LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education

Art appreciation is vital in nurturing critical thinking and emotional intelligence among learners. However, traditional art appreciation education has often been hindered by limited access to art resources, especially for disadvantaged students, and an imbalanced emphasis on STEM subjects in mainstream education. In response to these challenges, recent technological advancements have paved the way for innovative solutions. This study explores the application of multi-modal large language models (MLLMs) in art appreciation education, focusing on developing LLaVA-Docent, a model that leverages these advancements. Our approach involved a comprehensive literature review and consultations with experts in the field, leading to developing a robust data framework. Utilizing this framework, we generated a virtual dialogue dataset that was leveraged by GPT-4. This dataset was instrumental in training the MLLM, named LLaVA-Docent. Six researchers conducted quantitative and qualitative evaluations of LLaVA-Docent to assess its effectiveness, benchmarking it against the GPT-4 model in a few-shot setting. The evaluation process revealed distinct strengths and weaknesses of the LLaVA-Docent model. Our findings highlight the efficacy of LLaVA-Docent in enhancing the accessibility and engagement of art appreciation education. By harnessing the potential of MLLMs, this study makes a significant contribution to the field of art education, proposing a novel methodology that reimagines the way art appreciation is taught and experienced.

Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction

This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research has yet to exploit its potential for model optimization and has struggled to predict difficulty from unseen data. To address these problems, we propose a difficulty-centered contrastive learning method for KT models and a Large Language Model (LLM)-based framework for difficulty prediction. These innovative methods seek to improve the performance of KT models and provide accurate difficulty estimates for unseen data. Our ablation study demonstrates the efficacy of these techniques by demonstrating enhanced KT model performance. Nonetheless, the complex relationship between language and difficulty merits further investigation.

MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing

Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others have focused on enhancing the performance. Models that consider both interpretability and the performance improvement have been insufficient. Moreover, models that focus on performance improvements have not shown an overwhelming performance compared with existing models. In this study, we propose MonaCoBERT, which achieves the best performance on most benchmark datasets and has significant interpretability. MonaCoBERT uses a BERT-based architecture with monotonic convolutional multihead attention, which reflects forgetting behavior of the students and increases the representation power of the model. We can also increase the performance and interpretability using a classical test-theory-based (CTT-based) embedding strategy that considers the difficulty of the question. To determine why MonaCoBERT achieved the best performance and interpret the results quantitatively, we conducted ablation studies and additional analyses using Grad-CAM, UMAP, and various visualization techniques. The analysis results demonstrate that both attention components complement one another and that CTT-based embedding represents information on both global and local difficulties. We also demonstrate that our model represents the relationship between concepts.