Abstract:

In contemporary education teachers look for tools which will help them create stimulating classes for their students. They often look for ideas and approaches which will enable students’ activity. Within the project “e-Schools: Establishing a System for Developing Digitally Mature Schools (pilot project)” we designed and tested initial 20 teaching scenarios which were created for subjects Biology, Chemistry, Mathematics and Physics for 7th and 8th grades of primary level of education and 1st and 2nd grades of secondary level of education. The total of 1508 students from 13 different schools participated in the research. Data was analysed by means of data mining techniques. Data mining approach is proven to be useful tool for knowledge extraction from large data sets. This research applies two data mining techniques: cluster analysis and decision trees. The results of clustering and the generation of the rules from decision tree model are based on students’ questionnaires. Various interesting rules are extracted and discussed. With respect to the gender, grade point average, class and school students attend, differences in the perception of the teaching scenarios were examined. The analysis of the clusters indicates strong relationship between students’ grade and perception of teaching. Students with higher grades gave higher scores to the teaching materials and teaching process. Students find teaching process which is based on created teaching scenarios to be comprehensible, logical and motivating as well as encouraging for them to actively participate in the class. Further, results show that students perceive such teaching as encouraging for self-learning, reflection and thinking about the subject. We find that one of the strongest arguments for the use of teaching scenarios in a teaching process is the result of conducted research where over 60% of students find that such teaching process has helped them see possible application of learned lessons in real life situations.

Keywords:

Teaching Scenarios, Student's Perception, Teaching Process, STEM, Active Learning, Data Mining.