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| 008 | 070212s2005 th uu|m rtt 0| a1eng d | ||
| 035 | _a.b12006622 | ||
| 099 | 9 | _aAIT Diss. no.IM-05-03 | |
| 100 | 0 | _aSiriwan Suebnukarn | |
| 245 | 1 | 0 | _aIntelligent tutoring for medical problem-based learning |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2005 |
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| 300 |
_a97 p. : _bill. |
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| 490 | 1 |
_aDissertation ; _vno. IM-05-03 |
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| 500 | _aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy | ||
| 502 | _aThesis (Ph.D.) - Asian Institute of Technology, 2005 | ||
| 520 | _aThe transformation from medical student to physician is a gradual one, requiring the assimilation of a vast amount of knowledge as well as the development of clinicalreasoning skills. Clinical reasoning is the cognitive process by which the information contained in a clinical case is synthesized, integrated with the physician's knowledge and experience, and used to diagnose or manage the patient's problem. 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 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 dissertation describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of this work is the development of general domain-independent individual and collaborative student modeling techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. COMET is designed to provide an experience that emulates that of live human-tutored medical PBL sessions as much as possible while at the same time permitting the students to participate collaboratively from disparate locations. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems. Medical PBL is particularly challenging due to the complexity of the knowledge involved, the lack of standard, commonly accepted student clinical-reasoning techniques, and the lack of standards for tutoring. This means that we must first attempt to identify prototypical patterns of clinical reasoning and then formalize them to create the clinical reasoning model. Qualitative analysis of PBL tutorial sessions was performed in order to gain insight into the processes involved in PBL, thereby suggesting a framework for generating tutoring feedback. Generating appropriate tutorial actions in COMET requires a model of the students' clinical reasoning for the problem domain. In addition, 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. This modeling task is necessarily wrought with uncertainty since we have only a limited number of observations from which to infer each student's level of understanding. Therefore, the system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. COMET 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. COMET can currently support PBL problem analysis in the domains of Head injury, Stroke and Heart attack. In order to evaluate the appropriateness and quality of the hints generated by COMET, the tutoring hints generated by the system were compared with those of experienced human tutors. On average, 74.17% of the human tutors used the same hint as COMET. The 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). The validity of the modeling approach has been tested with student models in the areas 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). Finally, comparison of learning outcomes shows that student clinical reasoning gains from COMET are significantly higher than those obtained from human tutored sessions (Mann-Whitney, p = 0.011). | ||
| 650 | 0 | _aIntelligent tutoring systems | |
| 650 | 0 |
_aMedical care _xStudy and teaching |
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| 700 | 1 |
_aHaddawy, Peter, _eChairperson |
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| 700 | 1 |
_aGuha, Sumanta, _eExamination Committee |
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| 700 | 1 |
_aBohez, Erik L.J., _eExamination Committee |
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| 700 | 1 |
_aKay, Judy , _eExamination committee |
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| 710 | 2 |
_aRoyal Thai Government Fellowship, _eScholarship donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tDissertations ; _vno. IM-05-03 |
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| 856 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B09390 |
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