The Journal of Machine Learning Research
Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Towards modeling threaded discussions using induced ontology knowledge
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Wings: Intelligent Workflow-Based Design of Computational Experiments
IEEE Intelligent Systems
Computational workflows for assessing student learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
A network analysis of student groups in threaded discussions
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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The Pedagogical Assessment Workflow System (PAWS) is a new workflow-based pedagogical assessment framework that enables the efficient and robust integration of diverse datasets for the purposes of student assessment. The paper highlights two particular e-learning workflows supported by PAWS. The first workflow correlates student performance, as measured by project grades, with different dialogue roles, information seeker and information provider, that students take on in project-based discussion forums. The second workflow identifies the distribution of question topics within student discussions. Both workflows employ state of the art natural language processing techniques and machine learning algorithms for dialogue classification tasks. Workflow results were reviewed with a course instructor and feedback regarding the analysis and its fidelity are reported.