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首页> 外文期刊>Computers & education >Why fifth- and seventh-graders submit off-task responses to a web-based reading comprehension tutor rather than expected learning responses
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Why fifth- and seventh-graders submit off-task responses to a web-based reading comprehension tutor rather than expected learning responses

机译：为什么五年级和七年级的学生向基于Web的阅读理解导师提交任务外答案，而不是预期的学习答案

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Research shows the students improve their reading comprehension with Intelligent Tutoring of the Structure Strategy (ITSS). One problem for ITSS is that some students are producing responses in the online instruction that are unrelated to learning and practicing the reading strategy. These types of disengaged responses can be referred to as system active off-task responses ("off-task"). In this study we characterize who produces off-task responses and why. Classification and Regression Trees (C&RT) and logistic regression analyses were used to answer the why question. Variables predicted to relate to gaming included reading strategy and skill variables, motivation, attitude, self-efficacy, and goal orientation variables, demographic variables, and type of computer feedback (simple versus elaborated). C&RT analysis could explain 66% of the variance in off-task responses. Students without off-task responses were higher in motivation to read and worked in ITSS to produce good main ideas. Students with higher off-task responses had low scores on work mastery goals. The highest producers of off-task responses in Grades 5 and 7 (averaging 24 off-task responses over 7 lessons) had low motivation to read and scored over 2 SD below average on recall tasks in ITSS. The logistic regression could explain 42% of the variance in off-task responses. Use of motivational scales prior to starting instruction as well as on-line performance measures could be used to flag students for early intervention to prevent system active off-task responses and increase on-line learning. The C&RT approach may be particularly helpful to designers in making software more appropriate for different types of students.
机译：研究表明，学生可以通过结构策略的智能辅导（ITSS）来提高阅读理解能力。 ITSS的一个问题是，一些学生正在在线教学中产生与学习和练习阅读策略无关的答案。这些类型的脱离响应可以称为系统活动的任务外响应（“任务外”）。在这项研究中，我们描述了谁会产生任务外响应以及原因。分类和回归树（C＆RT）和逻辑回归分析用于回答“为什么”问题。预测与游戏相关的变量包括阅读策略和技能变量，动机，态度，自我效能感和目标取向变量，人口统计学变量以及计算机反馈的类型（简单与详细）。 C＆RT分析可以解释任务外响应中66％的方差。没有任务外反应的学生更有动力阅读和在ITSS中工作，以产生好的主要思想。任务外回应较高的学生在工作掌握目标上得分较低。 5年级和7年级的非任务响应的最高生成者（在7节课中平均有24个非任务响应）的阅读动机较低，并且在ITSS的召回任务中得分低于平均水平2 SD。逻辑回归可以解释任务外响应中42％的方差。在开始教学之前，可以使用激励量表和在线表现量度来标记学生的早期干预，以防止系统主动下班后的反应并增加在线学习。 C＆RT方法可能对设计人员在使软件更适合不同类型的学生方面特别有用。

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