Active Learning in Large Enrollment Classes: Learning Modules that Work

主动学习在大招生类：学习模块，工作这之间的合作地球科学和教学系统计划在宾夕法尼亚州立大学的目的是发展机制，让学生在大招生讲座类，包括通识教育类，实验室和现场经验的类型，丰富学生学习的科学方法时，应用于地球科学。 对于这些大型课程，提供单独的实验室部分在后勤上是不切实际的，这个项目将开发简单的策略，可以在大教室环境中使用。 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，每年为3200多名普通教育学生提供服务。 这些“类似实验室”的模块将建立在与实际危害有关的真实的数据集和社会情况基础上。 原型模块，如帕克菲尔德地震预测项目，在初步测试中表现出良好的前景，将扩大和更正式地评估其对学生学习和理解的影响。 将开发的模块将涉及四个主要主题之一：a）量化自然事件的时空模式和过程速度; B）将科学分析与社会影响结合起来; c）纳入实时或近实时数据; 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。 通过该项目，将制作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.