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Showing 1 to 15 of 18 results Save | Export
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Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
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Gkontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
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Xu, Tonghui – Journal of Educators Online, 2023
The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…
Descriptors: College Students, Online Courses, Academic Achievement, Data Analysis
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Costa, Stella F.; Diniz, Michael M. – Education and Information Technologies, 2022
The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…
Descriptors: Prediction, Undergraduate Students, Mathematics Education, Models
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Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
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Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao – IEEE Transactions on Learning Technologies, 2014
During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…
Descriptors: Cognitive Style, Pattern Recognition, Intelligent Tutoring Systems, Prediction
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Rutjens, Bastiaan T.; van Harreveld, Frenk; van der Pligt, Joop; Kreemers, Loes M.; Noordewier, Marret K. – Journal of Experimental Psychology: General, 2013
Stage theories are prominent and controversial in science. One possible reason for their appeal is that they provide order and predictability. Participants in Experiment 1 rated stage theories as more orderly and predictable (but less credible) than continuum theories. In Experiments 2-5, we showed that order threats increase the appeal of stage…
Descriptors: Developmental Stages, Theories, Role, Prediction
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Cornell, Sonia A.; Lahiri, Aditi; Eulitz, Carsten – Journal of Experimental Psychology: Human Perception and Performance, 2013
The precise structure of speech sound representations is still a matter of debate. In the present neurobiological study, we compared predictions about differential sensitivity to speech contrasts between models that assume full specification of all phonological information in the mental lexicon with those assuming sparse representations (only…
Descriptors: Neurosciences, Models, Speech Communication, Articulation (Speech)
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Bianchi, Ivana; Savardi, Ugo – Journal of Experimental Psychology: Human Perception and Performance, 2012
Research on naive physics and naive optics have shown that people hold surprising beliefs about everyday phenomena that are in contrast with what they see. In this article, we investigated what adults expect to be the field of view of a mirror from various viewpoints. The studies presented here confirm that humans have difficulty dealing with the…
Descriptors: Phenomenology, Misconceptions, Optics, Human Body
Liu, Bin; Bi, Qing-sheng – Online Submission, 2010
The Verhulst model can be used to forecast the sequence, which is characterized as non-monotone and fluctuant sequence or saturated S-form sequence. According to the situation of national enrollment scale of college, this paper forecasts the quantity of students taking entrance examination to college with a Verhulst model with remedy based on data…
Descriptors: Higher Education, Foreign Countries, Mathematical Models, College Entrance Examinations
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Chiou, Guo-Li; Anderson, O. Roger – Science Education, 2010
This study first used a new approach, combining students' ontological beliefs and process explanations, to represent students' mental models of heat conduction and then examined the relationships between their mental models and their predictions. Clinical interviews were conducted to probe 30 undergraduate physics students' mental models and their…
Descriptors: Undergraduate Students, Physics, Pattern Recognition, Heat
Niemi, David; Gitin, Elena – International Association for Development of the Information Society, 2012
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…
Descriptors: Higher Education, Online Courses, Academic Persistence, Identification
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Yeary, M. B.; Yu, T.; Palmer, R. D.; Monroy, H.; Ruin, I.; Zhang, G.; Chilson, P. B.; Biggerstaff, M. I.; Weiss, C.; Mitchell, K. A.; Fink, L. D. – IEEE Transactions on Education, 2010
Students are not exposed to enough real-life data. This paper describes how a community of scholars seeks to remedy this deficiency and gives the pedagogical details of an ongoing project that commenced in the Fall 2004 semester. Fostering deep learning, this multiyear project offers a new active-learning, hands-on interdisciplinary laboratory…
Descriptors: Meteorology, Data Analysis, Prediction, Natural Disasters
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Delen, Dursun – Journal of College Student Retention: Research, Theory & Practice, 2012
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is…
Descriptors: Higher Education, Student Attrition, School Holding Power, Prediction
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Shapiro, Joel; Bray, Christopher – Continuing Higher Education Review, 2011
This article describes a model that can be used to analyze student enrollment data and can give insights for improving retention of part-time students and refining institutional budgeting and planning efforts. Adult higher-education programs are often challenged in that part-time students take courses less reliably than full-time students. For…
Descriptors: Higher Education, Adult Students, Part Time Students, Enrollment Trends
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