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Showing 1 to 15 of 19 results Save | Export
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Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
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Nesrine Mansouri; Mourad Abed; Makram Soui – Education and Information Technologies, 2024
Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…
Descriptors: Undergraduate Students, Decision Making, Majors (Students), Specialization
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Korchi, Adil; Dardor, Mohamed; Mabrouk, El Houssine – Education and Information Technologies, 2020
Learning techniques have proven their capacity to treat large amount of data. Most statistical learning approaches use specific size learning sets and create static models. Withal, in certain some situations such as incremental or active learning the learning process can work with only a smal amount of data. In this case, the search for algorithms…
Descriptors: Learning Analytics, Data, Computation, Mathematics
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Lemay, David John; Doleck, Tenzin – Interactive Learning Environments, 2022
Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…
Descriptors: MOOCs, Academic Achievement, Prediction, Student Behavior
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Bertolini, Roberto; Finch, Stephen J.; Nehm, Ross H. – International Journal of Educational Technology in Higher Education, 2021
Educators seek to harness knowledge from educational corpora to improve student performance outcomes. Although prior studies have compared the efficacy of data mining methods (DMMs) in pipelines for forecasting student success, less work has focused on identifying a set of relevant features prior to model development and quantifying the stability…
Descriptors: Data Processing, Prediction, Validity, Undergraduate Students
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Chance, Beth; Reynolds, Shea – Journal of Statistics Education, 2019
Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an…
Descriptors: Prediction, Statistics, Data Processing, Homework
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Callanan, Gerard A.; Perri, David F.; Tomkowicz, Sandra M. – Journal of Education for Business, 2018
The authors present a pedagogical primer on the highly controversial business strategies of data mining and automated prediction. They provide a summary that allows business professors and students the opportunity to better understand the privacy and ethical issues that arise from high-tech, Internet-based organizations implementing programs to…
Descriptors: Automation, Prediction, Discussion (Teaching Technique), Privacy
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Hildebrandt, Mireille – Journal of Learning Analytics, 2017
This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of…
Descriptors: Behaviorism, Data Processing, Profiles, Learning Processes
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic – European Journal of Open, Distance and E-Learning, 2014
This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…
Descriptors: Online Courses, Distance Education, Dropout Characteristics, Prediction
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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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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Chang, Chih-Kai; Chen, Gwo-Dong; Wang, Chin-Yeh – Behaviour & Information Technology, 2011
Functional roles may explain the learning performance of groups. Detecting a functional role is critical for promoting group learning performance in computer-supported collaborative learning environments. However, it is not easy for teachers to identify the functional roles played by students in a web-based learning group, or the relationship…
Descriptors: Foreign Countries, Elementary School Teachers, Middle School Teachers, Group Dynamics
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Cocea, M.; Weibelzahl, S. – IEEE Transactions on Learning Technologies, 2011
Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement…
Descriptors: Prediction, Electronic Learning, Online Courses, Delivery Systems
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Baker, Ryan S. J. D.; Yacef, Kalina – Journal of Educational Data Mining, 2009
We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence…
Descriptors: Trend Analysis, Educational History, Educational Research, Research Methodology
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