Understanding student engagement in online education is crucial for optimizing learning outcomes. This paper introduces ECLIPSE dataset (Extended Classroom Learning Insights via Prolonged Student Engagement), comprising 10,110 annotated images from a 55-minutes , 30-minutes and 20-minutes online lecture. Annotations include four affective states: engagement, boredom, confusion, and frustration. ECLIPSE enables the investigation of learner attention dynamics over extended periods, overcoming the limitations of short-duration datasets. We establish benchmarks for ECLIPSE using models such as EfficientNet, Vision Transformer, Residual Attention Network, and GLAMOR-Net. We propose NeuralGaze, a novel framework integrating Neural Cellular Automata (NCA) with self-attention mechanisms, demonstrating superior accuracy in engagement level assessment compared to basic single-frame models. Furthermore, we introduce CG-SwT, a content-guided Swin Transformer model, which significantly outperforms the baseline ViT model on the ECLIPSE dataset (with F1-score improvements of 21.12%, 12.5%, 16.77%, and 15.41% for engagement, boredom, frustration, and confusion respectively). Our methods surpass existing single-frame engagement prediction baselines for both EngageNet and DAiSEE datasets by significant margins (7.4% and 6.2%, respectively). The code and dataset will be made publicly available.

The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine and assisted writing. The outcomes of this study enhance our understanding of how users interact with generative AI and have implications for improving the reliability of digital educational platforms.

Large ground-truth datasets and recent advances in deep learning techniques have been useful for layout detection. However, because of the restricted layout diversity of these datasets, training on them requires a sizable number of annotated instances, which is both expensive and time-consuming. As a result, differences between the source and target domains may significantly impact how well these models function. To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain. In this research, we introduced a synthetic document dataset called RanLayNet, enriched with automatically assigned labels denoting spatial positions, ranges, and types of layout elements. The primary aim of this endeavor is to develop a versatile dataset capable of training models with robustness and adaptability to diverse document formats. Through empirical experimentation, we demonstrate that a deep layout identification model trained on our dataset exhibits enhanced performance compared to a model trained solely on actual documents. Moreover, we conduct a comparative analysis by fine-tuning inference models using both PubLayNet and IIIT-AR-13K datasets on the Doclaynet dataset. Our findings emphasize that models enriched with our dataset are optimal for tasks such as achieving 0.398 and 0.588 mAP95 score in the scientific document domain for the TABLE class.