Hafsa Mounir, Wattebled Pamela, Jacques Julie, Jourdan Laetitia
Mandarine Academy, Lille, France.
University of Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, Lille, France.
Data Brief. 2023 Feb 1;47:108942. doi: 10.1016/j.dib.2023.108942. eCollection 2023 Apr.
Mandarine Academy is an Ed-Tech company that specializes in innovative corporate training techniques such as personalized Massive Open Online Courses (MOOCs), web conferences, etc. With more than 550K users spread across 100 active e-learning platforms. The company creates online pedagogical content (videos, quizzes, documents, etc.) on daily basis to support the digitization of work environments and to keep up with current trends. Mandarine Academy provided us with access to Mooc.office365-training.com. A publicly available MOOC in both French and English versions to conduct research on recommender systems in online learning environments. Mandarine Academy collects user feedback using two types of ratings: Explicit (Like Button, Social share, Bookmarks), and Implicit (Watch Time, Page View). Unfortunately, explicit ratings are underutilized. Most users avoid the burden of stating their preferences explicitly. To address this, we shift our attention to implicit interactions, which generate more data that can be significant in some cases. Implicit Ratings are what constitute Mandarine Academy Recommender System (MARS) Dataset. We believe that the degree of viewing has an impact on the overall impression, for this reason, we applied changes to the implicit data and made a part of it similar to the explicit rating format found in other known datasets (e.g., Movielens). This paper presents two real-world dataset variations that consist of 89,000 explicit ratings and 276,000 implicit ratings. Data was collected starting early 2016 until late 2021. Chosen users had rated at least one item. To protect their privacy, sensitive information has been removed. To the best of our knowledge, this is the first publicly available real-world dataset of E-Learning recommendations in both French and English with mixed ratings (implicit and explicit), allowing the research community to focus on pre-and post-COVID-19 behavior in online learning.
柑橘学院是一家教育科技公司，专门从事创新的企业培训技术，如个性化大规模开放在线课程（MOOC）、网络会议等。该公司在100个活跃的在线学习平台上拥有超过55万用户。公司每天创建在线教学内容（视频、测验、文档等），以支持工作环境的数字化并跟上当前趋势。柑橘学院为我们提供了访问Mooc.office365-training.com的权限。这是一个提供法语和英语版本的公开MOOC，用于在线学习环境中的推荐系统研究。柑橘学院使用两种评分方式收集用户反馈：显式评分（点赞按钮、社交分享、书签）和隐式评分（观看时长、页面浏览量）。不幸的是，显式评分未得到充分利用。大多数用户避免明确表达自己偏好的负担。为了解决这个问题，我们将注意力转移到隐式交互上，隐式交互会产生更多数据，在某些情况下可能很重要。隐式评分构成了柑橘学院推荐系统（MARS）数据集。我们认为观看程度会对整体印象产生影响，因此，我们对隐式数据进行了修改，使其一部分类似于其他已知数据集中的显式评分格式（例如，MovieLens）。本文展示了两个真实世界的数据集变体，包括89000个显式评分和276000个隐式评分。数据收集时间从2016年初持续到2021年末。选定的用户至少对一项内容进行了评分。为保护他们的隐私，已删除敏感信息。据我们所知，这是第一个公开可用的、具有混合评分（隐式和显式）的法语和英语在线学习推荐真实世界数据集，使研究社区能够关注新冠疫情前后的在线学习行为。
