基于SPOC的《环境生态学》线上线下混合式教学探索研究

Exploratory Research on SPOC-Based Blended Online and Offline Teaching of “Environmental Ecology”

摘要: 针对《环境生态学》课程知识跨度大、理论与实践衔接不足以及学习过程评价不充分等问题，依托智慧树平台，面向环境工程2021-A1和2021-A2两个班级共80名学生，实施“三阶段–六环节”SPOC线上线下混合式教学。两个班采用统一教学模式，以历届采用传统教学的学生为历史比较对象。课程总学时为40学时，其中理论教学32学时、课内实验8学时。围绕学习目标，整合微课、导学单、诊断题与案例任务，将课前学习诊断、课中问题探究及课后迁移反馈相衔接，并以种群生态学和生态系统修复模块说明具体实施路径。研究建立面向历史对照的评价框架，区分可跨届比较的学业表现与反映本届学习过程的平台、问卷及作品证据。该设计为课程教学组织和过程评价提供了可操作路径；学习效果及其与教学模式的关系仍需结合跨届可比数据加以检验。

Abstract: To address issues such as the broad knowledge scope of the “Environmental Ecology” course, the disconnect between theory and practice, and insufficient assessment of the learning process, a “three-stage, six-step” SPOC-based blended teaching model was implemented. This study utilized the “Zhihuishu” (Wisdom Tree) platform and involved 80 students from two Environmental Engineering classes (2021-A1 and 2021-A2). Both classes adopted the same teaching model, with students from previous cohorts who underwent traditional instruction serving as the historical comparison group. The course comprised 40 total hours: 32 hours of theoretical instruction and 8 hours of in-class experiments. Centered on learning objectives, the course integrated micro-lectures, guided study sheets, diagnostic questions, and case-based tasks to link pre-class diagnostic learning, in-class inquiry, and post-class knowledge transfer and feedback. Specific implementation pathways were illustrated using the modules on population ecology and ecosystem restoration. An evaluation framework was established to facilitate historical comparison, distinguishing between academic performance metrics comparable across cohorts and evidence—derived from the platform, questionnaires, and student projects—reflecting the learning process of the current cohort. This design provides a practical approach to course organization and process evaluation; however, the effectiveness of the learning outcomes and the relationship between these outcomes and the teaching model require further validation using data comparable across cohorts.