Are primary students prepared to write in a digital world?
Peer-Reviewed Publication
Updates every hour. Last Updated: 3-Dec-2025 13:11 ET (3-Dec-2025 18:11 GMT/UTC)
A nation-wide study on computer-based writing instruction in Australian classrooms, led by researchers from Edith Cowan University (ECU) in collaboration with colleagues from the Writing for All research group, has shined a spotlight on how little time and attention primary schools are giving to teaching students how to write using a computer.
A programme of smell screening, awareness campaigns and health policies could improve the mental and physical health of millions – according to research led by the University of East Anglia.
Smell dysfunction is present in more than 130 neurological, somatic and hereditary disorders, with some evidence suggesting a causal role.
But a new paper published today reveals how smell is overlooked as a “Cinderella sense” in public health.
And researchers are calling for a worldwide campaign to put smell health on the map - with screening, education and awareness at its core.
A new national report has shown for the first time how generative AI (GenAI) is already being used by some universities to assess the quality of their research – and it could be scaled up to help all higher education institutions (HEIs) save huge amounts of time and money.
While originally created as a way to help people stop smoking, a UBC Okanagan researcher is raising concerns about oral nicotine pouches being portrayed as trendy and pleasurable, especially among young people.
Dr. Laura Struik, Associate Professor in UBCO’s School of Nursing, recently published a study examining how the social media platform TikTok appears to promote nicotine pouches, particularly the brand Zyn, as a lifestyle rather than a way to quit smoking.
With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.