离散数学全过程反馈混合式教学研究  

Blended Teaching of Discrete Mathematics Based on Whole-Process Data Feedback

作　　者：马慧[1] 曲海鹏[1] 洪锋[1] 盛艳秀[1] MA Hui;QU Hai-peng;HONG Feng;SHENG Yan-xiu(Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,China)

机构地区：[1]中国海洋大学信息科学与工程学部,山东青岛266100

出　　处：《软件导刊》2023年第1期99-104,共6页Software Guide

基　　金：山东省自然科学基金项目(ZR2021MD054);山东省本科教学改革研究项目面上项目(M2021272,M2020065);中国海洋大学本科教育教学研究重点项目(2021ZD03);中国海洋大学教师教学发展基金项目(2021JXJJ13);中国海洋大学研究生精品示范课建设项目(HDYK22023)。

摘　　要：围绕新工科建设需求和离散数学的课程特点,研究基于教学数据和全过程反馈的混合式学习策略与实现方法。针对学生面临的学习困难问题,结合学习理论与教学数据进行教学设计,实现教学活动全过程评估和反馈强化。以学生发展为中心,引导学生通过课程讨论、习题设计、小组合作等方式深度参与课程,帮助学生主动学习。教学过程涵盖教材内外和学校内外的教学活动,重视与前驱课程以及后续课程的联系,帮助学生掌握计算机学科的整体逻辑。教学实践表明,基于教学大数据和全过程反馈强化等教学方法能够有效培养学生的计算思维,契合专业认证的成果导向与持续改进方向,为学生终身发展奠定基础。通过课程教学目标的达成度分析、CSP认证成绩变化、竞赛获奖等,验证了教学的有效性。For the requirement of emerging engineering education and the characteristics of discrete mathematics course,the blended onlineoffline teaching based on the data and whole-process feedback is studied.To solve the difficulties the students faced,analyze the teaching designing based on learning theory and data,and realize the whole-process teaching evaluation and feedback.Taking students as center of education,we help students to complete deep learning and self-motivated learning through the discussion,taking part in the designing of exercises,and cooperation of groups.The teaching,involving activities inside-outside textbook and campus,pays attention to the relations of different courses and helps students master the whole logic of computer discipline.The applications show that the teaching practices based on wholeprocess feedback and data can promote the computational thinking,agree with the output-oriented principle and continuous improvement of professional certification,and help to complete lifetime development of students.The effectivity of the teaching is verified by complementation degree of course goals,changing of CSP certification scores,and prizes in different contests.

关 键 词：离散数学 混合式教学 全过程反馈 数据分析 自主学习 

分 类 号：G434[文化科学—教育学]