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Teaching College Level Content and Reading Comprehension Skills Simultaneously via an Artificially Intelligent Adaptive Computerized Instructional System

机译:通过人工智能自适应计算机教学系统同时教授大学水平的内容和阅读理解技能

摘要

This paper presents a behavioral model for conceptualizing advanced reading comprehension as a “higher order” behavior class. Also discussed are strategies and tactics utilized by an artificially intelligent adaptive tutoring and testing software system designed to shape such comprehension skills while also teaching subject-specific “content” to college students. The system, called MediaMatrix, offers internet delivery of relatively traditional textbook content using highly individualized and adaptive tutorial and assessment procedures (Ray, 1995a; 1995b, 2004). Extant and new research on the effectiveness of this system is presented, with particular emphasis on a preliminary study of students in two small sections of an introductory psychology course. Students were evaluated during early (pre) and late (post) portions of the semester using two equivalent forms of a specially constructed SA T/GRE type reading comprehension test. A statistically significant 17% gain from pre-to-post reading comprehension scores was observed, suggesting that both the behavioral model and the MediaMatrix strategies and tactics for shaping such higher order behaviors merit further research. Practical implications of teaching both specific course content and higher order behaviors such as reading comprehension without direct teacher contact are especially noted.
机译:本文提出了一种行为模型,用于将高级阅读理解概念化为“高阶”行为类别。还讨论了由人工智能自适应补习和测试软件系统利用的策略和战术,旨在塑造这种理解能力,同时还向大学生教授特定主题的“内容”。该系统称为MediaMatrix,它使用高度个性化的自适应教程和评估程序,提供相对传统的教科书内容的互联网传送(Ray,1995a; 1995b,2004)。提出了关于该系统有效性的现有和新研究,特别着重于对心理学入门课程的两个小部分的学生进行了初步研究。在学期的前期(前)和期末(后)对学生进行了评估,使用了两种等效形式的特殊构造的SA T / GRE型阅读理解测试。从阅读前到阅读后的理解得分上,统计学上有17%的显着提高,这表明行为模型以及塑造此类高阶行为的MediaMatrix策略和策略都值得进一步研究。特别要指出的是,教授特定课程内容和高阶行为(例如在没有直接与教师接触的情况下进行阅读理解)的实践意义。

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    Ray Roger D; Belden Noelle;

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  • 年度 2010
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