DIGITAL LIBRARY
THE ATTITUDE OF STUDENTS FROM DIFFERENT CULTURAL BACKGROUNDS TOWARDS THE IDEAL TEACHER AT A SPECIALIZED UNIVERSITY
1 Lithuanian University of Health Sciences (LITHUANIA)
2 Kaunas University of Technology (LITHUANIA)
About this paper:
Appears in: ICERI2019 Proceedings
Publication year: 2019
Pages: 4457-4462
ISBN: 978-84-09-14755-7
ISSN: 2340-1095
doi: 10.21125/iceri.2019.1117
Conference name: 12th annual International Conference of Education, Research and Innovation
Dates: 11-13 November, 2019
Location: Seville, Spain
Abstract:
The results of the research (2018) revealed that students associate the vision of dreams studies with teachers and their educational activities in the first place: this was expressed by 70% of students in their mind maps. Therefore, it was decided to look at the image of the ideal teacher reflecting the expectations of the students in terms of creating an educational environment. The article attempts to answer the question of what aspects of a teacher's educational activity are assessed by students as the traits of an ideal teacher. Which of the teacher's educational competences, i.e. pedagogical, communicative or organizational, are the most important from the students' point of view when they create the image of an ideal teacher? What differences can be noticed in this respect when comparing mind maps of an 'Ideal Teacher' created by international students and local (i.e. Lithuanian) students. These questions are addressed through the participants of the research, i.e. the students of a specialized university who have come to study from other countries (hereinafter: international students) and the Lithuanian students of the same university.

The aim of the study is to identify the attitudes of the students from different cultural backgrounds towards an ideal teacher at a specialized university based on the students' mind maps.

As already noted in previous studies, the educational environment, where students' attitudes towards learning are influenced by their own cultural differences, must be characterized by special flexibility and unconventional pedagogical solutions, therefore, the lecturers at a specialized university working with the students from different cultures face various difficulties related to the expression of teachers' educational competence. Feedback is crucial in solving these problems; thus, it makes sense to find out what educational competencies are most desirable for students. According to Ramsden in his book Learning to Teach in Higher Education 'good teaching and good learning are combined with our students' perception of our activities. Hence, we will not be able to teach better if we do not look at what we do in the eyes of students'.

The article is based on theoretical positions that highlight constructivist attitudes.
Thus, an individual task to create a mind map of an 'Ideal Teacher' was given for the first year international students and Lithuanian students. The research method Mind Map was selected for the survey data collection as this form allows to collect data in the form of a summarized minimal text. The mind maps were created by 135 first year students at Lithuanian University of Health Sciences: 75 international students and 60 Lithuanian students. The study population is a targeted, user-friendly, and context-sensitive. A convenient random selection was applied by inviting volunteers to participate in the study. The data of the mind maps (N = 135) was processed using qualitative content analysis.

Conclusions:
The group of international student participants of the study the most characteristic features of the teacher are those which form part of the communicative educational competence of the teacher. However, the Lithuanian student participants emphasize the organizational and pedagogical parts of the teacher's educational competence. Both groups of the participants of the study emphasized the human qualities of the teacher such as patience and understanding.
Keywords:
ideal teacher, mind map, educational environment, learning environment, deep learning