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).

在数字化时代，电子学习（eLearning，亦称在线学习）已遍及全球各个角落。受新冠疫情（COVID-19）影响，电子学习的价值显著提升。在电子学习推荐系统领域，信息过载、个性化建议缺失、数据稀疏性与推荐准确性是四大核心问题。为满足用户需求，构建精准的电子学习推荐系统至关重要。构建此类模型需依赖用户画像（User Profile）与用户评分（User Rating）数据集。其中，用户画像数据集通过Calyxpod程序采集学生档案生成，用户需求可通过这些档案获取；课程结束后收集的学生评价数据集评分范围为1（最低）至5（最高）。