How Students can Effectively Choose the Right Courses: Building a Recommendation System to Assist Students in Choosing Courses Adaptively

In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students’ interests, abilities and career development. To meet students’ individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students’ past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students’ actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most.本研究构建了一款兼顾学生兴趣、能力与职业发展的个性化混合课程推荐系统（Personalized Hybrid Course Recommendation System，PHCRS）。为满足学生的个性化需求，本研究采用当前应用最为广泛的五种算法，包括基于内容的过滤（Content-based Filtering）、基于流行度的方法（Popularity-based Methods）、基于项目的协同过滤（Item-based Collaborative Filtering）、基于用户的协同过滤（User-based Collaborative Filtering）以及基于评分的方法（Score-based Methods），来搭建该推荐系统。首先，研究人员收集了课程大纲，并对每门课程进行标注（例如所教授的知识/技能、基础/进阶等级）。随后，利用课程标注信息以及学生过往的课程选择与成绩数据，训练了五种推荐模型。为验证该系统的推荐准确性，本研究以电气与计算机工程系的学生为对象开展实验，该系共开设1794门课程，服务925名学生；实验采用受试者工作特征曲线（Receiver Operating Characteristic Curve，ROC）与归一化折损累计增益（Normalized Discounted Cumulative Gain，NDCG）作为评价指标。实验结果显示，所提出的系统在ROC指标上可达80%的准确率，在NDCG指标上可达90%的准确率。本研究邀请了46名参与者对该系统进行测试并完成问卷调研。总体而言，60%至70%的参与者对系统推荐的课程表现出兴趣；其中，基于内容的过滤所生成的课程推荐列表，与67.40%的学生实际课程偏好相符。本研究还发现，学生对推荐列表顶部的课程兴趣更高；相较于外在信息型动机，采用五种推荐方法时，更多学生表现出自主学习动机。上述研究结果表明，本研究所提出的课程推荐系统能够帮助学生选择最符合自身兴趣的课程。

搜集汇总

背景与挑战

背景概述

该数据集基于一项研究，构建了一个个性化的混合课程推荐系统（PHCRS），旨在帮助学生根据兴趣、能力和职业发展选择课程。系统采用五种算法（如内容过滤和协同过滤），利用课程标签和学生历史数据训练，在电气和计算机工程系（涉及1794门课程和925名学生）中实现高准确率（ROC 80%，NDCG 90%），并通过用户测试验证了其有效性，显示多数学生对推荐课程感兴趣且偏好推荐列表顶部的选项。