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With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes.…
StreamBED is an embodied VR training for citizen scientists to make qualitative stream assessments. Early findings garnered positive feedback about training qualitative assessment using a virtual representation of different stream spaces,…
Early exposure to Computer Science (CS) for all is critical to broaden participation and promote equity in the field. But how does introducting CS into primary school curricula impact learning, perception, and gaps between groups of…
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tuning (VPT), prepending learnable prompts to input space,…
In most VR experiences, the visual sense dominates other modes of sensory input, encouraging non-visual senses to respond as if the visual were real. The simulated visual world thus becomes a sort of felt actuality, where the 'actual'…
Video Large Language Models (Video LLMs) have achieved significant success by adopting the paradigm of large-scale pre-training followed by supervised fine-tuning (SFT). However, existing approaches struggle with temporal reasoning due to…
Humans navigate and understand complex visual environments by subconsciously quantifying what they see, a process known as visual enumeration. However, traditional studies using flat screens fail to capture the cognitive dynamics of this…
Preference-based reinforcement learning (PbRL) is emerging as a promising approach to teaching robots through human comparative feedback, sidestepping the need for complex reward engineering. However, the substantial volume of feedback…
Conventional visuomotor imitation learning usually predicts future robot actions directly in the time domain. Such formulations often have limited physical scene awareness and weak long-horizon memory. In contrast, world-model-based…
Academic procrastination is a persistent challenge in computing education, yet evidence on the effectiveness of course-level interventions remains fragmented across diverse designs and contexts. We present a systematic literature review of…
Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective:…
Although the use of technologies like multimedia and virtual reality (VR) in training offer the promise of improved learning, these richer and potentially more engaging materials do not consistently produce superior learning outcomes.…
Training students in basic concepts of physics, such as the ones related to mass, volume, or density, is much more complicated than just stating the underlying definitions and laws. One of the reasons for this is that most students have…
Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of…
Class-Incremental Learning (CIL) [40] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars…
Virtual reality is the way of the future. The use of virtual reality is expanding over time across all sectors, from the entertainment industry to the military and space. VREd is a similar concept where a virtual reality-based classroom is…
Recall impairment in a different environmental context from learning is called context-dependent forgetting. Two learning methods have been proposed to prevent context-dependent forgetting: reinstatement and decontextualization.…
Campus well-being underpins academic success, yet many universities lack effective methods for monitoring satisfaction and detecting mental health risks. This dissertation addresses these gaps through prevention (improving feedback…
Recently, masked image modeling (MIM) has become a promising direction for visual pre-training. In the context of vision transformers, MIM learns effective visual representation by aligning the token-level features with a pre-defined space…
Parameter-Efficient Fine-Tuning (PEFT) has emerged to mitigate the computational demands of large-scale models. Within computer vision, adapter-based PEFT methods are often favored over prompt-based approaches like Visual Prompt Tuning…