4 research outputs found
Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game
While open-ended self-explanations have been shown to promote robust learning
in multiple studies, they pose significant challenges to automated grading and
feedback in technology-enhanced learning, due to the unconstrained nature of
the students' input. Our work investigates whether recent advances in Large
Language Models, and in particular ChatGPT, can address this issue. Using
decimal exercises and student data from a prior study of the learning game
Decimal Point, with more than 5,000 open-ended self-explanation responses, we
investigate ChatGPT's capability in (1) solving the in-game exercises, (2)
determining the correctness of students' answers, and (3) providing meaningful
feedback to incorrect answers. Our results showed that ChatGPT can respond well
to conceptual questions, but struggled with decimal place values and number
line problems. In addition, it was able to accurately assess the correctness of
75% of the students' answers and generated generally high-quality feedback,
similar to human instructors. We conclude with a discussion of ChatGPT's
strengths and weaknesses and suggest several venues for extending its use cases
in digital teaching and learning.Comment: Be accepted as a Research Paper in 18th European Conference on
Technology Enhanced Learnin
Vibrational spectroscopic analysis and quantification of proteins in human blood plasma and serum
International audienc
Candidate-based proteomics in the search for biomarkers of cardiovascular disease
The key concept of proteomics (looking at many proteins at once) opens new avenues in the search for clinically useful biomarkers of disease, treatment response and ageing. As the number of proteins that can be detected in plasma or serum (the primary clinical diagnostic samples) increases towards 1000, a paradoxical decline has occurred in the number of new protein markers approved for diagnostic use in clinical laboratories. This review explores the limitations of current proteomics protein discovery platforms, and proposes an alternative approach, applicable to a range of biological/physiological problems, in which quantitative mass spectrometric methods developed for analytical chemistry are employed to measure limited sets of candidate markers in large sets of clinical samples. A set of 177 candidate biomarker proteins with reported associations to cardiovascular disease and stroke are presented as a starting point for such a ‘directed proteomics’ approach