Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Personis: A Server for User Models
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Making large class teaching more adaptive with the logic-ITA
ACE '04 Proceedings of the Sixth Australasian Conference on Computing Education - Volume 30
Bayes nets in educational assessment: Where the numbers come from
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learner reflection in student self-assessment
ACE '07 Proceedings of the ninth Australasian conference on Computing education - Volume 66
Bootstrapping Accurate Learner Models from Electronic Portfolios
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Tackling HCI challenges of creating personalised, pervasive learning ecosystems
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Scrutable adaptation: because we can and must
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
From learner information packages to student models: which continuum?
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Scrutable learner modelling and learner reflection in student self-assessment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
The effect of predicting expertise in open learner modeling
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
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This paper describes our work towards building detailed scrutable student models to support learner reflection, by exploiting diverse sources of evidence from student use of web learning resources and providing teachers and learners with control over the management of the process. We build upon our automatically generated light-weight ontologies using them to infer from the fine-grained evidence that is readily available to higher level learning goals. To do this, we have to determine how to interpret web log data for audio plus text learning materials as well as other sources, how to combine such evidence in ways that are controllable and understandable for teachers and learners, as required for scrutability, and finally, how to propagate across granularity levels, again within the philosophy of scrutability. We report evaluation of this approach. This is based on a qualitative usability study, where users demonstrated good, intuitive understanding of the student model visualisation with system inferences.