David vs. Goliath: comparing conventional machine learning and a large language model for assessing students' concept use in a physics problem.

Large language models have been shown to excel in many different tasks across disciplines and research sites. They provide novel opportunities to enhance educational research and instruction in different ways such as assessment. However, these methods have also been shown to have fundamental limitations. These relate, among others, to hallucinating knowledge, explainability of model decisions, and resource expenditure. As such, more conventional machine learning algorithms might be more convenient for specific research problems because they allow researchers more control over their research. Yet, the circumstances in which either conventional machine learning or large language models are preferable choices are not well understood. This study seeks to answer the question to what extent either conventional machine learning algorithms or a recently advanced large language model performs better in assessing students' concept use in a physics problem-solving task. We found that conventional machine learning algorithms in combination outperformed the large language model. Model decisions were then analyzed via closer examination of the models' classifications. We conclude that in specific contexts, conventional machine learning can supplement large language models, especially when labeled data is available.

The vaccinia-based Sementis Copenhagen Vector coronavirus disease 2019 vaccine induces broad and durable cellular and humoral immune responses.

The ongoing coronavirus disease 2019 (COVID-19) pandemic perpetuated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has highlighted the continued need for broadly protective vaccines that elicit robust and durable protection. Here, the vaccinia virus-based, replication-defective Sementis Copenhagen Vector (SCV) was used to develop a first-generation COVID-19 vaccine encoding the spike glycoprotein (SCV-S). Vaccination of mice rapidly induced polyfunctional CD8 T cells with cytotoxic activity and robust type 1 T helper-biased, spike-specific antibodies, which are significantly increased following a second vaccination, and contained neutralizing activity against the alpha and beta variants of concern. Longitudinal studies indicated that neutralizing antibody activity was maintained up to 9 months after vaccination in both young and middle-aged mice, with durable immune memory evident even in the presence of pre-existing vector immunity. Therefore, SCV-S vaccination has a positive immunogenicity profile, with potential to expand protection generated by current vaccines in a heterologous boost format and presents a solid basis for second-generation SCV-based COVID-19 vaccine candidates incorporating additional SARS-CoV-2 immunogens.