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Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…
Descriptors: Data Analysis, Higher Education, Course Selection (Students), Enrollment
Bravo, Javier; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
E-Learning systems offer students innovative and attractive ways of learning through augmentation or substitution of traditional lectures and exercises with online learning material. Such material can be accessed at any time from anywhere using different devices, and can be personalized according to the individual student's needs, goals and…
Descriptors: Data Analysis, Electronic Learning, College Students, Low Achievement
Stamper, John; Barnes, Tiffany – International Working Group on Educational Data Mining, 2009
We seek to simplify the creation of intelligent tutors by using student data acquired from standard computer aided instruction (CAI) in conjunction with educational data mining methods to automatically generate adaptive hints. In our previous work, we have automatically generated hints for logic tutoring by constructing a Markov Decision Process…
Descriptors: Data Analysis, Computer Assisted Instruction, Intelligent Tutoring Systems, Markov Processes
Quevedo, J. R.; Montanes, E. – International Working Group on Educational Data Mining, 2009
Specifying the criteria of a rubric to assess an activity, establishing the different quality levels of proficiency of development and defining weights for every criterion is not as easy as one a priori might think. Besides, the complexity of these tasks increases when they involve more than one lecturer. Reaching an agreement about the criteria…
Descriptors: Data Analysis, Scoring Rubrics, Evaluation Criteria, Automation
Anaya, Antonio R.; Boticario, Jesus G. – International Working Group on Educational Data Mining, 2009
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…
Descriptors: Data Analysis, Cooperative Learning, College Students, Adult Students
Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul – International Working Group on Educational Data Mining, 2009
Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…
Descriptors: Data Analysis, Methods, Computer Software, Computer Assisted Testing
Rus, Vasile; Lintean, Mihai; Azevedo, Roger – International Working Group on Educational Data Mining, 2009
This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge…
Descriptors: Data Analysis, Prior Learning, Cognitive Structures, College Students
Madhyastha, Tara M.; Tanimoto, Steven – International Working Group on Educational Data Mining, 2009
Most of the emphasis on mining online assessment logs has been to identify content-specific errors. However, the pattern of general "consistency" is domain independent, strongly related to performance, and can itself be a target of educational data mining. We demonstrate that simple consistency indicators are related to student outcomes,…
Descriptors: Web Based Instruction, Computer Assisted Testing, Computer Software, Computer Science Education
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)