The study aims to create a comprehensive panel study infrastructure on the implementation of teacher education in Norway that traces both teacher education students, study programs, and institutions over time.
The main goal of TEPS is to create a comprehensive panel study infrastructure on the implementation of teacher education in Norway that
- traces teacher students' educational trajectories and subsequent career paths
- allows for the comparison of different teacher education programs and institutions over time.
More about the project
Purpose of the project
TEPS has two key purposes: to serve as a database for high-quality research on teacher education and to serve as an evidence base for the quality management and improvement of teacher education in Norway. For both purposes, it is essential to use systematic data that paints a holistic picture of teacher education and reflects the increasingly complex job demands of the teaching profession.
Achieving this holistic panel study is ensured through a modular design. This design makes it possible to:
- put tailored expert teams in charge
- construct and optimize different parts in parallel
- give users customized access to parts of the panel
- flexibly connect to in-depth research projects
The 10 modules are at teacher/course or student level, are conducted every semester or only once per student, involve different data sources (text analysis, questionnaire and log file) and allow both more open and restricted data access.
Modules at the student level
- Study entry: Teacher education students will be asked among others about their study choice, demographic background, and teaching-related beliefs and attitudes in their first semester in a questionnaire.
- Study profile: Logfile data on students’ study choices, course participation, and grades will be obtained from digital tools once per semester.
- Study completion: Once students reach the 10th semester or exmatriculate, they receive a questionnaire about among others career ambitions, study evaluations, and teaching-related beliefs and attitudes.
- Master theses: The students’ master theses will be categorized regarding their topics, data collection, and methods using text coding by a large language model.
- Follow-up: A few years after the students leave teacher education, they will receive a questionnaire on among others their career path, their retrospective study evaluation, and teaching-related beliefs and attitudes.
Modules at the course/teacher level
- Course content: The online course descriptions of teacher education courses will be coded regarding their topics, teaching methods, and assessment methods using a large language model.
- Student evaluation: After each course, students will fill out questionnaires on their satisfaction with the course, their subjective learning, and workload, among others.
- Teacher evaluation: After each course, the teachers will also fill out questionnaires on their satisfaction with the course, the methods they used, how the course was implemented, and about their workload.
- Digital platforms: From digital learning and information tools, logfile data on the teacher and student activity, the use of implemented functionalities, and the course reading lists will be obtained.
- Teacher attributes: Once per year, the teachers in teacher education will respond to a questionnaire about their demography, professional development, and job satisfaction, among others.
In all modules, quantitative data will be created, documented, stored and published. The panel study targets a wide variety of users, including researchers from Norway and other countries and from different disciplines.
The panel study delivers both cross-sectional and longitudinal data. It is designed to answer a plethora of research questions that are of societal and research importance, such as:
- How do different institutions implement the five-year teacher education program?
- How can recruitment to, completion of and transition from teacher education be improved given the growing teacher shortage?
- How can the perceived teaching quality of teacher education programs be compared and improved?
Current status
In 2023 and 2024, a number of (pre-)pilot projects were carried out to test the feasibility of TEPS. For example, in the master's thesis module, we piloted if artificial intelligence (AI) can be used to categorize master theses by topic, data and method. A team has used summaries of master theses published at OsloMet to test whether a large language model can code these texts as well as humans on the basis of a developed coding scheme. The findings suggest that the AI strategy is suitable for categorizing all teacher education master theses in TEPS in the long term.
The way forward
TEPS will apply for research funding from the Research Council of Norway (NFR) in the context of a larger infrastructure project on teacher education in Norway. The next project phase (2025-2027) therefore aims to demonstrate the feasibility of TEPS in further pilots and to further develop the project.
