ITR/PE: A Learning Environment for Information Technology Concepts Using Intensive, Unobtrusive Assessment

尽管已经开发了许多有效地使学生参与组装计算对象、模拟或数学结构的学习环境，但在很大程度上，这些系统未能在教学上利用学生在计算机上工作这一事实所产生的与评估相关的丰富数据。这与其说是因为这些环境的新，不如说是因为有效地利用事件日志、学生写作和学生结构作为认知状态、学习偏好和技能的证据所面临的挑战。我们建议整合和扩展两个在线教育软件系统，以进行一系列实验，以评估使用密集、不引人注目的评估与建设性学习环境紧密结合对学习结果和效率的影响。综合学习环境将主要在华盛顿大学新生和默瑟岛高中高年级学生中进行测试，在涉及数字图像表示和处理、网络通信和计算机编程的问题解决和构建活动中进行测试。学生的写作、在线结构、在线草图和计算机生成的活动日志将使用计算机辅助和自动标记相结合的方式进行分析。加价的结果将触发对教师的教学建议，在某些情况下，还会直接向学生提出建议。这样的干预可以采取多种形式；例如，一群在解决某个问题时陷入挫折的学生可能会受益于让另一组中的一名学生表现出色的关键步骤引起他们的注意。我们的目标将是确定有效的方法，从在线学习的产品和副产品中提取教学有用的信息，并设计能够使用这些信息并从中受益的教学。

EIA-0121345 Tanimoto, StevenUniversity of WashingtonITR/PE: A Learning Environment for Information Technology Concepts Using Intensive, Unobtrusive AssessmentAlthough many learning environments have been developed that effectively engage students in assembling computational objects, simulations, or mathematical constructions, for the most part these systems fail to take pedagogical advantage of the wealth of assessment-related data that results from the fact that the students are working on computers. This is due less to the newness of these environments than to the challenges of effectively utilizing the event logs, student writing, and student constructions as evidence of cognitive state, learning preferences, and skills. We propose to integrate and extend two software systems for online education in order to perform a series of experiments that assess the impact of using intensive, unobtrusive assessment tightly integrated with a constructive learning environment upon learning outcomes and efficiencies. The integrated learning environment will be tested primarily with University of Washington freshmen but also with Mercer Island High School seniors, in problem solving and construction activities involving digital image representation and processing, web-based communication, and computer programming. The students' writing, online constructions, online sketches and computer-generated activity logs will be analyzed using a combination of computer-assisted and automated mark-up. The results of mark-up will trigger pedagogical recommendations to instructors, and in some cases, directly to the students. Such interventions may take a variety of forms; for example, a group of students stuck at the point of frustration in solving some problem may benefit by having a key step, performed well by a student in another group, called to their attention. Our objective will be to determine effective methods both for extracting pedagogically useful information from the products and byproducts of online learning and for designing the instruction to enable and benefit from the use of this information.