Assessment of Learning: Fairness, Bias, and Reliability

Related lectures (32)

Page 3 of 4

Explores the significance of anonymity in avoiding judgment based on personal characteristics and the need to balance it with accountability.

Introduces the AI and FX practical course, focusing on weekly readings and discussions on ethical and legal aspects of AI.

Explores experimental design for measuring learning outcomes and discusses biases, group equivalence, and within-subjects designs.

Covers motivation, bias, stress, and learning routines in education, emphasizing the impact of social identity and unconscious biases.

Explores expectations of an international experience, culture shock, stereotypes, and biases.

Explores the reasons behind our susceptibility to irrational beliefs and the impact of sociological and psychological factors on our beliefs.

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Explores Signal Detection Theory and its impact on decision-making processes in statistics and experimental design.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Discusses the multidimensional nature of teaching evaluations and offers tips for formulating effective questions.