Validation of learning style measures: implications for medical education practice
Validation of learning style measures: implications for medical education practice
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DOI：
10.1111/j.1365-2929.2006.02476.x
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发表时间：
2006-06-01
影响因子：
6
通讯作者：
Calhoun, Judith G.
中科院分区：
文献类型：
--
作者：
Chapman, Dane M.;Calhoun, Judith G.
背景资料：目前还不清楚哪些学习者将最受益于基于问题和计算机辅助学习的更个性化，学生结构化，互动的方法。学习风格措施的有效性是不确定的，并没有统一的学习风格结构确定预测这样的learners.Objective：本研究进行了验证学习风格的结构，并确定最有可能受益于基于问题和计算机辅助courses.Methods的学习者：使用横断面设计，3建立学习风格清单管理97年后2医学生。认知人格的测量组嵌入图形测试，信息处理的学习风格量表，和教学偏好的学习偏好量表。从3个库存的11个分量表进行因素分析，以确定共同的学习结构，并验证结构效度。结果：共有94名临床医学预科学生完成了所有3个量表。五个有意义的学习风格结构来自11个分量表：学生与教师结构的学习;具体与抽象的学习;被动与主动学习;个人与团体学习，和场依赖与场独立。11个分量表中有10个分量表的同时效度得到相关分析的支持。医学生最有可能茁壮成长的问题为基础的或计算机辅助的学习环境中，预计得分很高的抽象，积极和个人的学习结构，并会更field-independent.Conclusions：学习风格的措施进行了验证，在医学生人群和学习结构，确定学习者谁最有可能受益于基于问题的或计算机辅助的课程。
Background: It is unclear which learners would most benefit from the more individualised, student-structured, interactive approaches characteristic of problem-based and computer-assisted learning. The validity of learning style measures is uncertain, and there is no unifying learning style construct identified to predict such learners.Objective: This study was conducted to validate learning style constructs and to identify the learners most likely to benefit from problem-based and computer-assisted curricula.Methods: Using a cross-sectional design, 3 established learning style inventories were administered to 97 post-Year 2 medical students. Cognitive personality was measured by the Group Embedded Figures Test, information processing by the Learning Styles Inventory, and instructional preference by the Learning Preference Inventory. The 11 subscales from the 3 inventories were factor-analysed to identify common learning constructs and to verify construct validity. Concurrent validity was determined by intercorrelations of the 11 subscales.Results: A total of 94 pre-clinical medical students completed all 3 inventories. Five meaningful learning style constructs were derived from the 11 subscales: student- versus teacher-structured learning; concrete versus abstract learning; passive versus active learning; individual versus group learning, and field-dependence versus field-independence. The concurrent validity of 10 of 11 subscales was supported by correlation analysis. Medical students most likely to thrive in a problem-based or computer-assisted learning environment would be expected to score highly on abstract, active and individual learning constructs and would be more field-independent.Conclusions: Learning style measures were validated in a medical student population and learning constructs were established for identifying learners who would most likely benefit from a problem-based or computer-assisted curriculum.