Even if it tends to change, women remain still underrepresented in STEM jobs and education due to persistent gender stereotypes. The former Gender4STEM Erasmus+ project aimed at tackling this issue and therefore, developed a hands-on training combined with a digital assistant platform for teachers, to provide them with concrete tools for more gender-fair teaching. From 2017 to 2020, its blended approach reached more than 3000 participants (e.g. teachers, pupils, experts…) and confirmed that fairness in teaching is becoming a real competence among school education that needs to be accompanied with an advanced approach.
Building on the Gender4STEM experiment, the FIT project aims at developing an advanced approach to stepping up fair teaching practices especially in STEM disciplines, given consequently equal opportunities and access to STEM among diverse range of pupils.
To do so, the project has the ambition to extend the competency framework of fairness in teaching from gender to intersectional approach by observing a continuum between primary and secondary School level and developing its associated assessment questionnaires and training sessions. In order to ensure the large accessibility of the digital assistant platform, it will also strengthen its Multilanguage dimension, extend its contents, develop algorithms providing recommendations of fair teaching materials as well as its interactive features. Finally, this follow-up project aims at developing a FIT community of practice as a pillar to guarantee the large transferability of the FIT advanced approach.
In this context and in close collaboration with their partners, LIST researchers will co-develop the competency framework and co-design the associated assessment tools (e.g., judgement situational approach integrated in a training tool). They will also manage the technological developments required to enhance the transferability of the approach and the transparency of the recommendation algorithms, as well as to personalise the pathways of the platform’s users.
FIT will answer the digital transformation upskilling challenges, proposing innovative digitally enhanced learning and assessment environments. It will thus provide inclusive technologies and explore novel interaction techniques to support situation awareness and training. Moreover, this innovative project will answer the challenge of explainable AI by making the recommendation algorithms more transparent.
Finally, the FIT project expects to trigger a real process of change regarding fairness in teaching among primary and secondary level education so that more diverse range of pupils will equally access to STEM opportunities.