Personalised eLearning Recommendation system

eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are all major problems. The correct eLearning Recommendation System is necessary to tailor the course recommendation according to the user's needs. To create this model, dataset of the User Profile and User Rating is needed. The User Profile dataset is created by using the Calyxpod programme to collect student profiles. User requirements are available through these profiles. The dataset obtained by gathering student comments following course completion is in the range of 1 (lowest) to 5 (highest).

数字化时代，电子学习（eLearning）已遍及全球各个角落。受COVID-19疫情影响，电子学习的价值大幅提升。在电子学习推荐系统中，信息过载、个性化建议、数据稀疏性与推荐精度均为核心难题。为实现基于用户需求的定制化课程推荐，亟需构建适配的电子学习推荐系统。为搭建该模型，需用到用户画像（User Profile）与用户评分两类数据集。其中用户画像数据集通过Calyxpod程序采集学生画像生成，此类画像可反映用户的学习需求。用户评分数据集则通过收集学生完成课程后的评价得到，评分区间为1（最低分）至5（最高分）。