Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria PDF Download
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Author: Youssef Amr Naga
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 0
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Book Description
Abstract: This thesis aims to propose and evaluate possible predictors of success for incoming university students to the American University in Cairo (AUC) who wish to enroll in its engineering programs, by considering their overallgrade point average (GPA) at graduation as the measure of their success (output variable). This study is composed of two phases. First, available university admission variables; i.e., gender, high school diploma, high school score, and proficiency level in the English language (the language of instruction at AUC) at the time of application are evaluated as a predictor of students' performance using five different data mining techniques. The analysis suggests that the current input admission variables can only predict student performance with limited accuracy. Moreover, of all the university admission data available, the type of high school diploma exhibits the greatest statistical significance as a predictor of student success in AUC engineering programs. The second phase of research was to conduct an analysis on thesix high school Diplomas that are typically offered in Egypt, and which regularly feed into AUC. This phase was conducted on 60 current high school students and aimed to identify component-wise cognitive traits and habits of mind that could correlate diploma type to predicted success in studying engineering in general. The research findings suggest that student scores on aptitude tests which directly measure engineering knowledge in high school are the best predictor of success for studying engineering at the university level, rather than the more widely recognized general cognitive ability scores (e.g., logical, and verbal abilities). Nevertheless, the findings also identified that when student preparedness isuniformly above-average across all these general cognitive abilities, that situation too is a good indicator of their success in studying engineering at the university level.
Author: Youssef Amr Naga
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 0
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Book Description
Abstract: This thesis aims to propose and evaluate possible predictors of success for incoming university students to the American University in Cairo (AUC) who wish to enroll in its engineering programs, by considering their overallgrade point average (GPA) at graduation as the measure of their success (output variable). This study is composed of two phases. First, available university admission variables; i.e., gender, high school diploma, high school score, and proficiency level in the English language (the language of instruction at AUC) at the time of application are evaluated as a predictor of students' performance using five different data mining techniques. The analysis suggests that the current input admission variables can only predict student performance with limited accuracy. Moreover, of all the university admission data available, the type of high school diploma exhibits the greatest statistical significance as a predictor of student success in AUC engineering programs. The second phase of research was to conduct an analysis on thesix high school Diplomas that are typically offered in Egypt, and which regularly feed into AUC. This phase was conducted on 60 current high school students and aimed to identify component-wise cognitive traits and habits of mind that could correlate diploma type to predicted success in studying engineering in general. The research findings suggest that student scores on aptitude tests which directly measure engineering knowledge in high school are the best predictor of success for studying engineering at the university level, rather than the more widely recognized general cognitive ability scores (e.g., logical, and verbal abilities). Nevertheless, the findings also identified that when student preparedness isuniformly above-average across all these general cognitive abilities, that situation too is a good indicator of their success in studying engineering at the university level.
Author: Junshuai Feng
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their datasets and computational results. Then, it demonstrates how the synthetic student dataset is generated, analyzes some input attributes from the dataset such as gender and high school GPA, and delivers with some visualization results to determine which classification methods approaches are the most efficient. After testing the data with decision tree classifiers and neural networks methodologies, it concludes the effectiveness of both approaches in terms of the model evaluation performance as well as discussing some of the most promising future work of this research.
Author: Samira ElAtia
Publisher: John Wiley & Sons
ISBN: 1118998219
Category : Computers
Languages : en
Pages : 351
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Book Description
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Author: Sarah Alturki
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Phan Cong Vinh
Publisher: Springer
ISBN: 9783030929411
Category : Computers
Languages : en
Pages : 225
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Book Description
This book constitutes the refereed post-conference proceedings of the 7th International Conference on Nature of Computation and Communication, ICTCC 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 revised full papers presented were carefully selected from 43 submissions. The papers of ICTCC 2021 cover formal methods for self-adaptive systems and discuss natural approaches and techniques for natural computing systems and their applications.
Author: Lewis Whitley
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
The use of technology and data analysis within the classroom has been a resourceful tool in order to collect, study, and compare a student's level of success. With the large amount of regularly collected data from student behaviors, and course structure there is more than enough resources in order to find student success with data analysis. A method of data analysis within a learning environment is called Educational Data Mining (EDM), which has proven to be an emerging trend when it involves the development of exploration techniques and the analysis of educational data. EDM has been able to contribute to the understanding of student behavior, as well as factors that influence both student actions and their success. The study of student success within EDM has focused on student learning patterns, student to teacher culture, and teaching techniques. In this research we will look at uses of technology and data mining in an EDM setting and compare the success of findings. Using past experience of other research we will determine which method would be best in order to look at a learning environment, and try to find which factors will affect a student's academic performance.
Author: Deepak Gupta
Publisher: Springer Nature
ISBN: 9811551480
Category : Technology & Engineering
Languages : en
Pages : 1182
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Book Description
This book includes high-quality research papers presented at the Third International Conference on Innovative Computing and Communication (ICICC 2020), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 21–23 February, 2020. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Author: Nguyen Hoang Phuong
Publisher: Springer Nature
ISBN: 3031639294
Category :
Languages : en
Pages : 254
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Book Description
Author: Dirk Ifenthaler
Publisher: Springer Nature
ISBN: 3030473929
Category : Education
Languages : en
Pages : 464
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Book Description
The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.
Author: Valerie Jean Shute
Publisher: MIT Press
ISBN: 0262518813
Category : Education
Languages : en
Pages : 102
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Book Description
An approach to performance-based assessments that embeds assessments in digital games in order to measure how students are progressing toward targeted goals. To succeed in today's interconnected and complex world, workers need to be able to think systemically, creatively, and critically. Equipping K-16 students with these twenty-first-century competencies requires new thinking not only about what should be taught in school but also about how to develop valid assessments to measure and support these competencies. In Stealth Assessment, Valerie Shute and Matthew Ventura investigate an approach that embeds performance-based assessments in digital games. They argue that using well-designed games as vehicles to assess and support learning will help combat students' growing disengagement from school, provide dynamic and ongoing measures of learning processes and outcomes, and offer students opportunities to apply such complex competencies as creativity, problem solving, persistence, and collaboration. Embedding assessments within games provides a way to monitor players' progress toward targeted competencies and to use that information to support learning. Shute and Ventura discuss problems with such traditional assessment methods as multiple-choice questions, review evidence relating to digital games and learning, and illustrate the stealth-assessment approach with a set of assessments they are developing and embedding in the digital game Newton's Playground. These stealth assessments are intended to measure levels of creativity, persistence, and conceptual understanding of Newtonian physics during game play. Finally, they consider future research directions related to stealth assessment in education.
