Sign language processing technology development relies on extensive and reliable datasets, instructions, and ethical guidelines. We present a comprehensive Azerbaijani Sign Language Dataset (AzSLD) collected from diverse sign language users and linguistic parameters to facilitate advancements in sign recognition and translation systems and support the local sign language community. The dataset was created within the framework of a vision-based AzSL translation project. This study introduces the dataset as a summary of the fingerspelling alphabet and sentence- and word-level sign language datasets. The dataset was collected from signers of different ages, genders, and signing styles, with videos recorded from two camera angles to capture each sign in full detail. This approach ensures robust training and evaluation of gesture recognition models. AzSLD contains 30,000 videos, each carefully annotated with accurate sign labels and corresponding linguistic translations. The dataset is accompanied by technical documentation and source code to facilitate its use in training and testing. This dataset offers a valuable resource of labeled data for researchers and developers working on sign language recognition, translation, or synthesis. Ethical guidelines were strictly followed throughout the project, with all participants providing informed consent for collecting, publishing, and using the data.

Robots have the potential to enhance teaching of advanced computer science topics, making abstract concepts more tangible and interactive. In this paper, we present Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment. We integrated a JavaScript-based application that is projected around the robot, which allows users to construct graphs and visualise three different shortest path algorithms with colour-coded edges and vertices. Animated graph exploration and traversal are augmented by robot movements. To evaluate Timmy, we conducted two user studies. An initial study (= 10) to explore the feasibility of this type of teaching where participants were just observing both robot-synced and the on-screen-only visualisations. And a pilot study (= 6) where participants actively interacted with the system, constructed graphs and selected desired algorithms. In both studies we investigated the preferences towards the system and not the teaching outcome. Initial findings suggest that robots offer an engaging tool for teaching advanced algorithmic concepts, but highlight the need for further methodological refinements and larger-scale studies to fully evaluate their effectiveness.

Objective: We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from clinical records. Methods: We used four core DS information extraction tasks--namely, named entity recognition (NER: 2,949 clinical sentences), relation extraction (RE: 4,892 sentences), triple extraction (TE: 2,949 sentences), and usage classification (UC: 2,460 sentences) as our multitasks. We introduced a novel Retrieval-Augmented Multi-task Information Extraction (RAMIE) Framework, including: 1) employed instruction fine-tuning techniques with task-specific prompts, 2) trained LLMs for multiple tasks with improved storage efficiency and lower training costs, and 3) incorporated retrieval augmentation generation (RAG) techniques by retrieving similar examples from the training set. We compared RAMIE's performance to LLMs with instruction fine-tuning alone and conducted an ablation study to assess the contributions of multi-task learning and RAG to improved multitasking performance. Results: With the aid of the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 (3.51% improvement) on the NER task and demonstrated outstanding performance on the RE task with an F1 score of 93.74 (1.15% improvement). For the TE task, Llama2-7B scored 79.45 (14.26% improvement), and MedAlpaca-7B achieved the highest F1 score of 93.45 (0.94% improvement) on the UC task. The ablation study revealed that while MTL increased efficiency with a slight trade-off in performance, RAG significantly boosted overall accuracy. Conclusion: This study presents a novel RAMIE framework that demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records. Our framework can potentially be applied to other domains.

Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicators into parsimonious multimodal ones. Using a high-fidelity healthcare simulation context, we collected positional, audio, and physiological data, deriving 17 monomodal indicators. LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement, each capturing unique monomodal patterns. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious while offering higher explanatory power regarding students' task and collaboration performances. The findings highlight the potential of LCA in simplifying the analysis of complex multimodal data while capturing nuanced, cross-modality behaviours, offering actionable insights for educators and enhancing the design of collaborative learning interventions. This study proposes a pathway for advancing MMLA, making it more parsimonious and manageable, and aligning with the principles of learner-centred education.

"Gold" and "ground truth" human-mediated labels have error. The effects of this error can escape commonly reported metrics of label quality or obscure questions of accuracy, bias, fairness, and usefulness during model evaluation. This study demonstrates methods for answering such questions even in the context of very low reliabilities from expert humans. We analyze human labels, GPT model ratings, and transformer encoder model annotations describing the quality of classroom teaching, an important, expensive, and currently only human task. We answer the question of whether such a task can be automated using two Large Language Model (LLM) architecture families--encoders and GPT decoders, using novel approaches to evaluating label quality across six dimensions: Concordance, Confidence, Validity, Bias, Fairness, and Helpfulness. First, we demonstrate that using standard metrics in the presence of poor labels can mask both label and model quality: the encoder family of models achieve state-of-the-art, even "super-human", results across all classroom annotation tasks. But not all these positive results remain after using more rigorous evaluation measures which reveal spurious correlations and nonrandom racial biases across models and humans. This study then expands these methods to estimate how model use would change to human label quality if models were used in a human-in-the-loop context, finding that the variance captured in GPT model labels would worsen reliabilities for humans influenced by these models. We identify areas where some LLMs, within the generalizability of the current data, could improve the quality of expensive human ratings of classroom instruction.

This paper proposes a novel analytical framework: Transition Network Analysis (TNA), an approach that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community finding to identify patterns of behavior, and clustering to reveal temporal patterns. This paper introduces the theoretical and mathematical foundations of TNA. To demonstrate the functionalities of TNA, we present a case study with students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA could reveal the regulatory processes and identify important events, temporal patterns and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. In doing so, we showcase TNA's utility to capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include advancing estimation methods, expanding reliability assessment, exploring longitudinal TNA, and comparing TNA networks using permutation tests.

In her quest to design socially assistive robots--robots that provide social, not physical, support in realms like rehabilitation, education, and therapy--she realized that personalizing interactions would boost both engagement and outcomes. Artificial Intelligence (AI) has made that easier, though as always, surprises are never far when human beings are involved. Here, Matarić shares what she's learned about meeting people where they are. Let's talk about your work on socially assistive robots. You've said that having kids inspired you to build robots that help people. How did that interest develop into the mission of supporting specific behavioral interventions in health, wellness, and education?

E. Allen Emerson was the first graduate student of Edmund M. Clarke at Harvard University. After discussing several ideas for Allen's dissertation, they identified a promising candidate: verifying a finite-state system against a formal specification. According to Martha Clarke, Edmund's widow, it was during a walk across Harvard Yard that they decided to call it "model checking." Emerson received his Ph.D. in applied mathematics for this work in 1981. Twenty-five years later, he and Clarke (along with Joseph Sifakis) shared the ACM A.M. Turing Award in 2007 for this and related work.

Designing user-centric mobile applications is increasingly essential in educational technology. However, platforms like MIT App Inventor-one of the world's largest educational app development tools-face inherent limitations in supporting modern UI/UX design. This study introduces the Figma-Enhanced App Design (FEAD) Method, a structured framework that integrates Figma's advanced design tools into MIT App Inventor using an identify-design-implement workflow. Leveraging principles such as the 8-point grid system and Gestalt laws of perception, the FEAD Method empowers users to address design gaps, creating visually appealing, functional, and accessible applications. A comparative evaluation revealed that 61.2% of participants perceived FEAD-enhanced designs as on par with professional apps, compared to just 8.2% for baseline designs. These findings highlight the potential of bridging design with development platforms to enhance app creation, offering a scalable framework for students to master both functional and aesthetic design principles and excel in shaping the future of user-centric technology.

The advancement of Large Vision Language Models (LVLMs) has significantly improved multimodal understanding, yet challenges remain in video reasoning tasks due to the scarcity of high-quality, large-scale datasets. Existing video question-answering (VideoQA) datasets often rely on costly manual annotations with insufficient granularity or automatic construction methods with redundant frame-by-frame analysis, limiting their scalability and effectiveness for complex reasoning. To address these challenges, we introduce VideoEspresso, a novel dataset that features VideoQA pairs preserving essential spatial details and temporal coherence, along with multimodal annotations of intermediate reasoning steps. Our construction pipeline employs a semantic-aware method to reduce redundancy, followed by generating QA pairs using GPT-4o. We further develop video Chain-of-Thought (CoT) annotations to enrich reasoning processes, guiding GPT-4o in extracting logical relationships from QA pairs and video content. To exploit the potential of high-quality VideoQA pairs, we propose a Hybrid LVLMs Collaboration framework, featuring a Frame Selector and a two-stage instruction fine-tuned reasoning LVLM. This framework adaptively selects core frames and performs CoT reasoning using multimodal evidence. Evaluated on our proposed benchmark with 14 tasks against 9 popular LVLMs, our method outperforms existing baselines on most tasks, demonstrating superior video reasoning capabilities. Our code and dataset will be released at: https://github.com/hshjerry/VideoEspresso