Active Learning in Large Enrollment Classes: Learning Modules that Work

宾夕法尼亚州立大学地球科学和教学系统项目之间的合作旨在开发一种机制，为学生提供大规模的讲座课程，包括通识教育课程，各种实验室和实地经验，以丰富学生在应用于地球科学时对科学方法的学习。对于这些大型课程，提供单独的实验部分在后勤上是不切实际的，本项目将制定简单的策略，可以在大型教室环境中使用。在这个项目中，基于当前教学理解的新活动将作为地球101（自然灾害：好莱坞与现实）课程持续发展的一部分实施，该课程每年为3200多名通识教育学生提供服务。这些“类似实验室”的模块将建立在与实际危害相关的真实数据集和社会状况之上。原型模块，如帕克菲尔德地震预测项目，在初步测试中显示出良好的前景，将扩展和更正式地评估它们对学生学习和理解的影响。将要开发的模块将涉及四个主要主题之一：a)自然事件和过程速率在空间和时间上的量化模式；B)将科学分析与社会影响结合起来；C)纳入实时或接近实时的数据；d)基于社会经济或文化属性比较类似自然事件在不同环境中的后果。从这个项目中，将产生一套经过测试的、有效的主动学习模块，并提供给不同机构的教育工作者。

Active Learning in Large Enrollment Classes: Learning Modules that WorkThis collaboration between the Geosciences and Instructional Systems programs at Penn State University aims to develop mechanisms for giving students in large enrollment lecture classes, including General Education classes, the types of laboratory and field experiences which enrich student learning about the scientific method when applied to the geosciences. For these large courses, offering individual laboratory sections is logistically impractical, and this project will develop simple strategies that can be utilized within the large lecture room environment. In this project, new activities that are based on current pedagogical understanding will be implemented as part of the continuing development of the Earth 101 (Natural Disasters: Hollywood vs. Reality) class, which serves more than 3200 General Education students per year. These "lab-like" modules will be built on real data sets and societal situations that relate to actual hazards. Prototype modules, such as the Parkfield Earthquake Prediction project, that have shown good promise during preliminary testing will be expanded on and more formally evaluated for their impact on student learning and comprehension. Modules to be developed will address one of four main themes: a) quantifying patterns in space and time of natural events and rates of processes; b) coupling scientific analyses to societal impacts; c) incorporating real-time or near real-time data; and d) comparing the consequences of similar natural events in different environments based on socio-economic or cultural attributes. From this project, a suite of tested, effective active-learning modules will be produced and made available to educators at different institutions.