There has been a surge in experience sampling, ecological momentary assessment, diary, and multimodal research studies, in which students and teachers are followed intensively within a relatively short time-window. Technology assisted data-collection techniques such as electronic self-report questionnaires, facial emotion recognition algorithms, and wearable technology for physiological measurement enable researchers to collect detailed situation-specific data. Such data can be analysed using multilevel and dynamic structural equation models, using the Bayesian estimator. Going beyond many studies to date, I in this talk focus on the importance of multiple-reporter data (student-reports, teacher-reports and observations) and linkages with objective data (situational executive functioning). I will illustrate the talk with key findings from a range of intraindividual studies, and interpret these within a frame of personalized (individualized) learning.

This seminar is part of the Child Development and Learning (CDL) Seminar Series.

Join in-person or on Teams: teams.microsoft.com/meet/3799219398382?p=2e2iFubdvLDs8dvPmG