Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15232, USA.
Artif Intell Med. 2009 Nov;47(3):175-97. doi: 10.1016/j.artmed.2009.07.002. Epub 2009 Sep 25.
Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement.
Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined.
Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path.
Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.
确定在皮肤病学中应用有限执行智能辅导系统对学生错误、目标和解决方案路径的影响。确定在医学辅导系统中实施有限执行是否会阻碍学生学习最佳和最有效的解决方案。描述辅导过程中出现的偏离最佳解决方案的类型，以及这些偏差随时间的变化。确定在使用有限执行的辅导系统时，问题空间（域范围）的大小对学习增益的影响。
分析了来自 44 名病理住院医师的数据，这些数据是使用 SlideTutor——一种皮肤病学智能辅导系统挖掘出来的，该系统基于常用的诊断算法教授组织病理学诊断和报告技能。研究包括两个子域，代表不同大小和复杂程度的子算法。确定辅导系统对学生错误、目标状态和解决方案路径的影响。
学生逐渐增加与辅导系统期望的专家表现相匹配的步骤频率。所有错误类别的错误频率逐渐降低。学生的表现经常与导师定义的最佳路径不同。然而，随着学生继续接受辅导，他们会接近最佳解决方案路径。两个子域在错误和目标差异方面的表现相似。然而，学生朝着最佳解决方案路径前进的速度在两个领域之间存在差异。在浅表血管周围皮炎中进行辅导，即更大、更复杂的领域，与更接近最佳解决方案路径的速度较慢相关。
学生受益于有限执行的辅导系统，该系统利用诊断算法，但不阻止替代策略。即使执行有限，学生也会朝着最佳解决方案路径收敛。
