Higher education institutions (HEI) have widely considered implementing pedagogies that provide academic excellence. In recent years, the role of future educators has attracted attention as the learning criteria of Gen Z and Gen Alpha students have raised questions regarding how HEI would provide timely and precise feedback to educators. This study seeked to identify categories in which educators are subject to be assessed. Purposive sampling was implemented to acquire knowledge from individuals who are in the context of an educator's performance. 10 focus group sessions were students, department directors, professors and deans were carried out separately. Each session was analyzed using a text-mining software to determine critical categories to be considered in the assessment. These focal groups laid out categories such as the importance of verbal and direct feedback rather than the use of standardized surveys. The findings provide a robust structure to propose the F1 model for Performance Assessment (F1-MPA). This model runs on data-driven A.I. to visualize the current performance of educators on the go and delivers recommendations based on their scores on each category. The F1-MPA is also adaptable to other industries as it resembles a 360-degree feedback approach.

Authors:
Julián Nevárez Montes, Universidad de Monterrey, Mexico
Rafael Octavio Felix Verduzco, Universidad de Monterrey, Mexico
Isabel Alejandra González Caballero, Universidad de Monterrey, Mexico

About the Presenter(s)
Dr. Julián Nevárez is currently Director of Graduate Studies at Universidad de Monterrey in the Faculty of Education & Humanities. His research focuses on innovation, educational technologies and knowledge management.

Connect on Linkedin
https://www.linkedin.com/in/mrnevarez

See this presentation on the full schedule – Thursday Schedule

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