A collaborative environment for semi-structured medical problem based learning

Mahling, Dirk E.; Sorrows, B.B.; Skogseid, Ingjerd

doi:10.3115/222020.222182

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In contrast to one-to-one ITSs, since problem solving in group PBL is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. Researchers in the area of computer-supported collaborative learning (CSCL) have developed numerous computer-supported PBL environments over the past decade (Mahling et al 1995;Suthers et al 1997). This work has focused mainly on providing and maintaining shared information resources and shared workspaces rather than on intelligent tutoring.…”

Section: Introductionmentioning

confidence: 99%

Modeling individual and collaborative problem-solving in medical problem-based learning

Suebnukarn

Haddawy

2006

User Model User-Adap Inter

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Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education. While PBL has many strengths, effective PBL tutoring is time-intensive and requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes the student modeling approach used in the COMET intelligent tutoring system for collaborative medical PBL. To generate appropriate tutorial actions, COMET uses a model of each student's clinical reasoning for the problem domain. In addition, since problem solving in group PBL is a collaborative process, COMET uses a group model that enables it to do things like focus the group discussion, promote collaboration, and suggest peer helpers. Bayesian networks are used to model individual student knowledge and activity, as well as that of the group. The validity of the modeling approach has been tested with student models in the areas 212 User Model User-Adap Inter (2006) 16:211-248 of head injury, stroke, and heart attack. Receiver operating characteristic (ROC) curve analysis shows that the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that the focus of group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823).

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Section: Introductionmentioning

confidence: 99%

Modeling individual and collaborative problem-solving in medical problem-based learning

Suebnukarn

Haddawy

2006

User Model User-Adap Inter

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“…Denominado por AAERO (Ambiente de Aprendizado para Ensino de Redes de Computadores Orientado a PrOblemas), o ambiente suporta as funções necessárias para dois atores do processo de ensino aprendizagem (aluno e professor). Para o desenvolvimento deste protótipo, o pesquisador buscou na literatura por ferramentas colaborativas e específicas para o PBL, explicitando a BELVEDERE [15], CROCODILE -Creative Open Cooperative Distribued Learning Environment [16], CALE -Computer Assisted Learning and Exploration Environment [17] e CoMMIT -Collaborative Multi-Media Instructional Toolkit [18] e Munics [19]. E, com base na identificação e avaliação das características gerais identificadas nestes ambientes, Dutra propõe o AAERO.…”

Section: Estudos De Lms Baseados Em Pblunclassified

Aplicando a Taxonomia de Bloom Revisada para Gerenciar Processos de Ensino em Sistemas de Aprendizagem Baseada em Problemas

Rodrigues

Santos

2013

RBIE

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Resumo O processo de ensino e aprendizagem na área de Computação frequentemente sofre desafios relacionados à passividade dos alunos guiados por um método centrado no professor, por conteúdos de disciplinas isolados de sua aplicabilidade e práticas distantes da realidade de mercado. Frente a estes desafios, o método PBL (Problem Based Learning) como uma abordagem centrada no aluno, aprendizagem colaborativa e foco na resolução de problemas reais tem se mostrado uma solução adequada. No entanto, medir a efetividade de PBL não é uma tarefa fácil. O dinamismo do método e a intensidade de práticas acabam dificultando o planejamento e acompanhamento de tarefas e resultados ao longo do processo. Com esta motivação, este artigo propõe uma solução para gestão do processo de ensino e aprendizagem baseado em PBL a partir da definição de processos que fazem uso da Taxonomia de Bloom revisada, suportados por Sistemas de Gestão de Aprendizagem. Por meio da validação de um protótipo de baixa fidelidade, foi possível validar sua aplicabilidade e benefícios para o planejamento e acompanhamento do ensino na abordagem PBL.

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“…Researchers in the area of computer supported collaborative learning have developed numerous computer supported PBL environments over the past decade [2] [3]. This work has focused mainly on providing and maintaining shared information resources and shared workspaces rather than on intelligent tutoring.…”

Section: Related Workmentioning

confidence: 99%

A collaborative intelligent tutoring system for medical problem-based learning

Suebnukarn

Haddawy

2004

Proceedings of the 9th International Conference on Intelligent User Interfaces

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This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).

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A collaborative environment for semi-structured medical problem based learning

Cited by 5 publications

References 6 publications

Modeling individual and collaborative problem-solving in medical problem-based learning

Modeling individual and collaborative problem-solving in medical problem-based learning

Aplicando a Taxonomia de Bloom Revisada para Gerenciar Processos de Ensino em Sistemas de Aprendizagem Baseada em Problemas

A collaborative intelligent tutoring system for medical problem-based learning

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