基于认知负荷的C语言程序设计关键节点教学案例资源设计研究

Design of Teaching Case Resources for Key Learning Nodes in C Programming Based on Cognitive Load Theory

摘要: C语言程序设计是大学低年级学生学习程序设计思想和计算思维的重要基础课程，但由于课程内容涉及语法规则、程序执行过程、内存分配和算法实现等多类知识要素，学生在学习过程中容易出现“课堂能听懂、独立编程易出错”的现象。为识别课程学习中的关键认知负荷节点，本文基于近三届学生在线闯关平台中的历史学习数据进行分析，采用Apriori关联规则算法挖掘失败知识点之间的共现关系，识别构成学生学习中的关键负荷节点。并基于认知负荷理论，从内在认知负荷、外在认知负荷和相关认知负荷三个角度解释学生出错原因，并围绕关键节点重构教学案例资源，提出配套教学案例等资源设计方案。研究为C语言程序设计课程案例库优化和关键知识点精准教学提供了数据驱动的设计思路。

Abstract: C Programming is a fundamental course for lower-year university students to develop programming concepts and computational thinking. However, as the course involves multiple types of knowledge, including syntax rules, program execution processes, memory allocation, and algorithm implementation, students often encounter the problem of being able to understand concepts in class but making frequent errors when programming independently. To identify key cognitive load nodes in the learning process, this study analyzes historical learning data collected from an online challenge-based learning platform over the past three cohorts of students. The Apriori association rule algorithm is employed to mine co-occurrence relationships among failed knowledge points and identify key cognitive load nodes in students’ learning. Based on cognitive load theory, the causes of students’ errors are further interpreted from the perspectives of intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. Teaching case resources are then reorganized around the identified key nodes, and corresponding strategies for the design of teaching cases and related learning resources are proposed. This study provides a data-driven approach to optimizing the teaching case repository and supporting targeted instruction of key knowledge points in C Programming courses.