一句话：提出混合课程推荐系统 CRS，用数据挖掘、NLP 与推荐系统为软件工程学生和教师提供课程、职业、技能与学习资源建议。
随着大学选修课与 MOOCs 增多，软件工程学生难以选择学习路径，而产业技能快速变化也要求课程与教材频繁更新。本文提出混合课程推荐系统 CRS，整合数据挖掘、自然语言处理模型和推荐系统，构建用户端与管理端两个 Web 应用。系统提供个性化大学课程、职业、岗位、产业需求技能及相应在线学习材料建议，并为学生和讲师提供多种分析仪表盘。作者通过对软件工程学生和教师开展问卷调查，评估 CRS 的初步影响，结果证明该方法能有效应对上述选课与课程更新挑战。
With the increasing numbers of elective courses at universities and the Massive Open Online Courses (MOOCs), Software Engineering (SE) students are facing challenges in selecting their study paths in tech. On the other hand, the skills in SE-related fields have been changing significantly for the past decade, which requires more frequent updates to the curriculum and teaching materials. There is a strong demand for a better course guide and recommendation system to aid higher education in SE to keep up with the industry requirements. In this work, we incorporate data mining techniques, a natural language processing model, and a recommendation system in a web application that helps SE students and university faculty with those challenges. Our proposed hybrid Course Recommendation System (CRS) consists of two web applications (user and admin web apps) to provide multiple features, including user-specific suggestions for university courses, careers, jobs, industry-demanded skills together with online materials to learn those skills, and various analysis dashboards for both SE students and lecturers. We conduct a survey on SE students and faculty members to evaluate the initial impact of our CRS on the end users, which proved the effectiveness of our approach in addressing the mentioned issues. Demo: CRS User Web App and CRS Admin Web App.
DOI 原文 ·
@inproceedings{paperbot10969,
title = {CRS - A hybrid Course Recommendation System for Software Engineering Education},
author = {Nhi N.Y. Vo and Nam H. Vu and Tu A. Vu and Quang T. Vu and Bang D. Mach},
booktitle = {ICSE},
year = {2022},
doi = {10.1109/icse-seet55299.2022.9794145}
}