Guanliang Chen

TL;DR: This article provides a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques and classifies state-of-the-art studies into two principal branches: review-based user profile building and review- based product profile building.

TL;DR: A personalized feedback system is developed that facilitates social comparison with previously successful learners based on an interactive visualization of multiple behavioral indicators and finds the availability of social comparison cues significantly increases completion rates in MOOCs.

TL;DR: This research found large-scale experiments to be the most challenging environment for experimentation due to their size, heterogeneity of participants, and platform restrictions, and identified the three most promising strategies for effectively leveraging learning at scale as Cooperative Learning, Simulations a Gaming, and Interactive Multimedia.

TL;DR: This article conducted a province-wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China and performed cross-tabulation and Chi-square analysis to compare students' online learning conditions, experiences, and expectations.

TL;DR: In this article , the authors present a framework, referred to as XAI-ED, that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools.