安徽农学通报 >
2026 , Vol. 32 >Issue 5: 124 - 126
DOI: https://doi.org/10.16377/j.cnki.issn1007-7731.2026.05.029
生物化学线上线下混合授课模式构建与实践
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闫 安(1997—),男,黑龙江大庆人,硕士,研究实习员,从事生物化学教学与科研工作 |
Office editor: 张琴
收稿日期: 2025-03-17
网络出版日期: 2026-03-12
基金资助
黑龙江省高等教育教学改革研究项目研究生重点项目(SJGZY2024103)
黑龙江八一农垦大学教育教学研究一般课题(NDJY2402)
黑龙江八一农垦大学引进人才科研启动计划(XYB202313)
黑龙江省大学生创新训练计划省级一般项目(S202410223163)
Construction and practice of online and offline blended teaching model for Biochemistry
Received date: 2025-03-17
Online published: 2026-03-12
为优化生物化学课程教学效果,本文构建了生物化学课程线上线下混合式教学实施方案,并开展教学实践研究。该方案涵盖课前准备、课堂学习、课后拓展全教学环节。其中,课前通过教师互补分工构建适配性线上资源体系,借助AI助教与超星学习通平台助力学生高效预习;课中强化线下课堂互动,依托平台激活课堂氛围,结合具体章节优化教学流程;课后推送前沿内容与测试资源,通过AI助教开展针对性辅导;同时融入AI助教全教学链条,建立全过程考核评价体系,总评成绩由过程性成绩(50%)与期末考试成绩(50%)构成,过程性成绩涵盖章节测试、线上学习、课堂互动等5个维度。选取2022级、2023级4个本科教学班90名学生开展实践,结果显示,99.52%的学生按时完成线上学习与自测,线下讨论参与率为98.55%;学生期末考试平均分从72.30分提升至80.84分,优秀率从17.60%提升至36.14%,课程教学目标达成度为0.856以上;引入AI助教后课堂互动频次提升47%,作业反馈周期缩短至常规模式的1/5,在各类学科竞赛中获得多项荣誉。本文为相关课程混合式教学改革提供参考。
闫安 , 张海洋 , 石磊 . 生物化学线上线下混合授课模式构建与实践[J]. 安徽农学通报, 2026 , 32(5) : 124 -126 . DOI: 10.16377/j.cnki.issn1007-7731.2026.05.029
To optimize the teaching effect of the Biochemistry course, this study constructed an implementation scheme of online and offline blended teaching for the Biochemistry course and carried out empirical teaching research. The scheme covered the entire teaching process including pre-class preparation, in-class learning and after-class extension. In the pre-class stage, teachers formed a complementary division of labor to build an adaptive online resource system, and assisted students in efficient preview with the help of AI teaching assistants and the chaoxing learning platform. In the in-class stage, offline classroom interaction was strengthened, the classroom atmosphere was activated based on the platform, and the teaching process was optimized in combination with specific chapters. In the after-class stage, cutting-edge content and test resources were pushed, and targeted tutoring was provided through AI teaching assistants. Meanwhile, AI teaching assistants were integrated into the entire teaching chain, and a full-process assessment and evaluation system was established, in which the total score was composed of 50% process-based assessment and 50% final exam scores, with the process-based assessment covering 5 dimensions including chapter tests, online learning and classroom interaction. The empirical research was conducted on 90 students from 4 undergraduate classes of the 2022 and 2023 grades. The results showed that 99.52% of the students completed online learning and self-assessment on time, and the participation rate in offline discussions reached 98.55%. The average score of students in the final exam increased from 72.30 to 80.84, the excellent rate rose from 17.60% to 36.14%, and the achievement degree of the course teaching objectives reached more than 0.856. After the introduction of AI teaching assistants, the frequency of classroom interaction increased by 47%, the homework feedback cycle was shortened to one fifth of that of the traditional mode, and the students won many awards in various academic competitions. This study provided a reference for the blended teaching reform of similar courses.
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