COLLABORATIVE RESEARCH: WIDER: Improving Undergraduate Education Through Community Building and Adoption of an Evidence-based Practice to Evaluate Undergraduate Research

该项目正在测试，完善和传播一个有前途的循证模型，以指导有效的本科生研究。 该模型基于五年的数据。 正在进行更多的测试和完善活动，以确保在不同机构环境中的适用性。该项目将创建和提供所需的资源，以支持在任何本科院校的成功实施。 通过与卡尔顿学院本科生研究理事会（CUR）和科学教育资源中心（SERC）的合作，该项目旨在培养经过培训和激励的STEM学者的国家社区，以维持和扩大这一模式。 该模型旨在确保学生参与本科研究的学习成果尽可能强大。它通过在每次研究经历期间进行的一系列定量的、内置的形成性评价，在研究期间故意产生形成性指导。评价是这一模式的核心组成部分。该模型强调教师和学生在广泛的理想结果的多个进展评估，使评估过程提供了必要的信息，由学生和教师推进学习目标。 设计方法超越了特定的STEM学科。 这种卓越的功能提供了一个机会，在一系列的本科生研究经验的影响结果聚合。 该模型的内置评估功能将提供有关学生学习的汇总数据，可用于为资源分配决策提供信息。

This project is testing, refining, and disseminating a promising evidence-based model for guiding effective undergraduate research. The model is based on five years of data. Additional testing and refinement activity are underway to ensure applicability across diverse institutional settings. The project will create and provide the resources needed to support successful implementation at any undergraduate institution. Through collaborations with the Council on Undergraduate Research (CUR) and the Science Education Resource Center SERC) at Carleton College, the project intends to foster national communities of STEM scholars who are trained and motivated to sustain and expand this model. The model is designed to ensure that the student learning outcomes from participation in undergraduate research are as strong as possible. It deliberately generates formative guidance during the research through a series of quantitative, built-in formative evaluations conducted over the duration of each research experience. Evaluation is a central component of this model. The model emphasizes multiple progress assessments by both faculty and students across a wide range of desirable outcomes, so that the evaluation process provides essential information used by both students and faculty to advance learning objectives. The design approach transcends specific STEM disciplines. This transcendent feature offers an opportunity to aggregate impact results across a series of undergraduate research experiences. The model's built-in evaluation feature will provide summary data on student learning that could be used to inform resource allocation decisions.