未来路线。它可以给学生，教育者和学术机构带来好处和影响。

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