Abstract:

In the last years there is an explosive growth of smartphone availability. Some educational authorities drew attention to the disturbance it creates to the learning process [1]. Previous reports described also positive contribution of this wave on high school and higher education learning [2]. In the last few years, we introduced technological tools in class in preservice teachers, mainly mobile applications [2, 3]. In this work we checked whether introduction of technology in class encourages preservice teachers to use technology in their futures class as in-service teachers. We studied what are the factors that motivate preservice students to introduce technology in their own class [4].

The study was performed in four classes belonging to two colleges of education with overall 62 participating students. The students were future mathematics teachers and the courses were in the mathematics discipline. The courses included extensive use of computerized mobile technology as on-line student response system (Socrative) and online mobile interactive mathematical tool used for teaching and learning (GeoGebra). We collected data from the students about their experience and future expectation about the use of technology in their own future classes using a questioner. The factors that were measured are related to the technology acceptance model (TAM) [5] and included the external variable – Previous Experience (PE), Perceived Usefulness (PU) and Behavioral Intention (BI).

We found that only about 52% of the students expect to use computerized tools in their future classes, additional 21% consider computerized tools as beneficial but do not expect to be using them in their own classes and 27% do not consider them beneficial and are not expecting to use them. We have performed phi test factor analysis following previous studies for motivation of using computerized tools in teaching environment [5]. We found that there is a significant correlation between the perceived usefulness (PU) and the future behavioral intention (BI) – phi factor = 0.63. There was no significant correlation to the use of computerized tools in previous classes to BI. We deduce that there are other additional factors that influence the motivation as reflected by the Behavioral Intention. Further studies to enhance the model are required.

References:
[1] Tindell, D. R. & Bohlander, R. W. (2012), The Use and Abuse of Cell Phones and Text Messaging in the Classroom: A Survey of College Students, College Teaching Volume 60, Issue 1.
[2] Ben-Av, R.& Barchilon Ben-Av, M. (2013), Physics Laboratory in Your Pocket, 5th International Conference on Education and New Learning Technologies (EDULEARN13), Barcelona, Spain.
[3] Barchilon Ben-Av, M. & Gurevich, I. (2017), 9th International Conference on Education and New Learning Technologies (EDULEARN13), Barcelona, Spain.
[4] Cullen, T. A., & Greene, B. A. (2011). Preservice teachers' beliefs, attitudes, and motivation about technology integration. Journal of Educational Computing Research, 45(1), 29-47.
[5] Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644-654.

Keywords:

m-learning, technology adoption, pre-service teachers.