About us



Work With Us


Physics education: innovative methodologies/technologies

Physics education research looks at the effectiveness of different teaching strategies, the impact of different assessment methods, and the role of learning environments in student success. It also looks at how students learn different concepts, and how teachers can be better prepared to teach Physics. For example:

  • in active learning students don’t just listen passively to the teacher, but they discuss, write and do problem-solving activities. In an active learning environment, students can be asked to solve a physics problem in groups and then present their solutions to the class, which helps promote deeper understanding of the concept. Using small-group work, data collection, analysis, and simulations, improves social skills, problem-solving, and critical thinking;
  • in an open-ended lab, the teacher gives students a project without specifying the procedure. As a result, the students must determine the best strategy to achieve the goal. This approach gives students the freedom to be creative and think outside the box while still learning the fundamentals of the subject. It also helps them gain problem-solving skills and develop critical-thinking skills. The teacher in this case is more of a support rather than a guide;
  • in peer learning, student interaction is the crucial component to archive educational goals.

Physics education tries to answer questions related to all these teaching methods and learning strategies, observing and obtaining data from students learning in a classroom. The data can be collected by using questionnaires and selecting relevant information from the student's resume, and then analyzed in a statistical form:

  • descriptive statistics, by using measures like central tendency and distribution, draws conclusions based on known data and factually presents them;
  • inferential statistics, on the other hand, uses data that is unknown to draw conclusions. It uses methods like hypothesis testing and regression analysis to assess the validity of assumptions and make predictions.

Sito web: http://www.st2.fisi.polimi.it