{"title":"基于学术数据的教育数据挖掘增强学生成绩预测","authors":"Z. Alamgir, Habiba Akram, S. Karim, Aamir Wali","doi":"10.15388/infedu.2024.04","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":45270,"journal":{"name":"Informatics in Education","volume":"26 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Student Performance Prediction via Educational Data Mining on Academic data\",\"authors\":\"Z. Alamgir, Habiba Akram, S. Karim, Aamir Wali\",\"doi\":\"10.15388/infedu.2024.04\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":45270,\"journal\":{\"name\":\"Informatics in Education\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informatics in Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15388/infedu.2024.04\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15388/infedu.2024.04","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
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.
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.