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Combating Human Bias in Teaching and Learning

Primary supervisor

Guanliang Chen

Human bias, which refers to the differentiative notions, mindsets, and stereotypes that we may preconceive towards different groups of people, has been witnessed in a variety of settings in our daily life, including in teaching and learning. It has been reported that even the most experienced and well-intended teachers may hold biases that they are not aware of towards students. What is worse, such biases often affect teachers’ subsequent interaction with students, and further exacerbate the achievement gaps between different groups of students. On the other hand, students may also hold either conscious or unconscious biases towards teachers of different characteristics. For instance, a recent study conducted in UNSW Sydney showed that male teachers from English-speaking backgrounds, compared to their female counterparts from non-English-speaking backgrounds, were more likely to get a higher score on a student evaluation. There is no doubt that both teacher bias towards students and student bias toward teachers are detrimental to the effective development and implementation of learning and teaching practices.

Therefore, this project aims to (i) investigate the prevalence of human bias in teaching and learning; and (ii) develop effective tools to help instructors and students combat potential human bias at a large scale. Potential research questions in this project include but are not limited to:

  • To what extent do (unconscious) teacher bias manifest itself in the written feedback provided to students?
  • To what extent do (unconscious) student bias manifest itself in the evaluation provided to instructors?
  • How can Natural Language Processing techniques be used to avoid inconsistent (and potentially biased) written feedback provided to students?
  • How can Natural Language Processing techniques be used to avoid potential vicious comments in student evaluations for instructors?

Required knowledge

  • Strong programming skills (e.g., Python)
  • Basic knowledge in Data Science, Natural Language Processing, and Machine Learning
  • The following can be a plus: (i) prior experience in applying Deep Learning models; (ii) good at academic writing; and (iii) strong motivation in pursing quality academic publications.

Learn more about minimum entry requirements.