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Kenneth R. Koedinger - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
    2018
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Intelligent tutoring systems Help students acquire cognitive skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive Behavior: A prescriptive rational model aimed at improving Help-Seeking Behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming Behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty Behaviors. An analysis of students’ actions in two different tutors suggests that the Help-Seeking model is domain independent, and that students’ Behavior is fairly consistent across classrooms, age groups, domains, and task elements

  • improving students Help Seeking skills using metacognitive feedback in an intelligent tutoring system
    Learning and Instruction, 2011
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. Koedinger
    Abstract:

    The present research investigated whether immediate metacognitive feedback on students’ Help-Seeking errors can Help students acquire better Help-Seeking skills. The Help Tutor, an intelligent tutor agent for Help Seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ Help-Seeking Behavior correlated with other independent measures of Help Seeking, and that the Help Tutor improved students’ Help-Seeking Behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also Help-Seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved Help-Seeking skills transferred to learning new domain-level content during the month following the intervention, while the HelpSeeking support was no longer in effect. Implications for metacognitive tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.

  • automated unobtrusive action by action assessment of self regulation during learning with an intelligent tutoring system
    Educational Psychologist, 2010
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    Assessment of students’ self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, Help Seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of Help Seeking. Automated feedback on Help Seeking driven by this method led to a lasting improvement in students’ Help-Seeking Behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides Help Seeking.

  • can an intelligent tutoring system predict math proficiency as well as a standardized test
    Educational Data Mining, 2008
    Co-Authors: Mingyu Feng, Joseph E Beck, Kenneth R. Koedinger
    Abstract:

    It has been reported in previous work that students' online tutoring data collected from intelligent tutoring systems can be used to build models to predict actual state test scores. In this paper, we replicated a previous study to model students' math proficiency by taking into consideration students' response data during the tutoring session and their Help-Seeking Behavior. To extend our previous work, we propose a new method of using students test scores from multiple years (referred to as cross-year data) for determining whether a student model is as good as the standardized test to which it is compared at estimating student math proficiency. We show that our model can do as well as a standardized test. We show that what we assess has prediction ability two years later. We stress that the contribution of the paper is the methodology of using student cross-year state test score to evaluate a student model against a standardized test.

  • the Help tutor does metacognitive feedback improve students Help Seeking actions skills and learning
    Intelligent Tutoring Systems, 2006
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Students often use available Help facilities in an unproductive fashion. To improve students' Help-Seeking Behavior we built the Help Tutor – a domain-independent agent that can be added as an adjunct to Cognitive Tutors. Rather than making Help-Seeking decisions for the students, the Help Tutor teaches better Help-Seeking skills by tracing students actions on a (meta)cognitive Help-Seeking model and giving students appropriate feedback. In a classroom evaluation the Help Tutor captured Help-Seeking errors that were associated with poorer learning and with poorer declarative and procedural knowledge of Help Seeking. Also, students performed less Help-Seeking errors while working with the Help Tutor. However, we did not find evidence that they learned the intended Help-Seeking skills, or learned the domain knowledge better. A new version of the tutor that includes a self-assessment component and explicit Help-Seeking instruction, complementary to the metacognitive feedback, is now being evaluated.

Vincent Aleven - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
    2018
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Intelligent tutoring systems Help students acquire cognitive skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive Behavior: A prescriptive rational model aimed at improving Help-Seeking Behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming Behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty Behaviors. An analysis of students’ actions in two different tutors suggests that the Help-Seeking model is domain independent, and that students’ Behavior is fairly consistent across classrooms, age groups, domains, and task elements

  • improving students Help Seeking skills using metacognitive feedback in an intelligent tutoring system
    Learning and Instruction, 2011
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. Koedinger
    Abstract:

    The present research investigated whether immediate metacognitive feedback on students’ Help-Seeking errors can Help students acquire better Help-Seeking skills. The Help Tutor, an intelligent tutor agent for Help Seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ Help-Seeking Behavior correlated with other independent measures of Help Seeking, and that the Help Tutor improved students’ Help-Seeking Behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also Help-Seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved Help-Seeking skills transferred to learning new domain-level content during the month following the intervention, while the HelpSeeking support was no longer in effect. Implications for metacognitive tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.

  • automated unobtrusive action by action assessment of self regulation during learning with an intelligent tutoring system
    Educational Psychologist, 2010
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    Assessment of students’ self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, Help Seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of Help Seeking. Automated feedback on Help Seeking driven by this method led to a lasting improvement in students’ Help-Seeking Behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides Help Seeking.

  • the Help tutor does metacognitive feedback improve students Help Seeking actions skills and learning
    Intelligent Tutoring Systems, 2006
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Students often use available Help facilities in an unproductive fashion. To improve students' Help-Seeking Behavior we built the Help Tutor – a domain-independent agent that can be added as an adjunct to Cognitive Tutors. Rather than making Help-Seeking decisions for the students, the Help Tutor teaches better Help-Seeking skills by tracing students actions on a (meta)cognitive Help-Seeking model and giving students appropriate feedback. In a classroom evaluation the Help Tutor captured Help-Seeking errors that were associated with poorer learning and with poorer declarative and procedural knowledge of Help Seeking. Also, students performed less Help-Seeking errors while working with the Help Tutor. However, we did not find evidence that they learned the intended Help-Seeking skills, or learned the domain knowledge better. A new version of the tutor that includes a self-assessment component and explicit Help-Seeking instruction, complementary to the metacognitive feedback, is now being evaluated.

  • toward meta cognitive tutoring a model of Help Seeking with a cognitive tutor
    Artificial Intelligence in Education, 2006
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can Help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only Helps students acquire domain-specific skills, but also develop better general Help-Seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's Help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective Help-Seeking Behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' Help-Seeking Behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on Help Seeking has a positive effect on students' learning outcomes.

Ido Roll - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
    2018
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Intelligent tutoring systems Help students acquire cognitive skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive Behavior: A prescriptive rational model aimed at improving Help-Seeking Behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming Behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty Behaviors. An analysis of students’ actions in two different tutors suggests that the Help-Seeking model is domain independent, and that students’ Behavior is fairly consistent across classrooms, age groups, domains, and task elements

  • improving students Help Seeking skills using metacognitive feedback in an intelligent tutoring system
    Learning and Instruction, 2011
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. Koedinger
    Abstract:

    The present research investigated whether immediate metacognitive feedback on students’ Help-Seeking errors can Help students acquire better Help-Seeking skills. The Help Tutor, an intelligent tutor agent for Help Seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ Help-Seeking Behavior correlated with other independent measures of Help Seeking, and that the Help Tutor improved students’ Help-Seeking Behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also Help-Seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved Help-Seeking skills transferred to learning new domain-level content during the month following the intervention, while the HelpSeeking support was no longer in effect. Implications for metacognitive tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.

  • automated unobtrusive action by action assessment of self regulation during learning with an intelligent tutoring system
    Educational Psychologist, 2010
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    Assessment of students’ self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, Help Seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of Help Seeking. Automated feedback on Help Seeking driven by this method led to a lasting improvement in students’ Help-Seeking Behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides Help Seeking.

  • the Help tutor does metacognitive feedback improve students Help Seeking actions skills and learning
    Intelligent Tutoring Systems, 2006
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Students often use available Help facilities in an unproductive fashion. To improve students' Help-Seeking Behavior we built the Help Tutor – a domain-independent agent that can be added as an adjunct to Cognitive Tutors. Rather than making Help-Seeking decisions for the students, the Help Tutor teaches better Help-Seeking skills by tracing students actions on a (meta)cognitive Help-Seeking model and giving students appropriate feedback. In a classroom evaluation the Help Tutor captured Help-Seeking errors that were associated with poorer learning and with poorer declarative and procedural knowledge of Help Seeking. Also, students performed less Help-Seeking errors while working with the Help Tutor. However, we did not find evidence that they learned the intended Help-Seeking skills, or learned the domain knowledge better. A new version of the tutor that includes a self-assessment component and explicit Help-Seeking instruction, complementary to the metacognitive feedback, is now being evaluated.

  • toward meta cognitive tutoring a model of Help Seeking with a cognitive tutor
    Artificial Intelligence in Education, 2006
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can Help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only Helps students acquire domain-specific skills, but also develop better general Help-Seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's Help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective Help-Seeking Behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' Help-Seeking Behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on Help Seeking has a positive effect on students' learning outcomes.

Bruce M. Mclaren - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
    2018
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Intelligent tutoring systems Help students acquire cognitive skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive Behavior: A prescriptive rational model aimed at improving Help-Seeking Behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming Behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty Behaviors. An analysis of students’ actions in two different tutors suggests that the Help-Seeking model is domain independent, and that students’ Behavior is fairly consistent across classrooms, age groups, domains, and task elements

  • improving students Help Seeking skills using metacognitive feedback in an intelligent tutoring system
    Learning and Instruction, 2011
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. Koedinger
    Abstract:

    The present research investigated whether immediate metacognitive feedback on students’ Help-Seeking errors can Help students acquire better Help-Seeking skills. The Help Tutor, an intelligent tutor agent for Help Seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ Help-Seeking Behavior correlated with other independent measures of Help Seeking, and that the Help Tutor improved students’ Help-Seeking Behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also Help-Seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved Help-Seeking skills transferred to learning new domain-level content during the month following the intervention, while the HelpSeeking support was no longer in effect. Implications for metacognitive tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.

  • automated unobtrusive action by action assessment of self regulation during learning with an intelligent tutoring system
    Educational Psychologist, 2010
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    Assessment of students’ self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, Help Seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of Help Seeking. Automated feedback on Help Seeking driven by this method led to a lasting improvement in students’ Help-Seeking Behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides Help Seeking.

  • the Help tutor does metacognitive feedback improve students Help Seeking actions skills and learning
    Intelligent Tutoring Systems, 2006
    Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Ryan S. Baker, Kenneth R. Koedinger
    Abstract:

    Students often use available Help facilities in an unproductive fashion. To improve students' Help-Seeking Behavior we built the Help Tutor – a domain-independent agent that can be added as an adjunct to Cognitive Tutors. Rather than making Help-Seeking decisions for the students, the Help Tutor teaches better Help-Seeking skills by tracing students actions on a (meta)cognitive Help-Seeking model and giving students appropriate feedback. In a classroom evaluation the Help Tutor captured Help-Seeking errors that were associated with poorer learning and with poorer declarative and procedural knowledge of Help Seeking. Also, students performed less Help-Seeking errors while working with the Help Tutor. However, we did not find evidence that they learned the intended Help-Seeking skills, or learned the domain knowledge better. A new version of the tutor that includes a self-assessment component and explicit Help-Seeking instruction, complementary to the metacognitive feedback, is now being evaluated.

  • toward meta cognitive tutoring a model of Help Seeking with a cognitive tutor
    Artificial Intelligence in Education, 2006
    Co-Authors: Vincent Aleven, Ido Roll, Bruce M. Mclaren, Kenneth R. Koedinger
    Abstract:

    The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can Help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only Helps students acquire domain-specific skills, but also develop better general Help-Seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's Help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective Help-Seeking Behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' Help-Seeking Behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on Help Seeking has a positive effect on students' learning outcomes.

Daniel Eisenberg - One of the best experts on this subject based on the ideXlab platform.

  • Help Seeking for mental health on college campuses review of evidence and next steps for research and practice
    Harvard Review of Psychiatry, 2012
    Co-Authors: Daniel Eisenberg, Justin Hunt, Nicole Speer
    Abstract:

    This article reviews what is known about Help-Seeking Behavior for mental health problems in college populations and offers suggestions for the next steps that could be undertaken to improve knowledge and practice in this area. Our review suggests that traditional barriers, such as stigma, can only partially explain the high prevalence of untreated disorders. We discuss the conclusions and limitations of research on campus-based intervention strategies, including anti-stigma campaigns, screening programs, and gatekeeper trainings. In proposing new directions for research and practice, we consider insights from research on other health Behaviors (e.g., diet and exercise) as well as innovative ideas from Behavioral economics and cognitive psychology regarding Behavior change.

  • mental health service utilization among college students in the united states
    Journal of Nervous and Mental Disease, 2011
    Co-Authors: Daniel Eisenberg, Justin Hunt, Nicole Speer, Kara Zivin
    Abstract:

    We aimed to provide the most comprehensive picture, to date, of service utilization and Help-Seeking Behavior for mental health problems among college students in the United States. We conducted online surveys in 2007 and 2009 of random samples of students in 26 campuses nationwide. Among students with an apparent mental health problem (32% of the weighted sample), 36% received any treatment in the previous year. The prevalence of psychotherapy and medication use was approximately equal. Treatment prevalence varied widely across campuses, with some campuses having prevalence 2 to 3 times higher than those of others. Apparent barriers to Help-Seeking included skepticism on treatment effectiveness and a general lack of perceived urgency. Overall, the findings indicate that Help-Seeking for mental health varies substantially across student characteristics and across campuses. Strategies to address the low prevalence of treatment will need to be responsive to this diversity.

  • mental health problems and Help Seeking Behavior among college students
    Journal of Adolescent Health, 2010
    Co-Authors: Justin Hunt, Daniel Eisenberg
    Abstract:

    Mental disorders are as prevalent among college students as same-aged non-students, and these disorders appear to be increasing in number and severity. The purpose of this report is to review the research literature on college student mental health, while also drawing comparisons to the parallel literature on the broader adolescent and young adult populations.

  • perceived stigma and Help Seeking Behavior longitudinal evidence from the healthy minds study
    Psychiatric Services, 2009
    Co-Authors: Ezra Golberstein, Daniel Eisenberg, Sarah E Gollust
    Abstract:

    Objective Despite considerable policy interest in the association between perceived public stigmatization of mental illness and use of mental health services, limited empirical evidence, particularly from longitudinal data, documents this relationship. This study used longitudinal data to estimate the association between perceived public stigmatization and subsequent mental health care Seeking. Methods: A Web-based survey was used to collect data from a random sample of undergraduate and graduate students at a university at baseline and two years later (N=732). Logistic regression models assessed the association between students’ perceived public stigma at baseline and measures of subsequent Help Seeking for mental health problems (perceived need for Help and use of mental health services) at follow-up. Results: No significant associations were found between perceived public stigma and Help-Seeking Behavior over the two-year period. Conclusions: In this population of college students, perceived stigma did not appear to pose a substantial barrier to mental health care.