Trial lecture - time and place
Adjudication committee
- 1st opponent: Professor Kajsa Yang Hansen, Department of Education and Special Education, University of Gothenburg, Sweden
- 2nd opponent: Associate Professor James E. Pustejovsky, School of Education, University of Wisconsin-Madison, USA
- Chair of committee: Associate Professor Åste Marie Mjelve Hagen, Department of Special Needs Education, University of Oslo, Norway
Chair of defence
Professor Rolf Vegar Olsen, CEMO-Centre for Educational Measurement, University of Oslo, Norway
Supervisors
- Professor Ronny Scherer, CEMO - Centre for Educational Measurement, University of Oslo, Norway.
- Professor Mike W. L.Cheung, National University Singapore
Summary
Systematic reviews and meta-analyses are crucial in advancing the knowledge base in educational sciences. However, with the increasing amount of literature available, identifying and extracting relevant evidence becomes time-consuming and resource-intensive.
Furthermore, the diversity of research techniques, study designs, and reporting practices in the primary literature creates new challenges for analyzing and synthesizing study results. Machine learning and two-stage individual participant data meta-analysis can help reviewers cope with the information overload by streamlining the identification and screening of studies and standardizing the synthesis of quantitative information from large-scale studies.
In this doctoral thesis, I explore how these methodological approaches assist reviewers in collecting, analyzing, and integrating information from large-scale data sources in two critical stages of a systematic review and meta-analysis: (a) screening and identifying studies and (b) analyzing and synthesizing study results