1) Assessment and Evaluation of science learning processes - this strand focuses on the development of instruments to measure variables related to students' learning as e.g., motivation, interest, engagement, but also conceptual knowledge
2) Assessment, Evaluation, Accountability and Educational Improvement - this strand focuses on large scale studies aimed to evaluate students' learning and teachers' competences in order to help school institutions to design more effective learning environments
3) Data Science for Learning Processes and Education - this strand focuses on the development of technology-based environments, algorithms and techniques to support educational researchers in the process of understanding students' learning mechanisms
4) Methods and Methodological Aspects in Science Education Research - this strand focuses on methods of analysis that can be especially fruitful for science education researchers, as e.g., the Rasch model
5) Motivation, Engagement, and Metacognition - this strand focuses on the development of instruments to measure students' affective variables at all educational levels students', including e.g., metacognitive skills
6) Network Analysis in Education and Learning Processes - this strand focuses on methodological aspects about the structure of social relations and associations that may be expressed in network form in the educational context.
7) Science and Technology Disciplinary Identities - this strand analyes the development quantitative instruments to measure students' discipline identity, as physics identity, engineering identity with focus on gender issues
8) Teaching Processes and Teacher Education - this strand focuses on quantitative studies to measure teachers-related variables as Self-Efficacy or Pedagogical Content Knowledge
9) University Education - this strand focuses on quantitative, large-scale and cross-cultural studies that involve university students, instructors, and curricula
10) Visualization of Learning Processes - this strand focuses on developing methods to make visible learning and assessment processes and provide insights from the large amount of data nowadays available
