Disputation: John Mark Aiken
Doctoral candidate John Mark Aiken at the Department of Physics, Faculty of Mathematics and Natural Sciences, is defending the thesis "Understanding University Student Pathways Towards Graduation with Machine Learning and Institutional Data" for the degree of Philosophiae Doctor.
The PhD defence and trial lecture are fully digital and streamed using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the audience to ask ex auditorio questions either written or oral. This can be requested by clicking 'Participants' and then choose 'Raise hand'.
Sep. 24, 2020 3:00 PM,
"Artificial Intelligence, Machine Learning, and Statistical Analysis: An Introduction and Examination of How AI and ML are applied in Statistical Analysis"
A new framework for evaluating statistical models in physics education research (PER)
Across education there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due recently created very large data sets and machine learning. In physics education research (PER) this has recently been examined through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming classrooms via interactive engagement, demonstrated that students often move away from scientist-like attitudes due to science education, and has injected robust assessment into the physics classroom via concept inventories. This presentation examines the impact that machine learning will have on physics education research and presents a new framework for evaluating statistical models in PER. This paper then demonstrates the utility of this evaluation framework through simulations, analysis of survey data, and analysis of student pathways both in a physics major and across degree programs.
Contact information to Department: Line Trosterud Resvold