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Direct linkRomero-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
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
Download full textSmith, 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
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
Peer reviewedStanchev, Ivan – Journal of Research on Computing in Education, 1990
Discussion of the portability of educational software focuses on the software design and development process. Topics discussed include levels of portability; the user-computer dialog; software engineering principles; design techniques for student performance records; techniques of courseware programing; and suggestions for further research and…
Descriptors: Academic Achievement, Computer Assisted Instruction, Computer Software Development, Computer System Design
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring
Gordonov, Anatoliy; Kress, Michael; Klibaner, Roberta – 1998
This paper presents a model of an intelligent computer network that provides real-time evaluation of students' performance by incorporating intelligence into the application layer protocol. Specially designed drills allow students to independently solve a number of problems based on current lecture material; students are switched to the most…
Descriptors: Academic Achievement, Algorithms, Computer Assisted Instruction, Computer Managed Instruction
