Active Learning in Large Enrollment Classes: Learning Modules that Work

在大招生班级中的主动学习：有效的学习模块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机制，在大招生课堂上向学生提供包括普通教育课在内的各种类型的实验室和实地经验，以丰富学生对应用于地球科学的科学方法的学习。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，这个项目将开发出简单的策略，可以在大型课堂环境中使用。在这个项目中，将实施基于当前教学理解的新活动，作为地球101(自然灾害：好莱坞与现实)课程的持续发展的一部分，该课程每年为3200多名普通教育学生提供服务。这些“类似实验室”的模块将建立在真实的数据集和与实际危险相关的社会情况之上。在初步测试中显示出良好前景的原型模块，如帕克菲尔德地震预测项目，将得到扩展，并就其对学生学习和理解的影响进行更正式的评估。拟开发的模块将涉及四个主题之一：a)量化自然事件的时空模式和进程速率；b)将科学分析与社会影响相结合；c)纳入实时或接近实时的数据；d)根据社会经济或文化属性比较不同环境中类似自然事件的后果。从这个项目中，将制作一套经过测试的、有效的主动学习模块，并向不同机构的教育工作者提供。

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.