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A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction
Nicoletti, Maria do Carmo; de Oliveira, Osvaldo Luiz – Higher Education Studies, 2020
In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course…
Descriptors: Artificial Intelligence, Man Machine Systems, Computation, Prediction
Livieris, Ioannis E.; Mikropoulos, Tassos A.; Pintelas, Panagiotis – Themes in Science and Technology Education, 2016
Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for…
Descriptors: Predictive Measurement, Decision Support Systems, Academic Achievement, Exit Examinations
Latham, Annabel; Crockett, Keeley; McLean, David; Edmonds, Bruce – Computers & Education, 2012
This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and…
Descriptors: Cognitive Style, Teaching Methods, Cognitive Measurement, Prediction
Romero-Zaldivar, Vicente-Arturo; Pardo, Abelardo; Burgos, Daniel; Delgado Kloos, Carlos – Computers & Education, 2012
The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. The increasing use of information and communication technology allows these interactions to be recorded so that analytic or mining techniques are used to gain a deeper…
Descriptors: Academic Achievement, Prediction, Learning Experience, Data
Alshare, Khaled A.; Freeze, Ronald D.; Lane, Peggy L.; Wen, H. Joseph – Decision Sciences Journal of Innovative Education, 2011
Success in an online learning environment is tied to both human and system factors. This study illuminates the unique contributions of human factors (comfort with online learning, self-management of learning, and perceived Web self-efficacy) to online learning system success, which is measured in terms of usage and satisfaction. The research model…
Descriptors: College Students, Student Attitudes, Satisfaction, Computer Use
Austin, Katherine A. – Computers & Education, 2009
In the wake of the information explosion and rapidly progressing technology [Mayer, R. E. (2001). "Multimedia learning". Cambridge: University Press] formulated a theory that focused on human cognition, rather than technology capacity and features. By measuring the effect of cognitive individual differences and display design manipulations on…
Descriptors: Multimedia Materials, Learning Modules, Transfer of Training, Cognitive Processes
Zheng, Lanqin; Yang, Kaicheng; Huang, Ronghuai – Educational Technology & Society, 2012
This study proposes a new method named the IIS-map-based method for analyzing interactions in face-to-face collaborative learning settings. This analysis method is conducted in three steps: firstly, drawing an initial IIS-map according to collaborative tasks; secondly, coding and segmenting information flows into information items of IIS; thirdly,…
Descriptors: Foreign Countries, Computer Uses in Education, Program Effectiveness, Research Methodology
Essa, Alfred; Ayad, Hanan – Research in Learning Technology, 2012
The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from…
Descriptors: Artificial Intelligence, Computer Graphics, Computer Interfaces, Statistical Analysis
Smith, Vernon C.; Lange, Adam; Huston, Daniel R. – Journal of Asynchronous Learning Networks, 2012
Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student…
Descriptors: Academic Achievement, At Risk Students, Prediction, Community Colleges
Moridis, Christos N.; Economides, Anastasios A. – Computers & Education, 2009
Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…
Descriptors: Prediction, Artificial Intelligence, Inferences, Psychological Patterns
Hung, Jui-Long; Hsu, Yu-Chang; Rice, Kerry – Educational Technology & Society, 2012
This study investigated an innovative approach of program evaluation through analyses of student learning logs, demographic data, and end-of-course evaluation surveys in an online K-12 supplemental program. The results support the development of a program evaluation model for decision making on teaching and learning at the K-12 level. A case study…
Descriptors: Web Based Instruction, Databases, Virtual Classrooms, Decision Support Systems
Huang, Yueh-Min; Huang, Tien-Chi; Wang, Kun-Te; Hwang, Wu-Yuin – Educational Technology & Society, 2009
The ability to apply existing knowledge in new situations and settings is clearly a vital skill that all students need to develop. Nowhere is this truer than in the rapidly developing world of Web-based learning, which is characterized by non-sequential courses and the absence of an effective cross-subject guidance system. As a result, questions…
Descriptors: Markov Processes, Transfer of Training, Probability, Internet
Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction

Purves, R. S.; Sanderson, M. – Journal of Documentation, 1998
Presents adaptations and tests undertaken to allow an information retrieval system to forecast the likelihood of avalanches on a particular day; the forecasting process uses historical data of the weather and avalanche conditions for a large number of days. Describes a method for adapting these data into a form usable by a text-based IR system and…
Descriptors: Climate, Computer Oriented Programs, Computer System Design, Data Analysis
Ogan, Amy; Aleven, Vincent; Jones, Christopher – International Journal of Artificial Intelligence in Education, 2009
Most successes in intelligent tutoring systems have come in well-defined domains like algebra or physics. We investigate how to support students in acquiring ill-defined skills of intercultural competence using an online environment that employs clips of feature films from a target culture. To test the effectiveness of a set of attention-focusing…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Perspective Taking, Cultural Awareness