
This dataset contains the ChatGPT-4 responses generated for the study “ChatGPT-4’s ability to describe kinematic graphs”. The dataset includes prompts and responses for 26 items from the Test of Understanding Graphs in Kinematics (TUG-K), with five independent iterations per item (130 responses in total). The files correspond to the data analysed in the thesis.
LLM, Physics education, ChatGPT, Accessibilty, Concept inventories, GPT-4, Kinematics graphs, Large language models, TUG-K
LLM, Physics education, ChatGPT, Accessibilty, Concept inventories, GPT-4, Kinematics graphs, Large language models, TUG-K
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
