An Artificial Intelligence-Based Distance Education System: Artimat
Abstract
The purpose of this study is to evaluate the artificial intelligence-based distance education system called as ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed with 4 teachers and 59 students in 10th grade in an Anatolian High School in Trabzon. Many institutions and organizations in the world approach seriously to distance education besides traditional education. It is inevitable to use the distance education in teaching the problem solving skills in this different dimension of the education. In the studies in Turkey and abroad in the field of mathematics teaching, problem solving skills are generally stated not to be at the desired level and often expressed to have difficulty in teaching. For this reason, difficulties of the students in problem solving have initially been evaluated and the system has been prepared utilizing artificial intelligence algorithms according to the obtained results. In the evaluation of the findings obtained from the application, it has been concluded that the system is responsive to the needs of the students and is successful in general, but that conceptual changes should be made in order that students adapt to the system quickly.
Keywords
Distance Education, Problem Solving, Artificial Intelligence.
References
- Baykul, Y. (2004). Teaching Mathematics In Primary Education. Ankara: PegemA
- Ben-Hur, M. (2006). Concept-rich mathematics instruction: building a strong foundation for reasoning and problem solving, Association for supervision and curriculum development, Alexandria, VA.
- Cai, J. (2003). Singaporean student’s mathematical thinking in problem solving and problem posing: An exploratory study. International journal of mathematical education in science and technology, 34(5), 719-737. DOI: 10.1080/00207390310001595401
- Chiu, M., Robert M. Klassen R. M. (2008). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteenyear- olds in 34 countries, Science direct learning and instruction, 20(1), 2-17.
- Cohen, L. &Manion, L. (2000). Research methods in education. New York: Routledge.
- De Corte, E. (2004). Mainstreams and perspectives in research on learning
- (mathematics) from instruction, Applied psychology, 2(53), 279-310. DOI: 1111/j.1464-0597.2004.00172.x
- Glesne, C., & Peshkin, A. (1992). Becoming qualitative researchers: An introduction. White plains, NY: Longman.
- Inzunza, S. (2006). Students’ errors and difficulties for solving problems of sampling distributions by means of computer simulation, ICOTS-7.
- Kantar, M., Ibili, E., Bayram, F., Hakkari, F., & Doğan, M. (2008), Software and content creation on distance education systems, Conference proceedings, İstanbul University Press, 4793, 334-343.