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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
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
Simko, Marian; Bielikova, Maria – International Working Group on Educational Data Mining, 2009
To make learning process more effective, the educational systems deliver content adapted to specific user needs. Adequate personalization requires the domain of learning to be described explicitly in a particular detail, involving relationships between knowledge elements referred to as concepts. Manual creation of necessary annotations is in the…
Descriptors: Foreign Countries, Data Analysis, Individualized Instruction, Electronic Learning