基于学术数据的教育数据挖掘增强学生成绩预测

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
Z. Alamgir, Habiba Akram, S. Karim, Aamir Wali
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引用次数: 0

摘要

教育数据挖掘被广泛应用于从学术数据中提取有价值的信息和模式。这项研究探索了有助于预测本科生未来表现的新特征,并在早期识别出有风险的学生。它回答了一些以前的研究没有解决的关键和直观的问题。现有的研究大多是基于2-3年的数据,采用绝对评分方案。我们考察了15年的历史学术数据对预测建模的影响。此外,我们探讨了本科生在相对评分方案中的表现,并检查了核心课程和第一学期的成绩对未来表现的影响。作为一项试点研究,我们分析了计算机专业学生的学习成绩。有了许多令人兴奋的发现;历史数据的持续时间和大小在预测未来表现方面发挥着重要作用,主要是由于课程、教师、社会和发展趋势的变化。此外,在一个相对的评分方案中,根据最初的必修课程来预测高级课程的成绩是具有挑战性的,因为学生的表现不仅取决于他们自己的努力,还取决于他们的同龄人。简而言之,教育数据挖掘可以通过从学术数据中发现有价值的见解来预测未来的表现,并确定需要显著改进的关键领域,从而拯救学生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Student Performance Prediction via Educational Data Mining on Academic data
Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.
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来源期刊
Informatics in Education
Informatics in Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.10
自引率
3.70%
发文量
20
审稿时长
20 weeks
期刊介绍: INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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