Help seeking, learning and contingent tutoring
Computers & Education
Density biased sampling: an improved method for data mining and clustering
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Computers and Classroom Culture
Computers and Classroom Culture
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
User Modeling and User-Adapted Interaction
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
How Does Students' Help-Seeking Behaviour Affect Learning?
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
An Intelligent SQL Tutor on the Web
International Journal of Artificial Intelligence in Education
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Effects of Dissuading Unnecessary Help Requests While Providing Proactive Help
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Affect and Usage Choices in Simulation Problem-Solving Environments
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Repairing Disengagement With Non-Invasive Interventions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Educational Software Features that Encourage and Discourage “Gaming the System”
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Detection and analysis of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
WTF? detecting students who are conducting inquiry without thinking fastidiously
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
Recently, detectors of gaming the system have been developed for several intelligent tutoring systems where the problem-solving process is reified, and gaming consists of systematic guessing and help abuse Constraint-based tutors differ from the tutors where gaming detectors have previously been developed on several dimensions: in particular, higher-level answers are assessed according to a larger number of finer-grained constraints, and feedback is split into levels rather than an entire help sequence being available at any time Correspondingly, help abuse behaviors differ, including behaviors such as rapidly repeating the same answer or blank answers to elicit answers We use text replay labeling in combination with educational data mining methods to create a gaming detector for SQL-Tutor, a popular constraint-based tutor This detector assesses gaming at the level of multiple-submission sequences and is accurate both at identifying gaming within submission sequences and at identifying how much each student games the system It achieves only limited success, however, at distinguishing different types of gaming behavior from each other.