Detecting Student Misuse of Intelligent Tutoring Systems

By Ryan Shaun Joazeiro de Baker, Albert T. Corbett, K. Koedinger. International Conference on Intelligent Tutoring Systems, 2004

409 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/e020164c8ebc962bad03b64d7e064b95c59315f9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

Citations in the corpus

Addressing the assessment challenge with an online system that tutors as it assesses (2009, background): “For example, it exposes students’ unusual behavior like making far more attempts and requesting more hints than other students in the class, which might be evidence that students did not take the ASSISTments seriously or were “gaming the system” (Baker et al. 2004; Walonoski and Heffernan 2006).”
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor (2006, background): “Previously, when the student clicked through a hint, the Help Tutor would present a message and then re-display the same hint, requiring that the student again spend the same amount of reading/comprehending time as initially required.”
The State of Educational Data Mining in 2009: A Review and Future Visions. (2009, background): “For instance, in recent years, researchers have used EDM methods to infer whether a student is gaming the system [Baker et al. 2004], experiencing”
Web usage mining for predicting final marks of students that use Moodle courses (2013, background): “…student groups with similar characteristics and reactions to a particular pedagogical strategy [19]; detecting students’ misuse or game-playing [20]; grouping students who are hint-driven or failure-driven, and ﬁnd common misconceptions…”
Educational Data Mining: A Review of the State of the Art (2010, background): “dent’s learning problems [126]; a latent response model to identify if students are playing with the system (to detect student misuse) in a way that would lead to poor learning [15] and to”
Data Mining Algorithms to Classify Students (2008, background): “…student groups with similar characteristics and reactions to a particular pedagogical strategy [6], to detect students’ misuse or game-playing [2], to group students who are hint-driven or failure-driven and find common misconceptions that students possess [34], to identify learners with low…”
Affect-aware tutors: recognising and responding to student affect (2009, background): “It has been estimated that students who game the system learn two thirds of what students who do not game the system learn (Baker et al., 2004).”