Teacher Insights: Chocolate, the Right Food to Improve Your Brain Power  |  Leadership Instincts: Strong Bricks Can Be Made from Bio solids and Clay  |  Parent Interventions: Order of Birth in Family Has Influence on Intelligence  |  Cover Story: MIND THE NET  |  Technology Inceptions: Oppo’s 10X Lossless Hybrid Zoom Smartphone Camera Tech to Enter Mass Production   |  Technology Inceptions: AI Can Help Improve Understanding of Earth Science  |  Cover Story: THE CYBER BRAIN  |  Science Innovations: New treatment for osteoporosis   |  Technology Inceptions: SpaceX Protests NASA Launch Contract Award  |  Science Innovations: Cost-efficient catalysts  |  Technology Inceptions: NASA to Launch New Space Telescope in 2023 to Explore Origins of Universe  |  Leadership Instincts: Social Media Cannot Cause Depression  |  Parent Interventions: Maternal Grandmothers Can Raise Survival Rate of Grandchildren  |  Teacher Insights: Waking Up Early No Guarantee for Success  |  Teacher Insights: Ask your girl child to do science, not become scientist  |  
  • Pallikkutam Magazine
  • Companion Magazine
  • Mentor
  • Smart Board

February 08, 2019 Friday 02:57:28 PM IST
Big Data May Help Evaluate Teacher Performances

Often there is much talk of improving student grades and performances in schools and colleges. However, we still follow an outdated method of assessing teacher performances. A new reliable method of evaluating teacher quality based on principles of econometrics is being developed by Jiaying Gu, Assistant Professor of University of Toronto, Department of Economics.

The current evaluation uses the linear shrinkage principle that assumes that distribution of quality follows a bell curve. According to Jiaying Gu, a recipient of 2018 Polanyi Prize in Economic Science for her novel statistical methods in evaluating teachers, the data set in such a system is too diverse to be analysed one way. The differences in behaviour of individual teachers cannot be judged easily such as innate ability or personal preference. Big data may give quantities of nuanced information to tackle datasets that are difficult to observe using traditional methods. In evaluation based on student grades, the teaching style cannot be accurately factored in due to difficulty in observing it. Therefore, Gu is working on datasets in elementary schools in USA with the aim of accurately evaluating the quality of teachers.

The research outcome will have far-reaching impact on educational policy and help improve our educational system at various levels.

Source: https://www.economics.utoronto.ca/index.php/index/index/newsDetails/175

Comments