Aims, objectives/research question(s)

Primary question: What is the attitude of the teachers and students towards the introduction of mobile technology in educational institutions?

The research objectives as formulated for this study can be given as follows:

1. To gain insight into the impact of the introduction of mobile technologies to middle schools in the UAE in terms of educational outcomes.
2. To gain insight into the factors that influence positively or negatively the impact of the introduction of mobile technologies to the classroom.

Theoretical Framework

The Acceptance Model

This model has been used as an attempt at understanding the factors at play in making the involved people and system to accept any situation or phenomenon. The framework provided by this model has been found to be relevant to this situation where the success of the introduction of mobile technology and its acceptance in the learning environment (Alharbi & Drew, 2014) has to be evaluated. According to the acceptance model, in order for mobile technology to be accepted in the learning environment, there should be perceived usefulness and ease in use so that it can be seen as a benefit. The motivation and the behavioural intention of students and teachers are also necessary for realizing if mobile technology would be feasible (Martins et al. 2014). It is for this reason that the model has been selected for addressing the study of the attitude of teachers and students towards mobile technology.

The Venkatesh’s Model

            The Unified Theory of Acceptance and Use of Technology (UTAUT) model, also known as Venkatesh’s Model, is used in this research study for addressing the benefits and challenges as perceived by the teachers and students in the learning situation. This model has been reflected upon, in this study for understanding the ‘facilitating conditions’ that make the use and application of mobile technology to be effective for the learning of the students (Khechine et al. 2014). This model has been adopted for it reflects upon the expectancy that people have regarding the performance and the efforts that are been put by the learning environment for developing the learning efficacy of the students (Tinkler et al. 2015).

Technical Capital

            A third theoretical model is focused on the future impact of technology on the classroom situation, has also been selected to frame this study. This model will allow me to ascertain the long-term impacts of the technology on the teachers and students. In this regard, my selection of the theory of Technical Capital has been used for advocating the use of advanced technology in different areas of learning. This is to ensure that technology may percolate in the learning environment leading to better learning outcomes. The reason for the selection of this theory has been to drive the discussion in this research study to support the inclusiveness of mobile technology in the learning environment of the students for possible positive impacts (Piketty, 2014).

            While the three models discussed here focus on different areas of technology integration, it is considered that none of them covers the whole process. Therefore, I used the three of them to develop a conceptual and theoretical framework that allowed me to develop data collection instruments, as well as analyse and interpret the information that is collected. Furthermore, since these three models have been developed in occidental contexts, the use of empirical data collected in UAE to test them might result in the addition of new elements or explanations that were not considered when these theories were developed (e.g. the single-sex schools).

In order to achieve the first objective, I looked into the outcomes of the introduction of mobile technologies using the following research questions as a guideline: 1.1 Is there a change in the students’ attitudes and engagement towards technology before and after the introduction of mobile technologies to the classroom? 1.2 What are the student, teacher or school characteristics associated to these changes, if any? The quantitative instruments collected information to quantify the educational outcomes expected from the intervention: • Attitudes and disposition towards technology: engagement with technology and IT self-confidence and self-efficacy. • Sociodemographic information: age, gender, socioeconomic status, etc. • Learning context: perceived teacher support, school climate, parental support, use of technology at home (frequency and length). The information mentioned above was collected through a questionnaire designed for the project and was applied before and after the intervention (Fraillon, et al. 2014). The questionnaire items were based on theoretical concepts arising from the literature review and in most cases consisted of adaptations of previously tested instruments (Martin, et al. 2012). The intervention, consisted of the introduction of laptops to the classroom environment. My basic concern with respect to quantitative data analysis is related to the quantification of the most significant student parameters as it considers the different types of variables for the data analysis. So, I have taken the following steps: • Descriptive statistics of all the variables collected through the questionnaires. • Bi-variate association analyses between educational outcomes and i) sociodemographic variables, ii) students’ learning context. Specifically, correlations or mean differences will be estimated according to the level of measurement of the explanatory variables. • Multivariate regression analyses to test the same correlation but controlling for theoretical relevant variables. • The comparison of these associations before and after the intervention for a test and a control group. The data were collected from both a test and a control group for students. The school A belongs to the male test group and school B belongs to the male control group. Similarly, school C belongs to the female test group and school D belongs to the female control group. Information was collected from two 8-grade classrooms from each school. The total number of participant were 199 students. In this way, I am trying to investigate the attitudes of students before and after the implementation of mobile technology (i.e., laptops) in the middle school class environment (Nardi, 2018). All analyses were carried out using, SPSS.

Quantitative result Both the correlation and regression analyses suggest that there is a positive relationship between the Treatment variable (using a laptop) and Self-Confidence. That means that the introduction of computers in the classroom increased the students’ Self-Confidence in the Use of Technology. Gender and Availability also have a positive and significant relationship with Self-Confidence. In other words, boys are more Self-Confident than girls; and the more technology items were available at home, the higher the Self-Confidence of students. In terms of Self-Efficacy, there is a positive relationship between the Treatment variable (using a laptop) and Self-Efficacy. That is, the introduction of computers in the classroom increased the students’ Self-Efficacy in the Use of Technology. The details on the statistical models, as well as the interpretation and discussion of results will be included in the last part of the presentation.

References Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155. Khechine, H., Lakhal, S., Pascot, D., &Bytha, A. (2014). UTAUT model for blended learning: The role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning Objects, 10(1), 33-52. Martins, C., Oliveira, T., &Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13. Nardi, P.M., 2018. Doing survey research: A guide to quantitative methods. Routledge. Piketty, T. (2014). Capital in the 21st Century. Tinkler, J. E., Whittington, K. B., Ku, M. C., & Davies, A. R. (2015). Gender and venture capital decision-making: The effects of technical background and social capital on entrepreneurial evaluations. Social Science Research, 51, 1-16. Fraillon, J., Schulz, W., Friedman, T., Ainley, J., & Gebhardt, E. (Eds.) (forthcoming). International Computer and Information Literacy Study 2014 technical report. Amsterdam, the Netherlands: International Association for the Evaluation of Educational Achievement (IEA).