  Natural Language Processing (NLP) has become increasingly utilized to provide
adaptivity in educational applications. However, recent research has
highlighted a variety of biases in pre-trained language models. While existing
studies investigate bias in different domains, they are limited in addressing
fine-grained analysis on educational and multilingual corpora. In this work, we
analyze bias across text and through multiple architectures on a corpus of
9,165 German peer-reviews collected from university students over five years.
Notably, our corpus includes labels such as helpfulness, quality, and critical
aspect ratings from the peer-review recipient as well as demographic
attributes. We conduct a Word Embedding Association Test (WEAT) analysis on (1)
our collected corpus in connection with the clustered labels, (2) the most
common pre-trained German language models (T5, BERT, and GPT-2) and GloVe
embeddings, and (3) the language models after fine-tuning on our collected
data-set. In contrast to our initial expectations, we found that our collected
corpus does not reveal many biases in the co-occurrence analysis or in the
GloVe embeddings. However, the pre-trained German language models find
substantial conceptual, racial, and gender bias and have significant changes in
bias across conceptual and racial axes during fine-tuning on the peer-review
data. With our research, we aim to contribute to the fourth UN sustainability
goal (quality education) with a novel dataset, an understanding of biases in
natural language education data, and the potential harms of not counteracting
biases in language models for educational tasks.
