Text-to-Image (T2I) generation models have achieved significant advancements. Correspondingly, many automated methods emerge to evaluate the image-text alignment capabilities of generative models. However, the performance comparison among these automated methods is constrained by the limited scale of existing datasets. Additionally, existing datasets lack the capacity to assess the performance of automated methods at a fine-grained level. In this study, we contribute an EvalMuse-40K dataset, gathering 40K image-text pairs with fine-grained human annotations for image-text alignment-related tasks. In the construction process, we employ various strategies such as balanced prompt sampling and data re-annotation to ensure the diversity and reliability of our dataset. This allows us to comprehensively evaluate the performance of image-text alignment methods for T2I models. Based on this dataset, we introduce an efficient automated evaluation method termed FGA-BLIP2, which enables Fine-Grained Alignment evaluation solely by inputting images and text leveraging BLIP2, without visual question answering for each fine-grained element. Experimental results show the proposed FGA-BLIP2 efficiently achieves good performance on multiple image-text alignment datasets. Meanwhile, benefiting from the high efficiency and fine-grained evaluation capability of FGA-BLIP2, we apply it as a reward model to improve text-to-image models, which effectively enhances the image-text alignment ability of text-to-image models.

The rise of the metaverse as an educational platform has introduced new opportunities and challenges in understanding students' cognitive processes. Traditional learning analytics methods often fail to fully capture the dynamic patterns of cognitive presence and metacognitive activities in immersive virtual environments, particularly when students interact with an artificial intelligence (AI)-powered digital human. To address this gap, this study aims to develop a multimodal learning analytics (MMLA) approach to analyse cognitive presence and metacognition in Learningverse - a metaverse platform. To address the limitations of traditional methods, this research integrated eye-tracking data with dialogue text from the AI-powered digital human to provide a more comprehensive understanding of these patterns. A pilot study involving undergraduate students was conducted to collect data, revealing specific patterns of cognitive presence and metacognition. The findings suggest that the MMLA approach could offer deeper insights into students' learning behaviours, providing valuable implications for the design of educational tools and the development of more effective learning strategies in virtual environments.