Getting to Know Your Student in Distance Learning Contexts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4227)


Good teachers know their students, and exploit this knowledge to adapt or optimise their instruction. Teachers know their students because they interact with them face-to-face in classroom or one-to-one tutoring sessions. They can build student models, for instance, by exploiting the multi-faceted nature of human-human communication. In distance-learning environments, teacher and student have to cope with the lack of such direct interaction, and this must have detrimental effects for both teacher and student. In this paper, we investigate the need of teachers for tracking student actions in computer-mediated settings. We report on a teacher’s questionnaire that we devised to identify the needs of teachers to make distance learning a less detached experience. Our analysis of the teachers’ responses shows that there is a preference for information that relates to student performance (e.g., success rate in exercises, mastery level for a concept, skill, or method) and analysis of frequent errors or misconceptions. Our teachers judged information with regard to social nets, navigational pattern, and historical usage data less interesting. It shows that current e-learning environments have to improve to satisfy teachers’ needs for tracking students in distance learning contexts.


Bayesian Belief Network Virtual Learning Environment Educational Data Mining Mastery Level Student Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.DFKIGerman Research Centre for Artificial IntelligenceSaarbrückenGermany

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