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Abdous, M'hammed; He, Wu; Yen, Cherng-Jyh – Educational Technology & Society, 2012
As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students' learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped…
Descriptors: Electronic Learning, Online Courses, Video Technology, Synchronous Communication
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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
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
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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
Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey – International Working Group on Educational Data Mining, 2009
This paper proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying…
Descriptors: Programming, Evidence, Intelligent Tutoring Systems, Regression (Statistics)
Zafra, Amelia; Ventura, Sebastian – International Working Group on Educational Data Mining, 2009
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Descriptors: Foreign Countries, Programming, Academic Achievement, Grades (Scholastic)
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Huang, Chenn-Jung; Chu, San-Shine; Guan, Chih-Tai – Computers & Education, 2007
In recent years, designing useful learning diagnosis systems has become a hot research topic in the literature. In order to help teachers easily analyze students' profiles in intelligent tutoring system, it is essential that students' portfolios can be transformed into some useful information to reflect the extent of students' participation in the…
Descriptors: Portfolios (Background Materials), Prediction, Online Courses, Intelligent Tutoring Systems
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Fennell, Joseph P.; And Others – 1977
This report summarizes the findings of a one man-month study of the current status and future direction of instructional computing in the Montgomery County, Maryland, Public Schools including computer assisted instruction, computer managed instruction, computer assisted problem solving, and data processing education. The report is presented in six…
Descriptors: Computer Assisted Instruction, Computer Managed Instruction, Computer Science Education, Costs
Uhlig, George E. – 1984
Dangers are inherent in predicting the future. In discussing the future of computers, specifically, it is useful to consider the brief history of computers from the development of ENIAC to microcomputers. Advances in computer technology can be seen by looking at changes in individual components, including internal and external memory, the…
Descriptors: Computer Assisted Instruction, Computer Literacy, Computer Managed Instruction, Computers
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries