Student mobility reflects academic transfer from one postsecondary institution to another and facilitates students’ educational goals of obtaining multiple credentials and/or advanced training in their field. This process often relies on transfer credit assessment, based on the similarity between learning outcomes, to determine what knowledge and skills were obtained at the sending institution as well as what knowledge and skills need to still be acquired at the receiving institution. As human evaluation can be both a challenging and time-consuming process, algorithms based on natural language processing can be a reliable tool for assessing transfer credit. In this article, we propose two novel datasets in the fields of Anatomy and Computer Science. Our aim is to probe the similarity between learning outcomes utilising pre-trained embedding models and compare their performance to human-annotated results. We found that ALBERT, MPNeT and DistilRoBERTa demonstrated the best ability to predict the similarity between pairs of learning outcomes. However, Davinci - a GPT-3 model which is expected to predict better results - is only able to provide a good qualitative explanation and not an accurate similarity score. The codes and datasets are available at https://github.com/JAkriti/New-Dataset-and-Performance-of-Embedding-Models.