Student Engagement
Student engagement, encompassing students' involvement, motivation, and attention in learning, is a key focus of educational research aiming to improve learning outcomes and retention. Current research utilizes diverse methods, including multimodal data analysis (combining visual, physiological, and textual data) with machine learning models like convolutional neural networks and transformers, and natural language processing for sentiment analysis of student feedback. These advancements enable more accurate and nuanced measurement of engagement, informing data-driven pedagogical decisions and personalized interventions to enhance the learning experience and address issues like student withdrawal.
Papers
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
Zane Durante, Robathan Harries, Edward Vendrow, Zelun Luo, Yuta Kyuragi, Kazuki Kozuka, Li Fei-Fei, Ehsan Adeli
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
Shiqi Liu, Sannyuya Liu, Lele Sha, Zijie Zeng, Dragan Gasevic, Zhi Liu