Psychometrics and “Big Data” Mined from Our Grade-Books to Improve Our Teaching and Address Students’ Needs

Research output: Contribution to conferencePresentationpeer-review

Abstract

Over a century since Psychometrics emerged in Psychology, Big Data has risen as a tool for examining vast online data. Educators can effectively blend traditional and new methods to benefit students. By gathering and evaluating various classroom data, instructors can employ several techniques to improve teaching effectiveness and student learning. These techniques include identifying flawed test items using spreadsheets, determining effective assessment methods based on students with regular attendance and participation, and assessing pedagogical techniques enhancing learning, such as sharing vivid stories or integrating concepts across classes. Additionally, educators can analyze predictive models to pinpoint misconceptions, address them, and refine lessons. Quantifying patterns in class data can be used to emphasize the significance of attendance and minimizing distractions. Furthermore, utilizing clustering techniques can help identify student outliers and cater to their specific needs. Lastly, "big data" approaches can be applied to better comprehend and motivate high-potential underachievers, such as by discussing the Law of Diminished Return. These techniques become more reliable with larger class sizes, allowing for impactful education without compromising faculty efforts. By combining data analysis with genuine concern for students, educators can significantly enhance their learning experiences.
Original languageAmerican English
StatePublished - 2016
EventCalifornia State University Symposium on University Teaching - San Jose, United States
Duration: Oct 21 2016 → …

Conference

ConferenceCalifornia State University Symposium on University Teaching
Country/TerritoryUnited States
CitySan Jose
Period10/21/16 → …

Keywords

  • psychometric
  • testing & measurement
  • big data

Disciplines

  • Scholarship of Teaching and Learning
  • Data Science
  • Quantitative Psychology

Cite this