An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools

We study how student behaviors associated with disengagement differ between different school settings. Towards this, we investigate the variation in the frequency of off-task behavior, gaming the system, and carelessness in an urban school, a rural school, and a suburban school in the United States of America. This analysis is conducted by applying automated detectors of these behaviors to data from students using the same Cognitive Tutor educational software for high school Geometry, across an entire school year. We find that students in the urban school go off-task and are careless significantly more than students in the rural and suburban schools. Differences between schools in terms of gaming the system are less stable. These findings suggest that some of the differences in achievement by school type may stem from differences in engagement and problem behaviors.

[1]  Antonija Mitrovic,et al.  An Intelligent SQL Tutor on the Web , 2003, Int. J. Artif. Intell. Educ..

[2]  Xitao Fan,et al.  Academic Achievement of Rural School Students: A Multi-Year Comparison with Their Peers in Suburban and Urban Schools. , 1998 .

[3]  Ryan Shaun Joazeiro de Baker,et al.  Modeling and understanding students' off-task behavior in intelligent tutoring systems , 2007, CHI.

[4]  R. Sawyer.  The Cambridge Handbook of the Learning Sciences: Introduction , 2014 .

[5]  Jonathan P. Rowe,et al.  Off-Task Behavior in Narrative-Centered Learning Environments , 2009, AIED.

[6]  Peter Gobel.  Student Off-task Behavior and Motivation in the CALL Classroom , 2008 .

[7]  K. E. Kelly,et al.  Preliminary Report on the Relation of Students' on-Task Behavior with Completion of School Work , 1999 .

[8]  Ryan Shaun Joazeiro de Baker,et al.  Labeling Student Behavior Faster and More Precisely with Text Replays , 2008, EDM.

[9]  Vincent Aleven,et al.  More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing , 2008, Intelligent Tutoring Systems.

[10]  Joseph E. Beck,et al.  Engagement tracing: using response times to model student disengagement , 2005, AIED.

[11]  M. A. Clements.  Careless Errors Made by Sixth-Grade Children on Written Mathematical Tasks. , 1982 .

[12]  Arnon Hershkovitz,et al.  The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? , 2009, AIED.

[13]  Vincent Aleven,et al.  Integrating Visual and Verbal Knowledge During Classroom Learning with Computer Tutors , 2007 .

[14]  Laura Maffei,et al.  Experiments with Aplusix in Four Countries. , 2005 .

[15]  Robert E. Slavin,et al.  Measuring Time-On-Task: Issues of Timing, Sampling and Definition. , 1980 .

[16]  J. Lloyd,et al.  Measurement and Evaluation of Task-Related Learning Behaviors: Attention to Task and Metacognition. , 1986 .

[17]  A. Corbett,et al.  The Cambridge Handbook of the Learning Sciences: Cognitive Tutors , 2005 .

[18]  R. Baker.  Is Gaming the System State-or-Trait ? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model , 2007 .

[19]  Susanna Loeb,et al.  Teacher Sorting and the Plight of Urban Schools: A Descriptive Analysis , 2002 .

[20]  Kenneth R. Koedinger,et al.  A Data Repository for the EDM Community: The PSLC DataShop , 2010 .

[21]  Ryan Shaun Joazeiro de Baker,et al.  Developing a generalizable detector of when students game the system , 2008, User Modeling and User-Adapted Interaction.