Representing Federal Minister Hubertus Heil, State Secretary Nermin Fazlic awarded prizes in Berlin to a total of 27 ideas in three thematic areas. StereOFF was among the winners in the category Teaching and Knowledge. Lorena Göritz and Daniel Stattkus accepted the award on behalf of the Smart Enterprise Engineering research department of DFKI, as did Jannick Eckle for Didactic Innovations.
The European Commission identifies gender stereotypes as one of the main causes of gender inequalities, as stereotypical roles and norms limit the choices and freedoms of girls and boys or men and women, according to the submitted outline of the idea. StereOFF strives for gender equality in education and training. "Reducing, for example, often stereotypical male portrayals in MINT learning content aims to inspire more girls and women to enter MINT fields," says DFKI scientist Lorena Göritz. The idea is therefore particularly interesting for training and further education institutes as well as textbook publishers.
Gender Bias Lexicon
The basis of the text analyses are publicly available so-called text corpora, such as film transcripts, which show gender bias, i.e. distortions that are shaped by gender-related stereotyping or prejudices. From this data, the researchers develop a gender bias lexicon using data-driven Natural Language Processing techniques. On this basis, publishers, for example, can then automatically identify gender-stereotypical role models in textbooks or social media agencies can examine large quantities of published texts for stereotypical language in real time.
StereOFF not only analyses the learning materials, but also recommends alternative suggestions. The aim is to present sectors such as the MINT field or the profession of educators in a more balanced way, to facilitate integration, to promote diversity and thus to counter the shortage of skilled workers. The software developed within StereOFF will be made publicly available in the future.
