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Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
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Leif Sundberg; Jonny Holmström – Journal of Information Systems Education, 2024
With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical…
Descriptors: Artificial Intelligence, Conventional Instruction, Data Collection, Models
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Mohamed, Mohamed Hegazy; Abdelgaber, Sayed; Abd-Ellatif, Laila – Journal of Education and e-Learning Research, 2023
Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective…
Descriptors: Educational Practices, Program Evaluation, Opinions, Data Collection
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Perez-Vergara, Kelly – Strategic Enrollment Management Quarterly, 2020
Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…
Descriptors: Enrollment Projections, Enrollment Management, Enrollment Trends, Models
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Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
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Motz, Benjamin; Busey, Thomas; Rickert, Martin; Landy, David – International Educational Data Mining Society, 2018
Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might…
Descriptors: Data Collection, Data Analysis, Enrollment, Higher Education
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Leblay, Joffrey; Rabah, Mourad; Champagnat, Ronan; Nowakowski, Samuel – International Association for Development of the Information Society, 2018
How can we learn to use properly business software, digital environments, games or intelligent tutoring systems (ITS)? Mainly, we assume that the new user will learn by doing. But what about the efficiency of such a method? Our approach proposes an answer by introducing on-line coaching. In learning process, learners may need guidance to help them…
Descriptors: Intelligent Tutoring Systems, Coaching (Performance), Efficiency, Learning Processes
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Morsomme, Raphaël; Alferez, Sofia Vazquez – International Educational Data Mining Society, 2019
Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we…
Descriptors: Liberal Arts, Course Selection (Students), Courses, College Students
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Wu, Pengfei; Yu, Shengquan; Wang, Dan – Educational Technology & Society, 2018
The present study uses a text data mining approach to automatically discover learner interests in open learning environments. We propose a method to construct learner interests automatically from the combination of learner generated content and their dynamic interactions with other learning resources. We develop a learner-topic model to discover…
Descriptors: Data Collection, Data Analysis, Educational Technology, Technology Uses in Education
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Barros, Thiago M.; Souza Neto, Plácido A.; Silva, Ivanovitch; Guedes, Luiz Affonso – Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…
Descriptors: Predictor Variables, Models, Dropout Rate, Classification
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Durak, Gurhan; Cankaya, Serkan; Yunkul, Eyup; Misirli, Zeynel Abidin – Turkish Online Journal of Distance Education, 2018
The present study aimed at conducting content analysis on dissertations carried out so far in the field of Educational Technology in Turkey. A total of 137 dissertations were examined to determine the key words, academic discipline, research areas, theoretical frameworks, research designs and models, statistical analyses, data collection tools,…
Descriptors: Content Analysis, Doctoral Dissertations, Educational Technology, Foreign Countries
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Shum, Simon Buckingham; Sándor, Ágnes; Goldsmith, Rosalie; Bass, Randall; McWilliams, Mindy – Journal of Learning Analytics, 2017
When used effectively, reflective writing tasks can deepen learners' understanding of key concepts, help them critically appraise their developing professional identity, and build qualities for lifelong learning. As such, reflective writing is attracting substantial interest from universities concerned with experiential learning, reflective…
Descriptors: Reflection, Writing Assignments, Educational Research, Data Collection
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Kuhnel, Matthias; Seiler, Luisa; Honal, Andrea; Ifenthaler, Dirk – Interactive Technology and Smart Education, 2018
Purpose: The purpose of the study was to test the usability of the MyLA app prototype by its potential users. Furthermore, the Web app will be introduced in the framework of "Mobile Learning Analytics", a cooperation project between the Cooperative State University Mannheim and University of Mannheim. The participating universities focus…
Descriptors: Electronic Learning, Higher Education, Data Collection, Usability
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Hughes, John; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2016
The high rate of students taking developmental education courses suggests that many students graduate from high school unready to meet college expectations. A college readiness screener can help colleges and school districts better identify students who are not ready for college credit courses. The primary audience for this guide is leaders and…
Descriptors: College Readiness, Screening Tests, Test Construction, Predictor Variables
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Zeng, Ziheng; Chaturvedi, Snigdha; Bhat, Suma – International Educational Data Mining Society, 2017
Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
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