International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 14, No. 13, 2020 Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning Device https://doi.org/10.3991/ijim.v14i13.13027 Wan Mohd Amir Fazamin Wan Hamzah (), Hafiz Yusoff, Ismahafezi Ismail Universiti Sultan Zainal Abidin, Terengganu, Malaysia amirfazamin@unisza.edu.my Azliza Yacob TATI University College, Terengganu, Malaysia Abstract—Advances in technology have allowed mobile devices to be used for learning purpose. The use of tablets in mobile learning has the potential to enhance learning, contributing to increased motivation and knowledge acquisi- tion. However, without careful planning and support for learning content, stu- dents may not have the behavioural intention to use tablets in their learning. Pre- vious studies have focused on the use of learning applications installed in the tablets. There is a lack of research on students' behavioural intentions to use tab- lets in learning. The partial least squares regression approach and the Unified Theory of Acceptance and Use of Technology (UTAUT) were used in this study to explore students’ behavioural intention to use the tablet in learning. This study was conducted in a private school in Malaysia. A total of 170 participants were enrolled in this study. The results showed that most of the hypotheses of the study were not supported and further revealed that the construct of performance expec- tancy was the only determinant of students’ behavioural intentions to use the tab- let in learning. Keywords—Behavioural intentions, UTAUT, tablet, mobile learning. 1 Introduction The use of mobile technology in learning is growing with time [1]. The advances in mobile technology and development of wireless Internet access have led to the growth of mobile learning [2][3], which allows students to enjoy personalised learning on their mobile devices. Mobile learning using tablets has the potential to enhance learning, contributing to increased motivation and knowledge acquisition [4][5][6][7]. The study by Haßler [8] classified three categories of findings for tablet use in supporting mobile learning; pos- itive learning outcomes, no difference in learning outcomes and adverse learning out- comes. It was found that there was an excellent potential for researching tablet to be used in schools, especially as technology becomes more accessible and affordable [9]. iJIM ‒ Vol. 14, No. 13, 2020 161 https://doi.org/10.3991/ijim.v14i13.13027 Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… Most studies involving the use of tablets have previously focused on the use of learn- ing applications installed on the tablet [10][11][12][13]. There is a lack of research on students' behavioural intentions to use tablets as mobile learning devices in schools. Providing a learning app for mobile learning does not guarantee that students will use it in their learning. The purpose of this study is to explore students' behavioural inten- tions to use tablets in school. A research model based on the current theory of technol- ogy adoption, Unified Theory of Acceptance and Use of Technology (UTAUT) was developed with four research hypotheses proposed. The UTAUT theory is often used to clarify individual usage behaviours associated with information systems [12][13][14]. The research model developed was empirically tested using data collected from the students at a private secondary school in Malaysia. The results of this study can be used to evaluate the return on investment in tablet use in school learning. 2 Literature Review 2.1 Mobile learning Mobile learning is a specialised learning environment that leverages mobile technol- ogy through handheld devices and wireless networks. Mobile learning is a part of e- learning [17]. Other researchers have focused more on students and learning experi- ences, but the underlying principles remained the same [18][19]. Mobile learning is also a term commonly used to refer to the delivery of information that leads to learning. Students have been actively seen to gain knowledge when using mobile devices to in- teract with learning objects anytime and anywhere [20]. Various mobile devices have been used in learning such as smartphones, tablets and the Raspberry Pi [21][22][23][24][25]. Tablets are mobile devices that integrate multi- ple components and sensors in a device, typically with a built-in global touchscreen, camera and global positioning system (GPS). The popularity of tablets has spurred in- terest in apps in education, especially in schools. Tablets are used to support various learning activities in science learning activities such as water cycles [6], plant morphology [23][24] and dynamics the food chain [28]. For Social Studies activities, tablets are used to support financial and economic man- agement activities [29]. Whereas for Math subjects, tablets are used to support frac- tional-related activities [30]. Additionally, they are also used to support economic learn- ing activities [12] and historical learning [31]. 2.2 Behavioural intentions Previous studies described several models developed to evaluate users’ attitudes and intentions to adopt new technologies or information systems. These models include the Technology Acceptance Model (TAM) [32], the Theory of Planned Behaviour (TPB) [33], the Innovation Diffusion Theory (IDT) [34], and the Unified Theory Of Ac- ceptance and Use of Technology (UTAUT) [35]. 162 http://www.i-jim.org Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… The UTAUT theory combines eight models, namely the Theory of Reasoned Action, the Model of Personal Computer Utilisation, the Social Cognitive Theory, the Technol- ogy Acceptance Model, the Theory of Planned Behavior, the Innovation Diffusion The- ory, the Motivational Model, the combined Technology Acceptance Model and the Theory of Planned Behaviour. Venkatesh [35] explained that users’ intentions to use a system of technologies and consecutive consumption behaviours are influenced by four main variables; effort ex- pectancy, facilitating conditions, performance expectancy and social influence. The definition of effort expectancy refers to the simple level associated with the use of a system. The facilitating conditions makes it easy to represent that people believe that an organisation exists to maintain the use of systems. The term performance expectancy is the extent to which people are confident that using a given system will help them finding support in their performance. Social influence means that individuals are aware that other prestigious people need to use certain new information systems. By using the UTAUT model, researchers can understand whether or not the technology system meets users’ criteria and, in addition, reflects the technology's acceptance of users. Therefore, UTAUT can be considered an important theoretical factor in exploring students’ behavioural intentions to use the tablet in learning for this study. Figure 1 shows a research model developed based on the UTAUT [35]. Fig. 1. Behavioural intention model based on the UTAUT There are two variables in the research model developed in this study namely inde- pendent variables and dependent variables. Independent variables consisted of effort expectancy, facilitating conditions, performance expectancy and social influence while the independent variable comprised behavioural intention. The hypotheses of the pro- posed model are as follow: i) Effort expectancy will significantly and positively influence students’ behav- ioural intentions to use tablets in learning: Previous literature suggested that iJIM ‒ Vol. 14, No. 13, 2020 163 Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… the issue effort expectancy has been regarded as a significant factor in tech- nology acceptance [36]. Effort expectancy is relevant to the perceived ease of use in the technology acceptance model, which estimates that a system that is easier to use has more possibility to elicit the perception of usefulness and behavioural intention. Moreover, it has been displayed that effort expectancy is an important predictor of behaviour intention through applying the technol- ogy acceptance model [32] and the UTAUT [37]. The above studies supported the notion that effort expectancy is substantial to technology use, and it seems that students with high effort expectancy are more likely to use tablet in their learning. ii) Facilitating conditions will significantly and positively influence students’ be- havioural intentions to use tablets in learning:The construct of facilitating con- ditions refers to specific components in the setting that smoothen the system usage to carry out a particular duty [40]. This construct is not similar like other conditions. Explicitly, facilitating conditions consist of resource determinants and technological factors [41]. It is reported that users will not have full aspi- rations to use a system if they do not have sufficient training or have problems with the mismatch. Therefore, facilitating conditions need to be considered in providing adequate training and support. In this study, facilitating conditions had made it easier for students to think that organisations are supportive re- garding the use of tablets in learning. iii) Performance expectancy will significantly and positively influence students’ behavioural intentions to use tablets in learning: Previous studies concerning the acceptance of technology have pointed out that performance expectancy of technology plays an important role in users’ behavioural intention [36]. Re- sults from Venkatesh [35] showed that performance expectancy is a strong indicator of how to use technology. Besides, performance expectancy is rele- vance to influence students’ behavioural intentions to use tablets in learning. iv) Social influence will significantly and positively influence students’ behav- ioural intentions to use tablets in learning: Previous studies have shown that social influence plays a role in changing the intention to use technology [38]. Users tend to communicate more for explaining their use of information tech- nology [39]. Previous study had shown that when inventive technologies are high in uncertainty, people decide whether or not to adapt based on others' point of view [38]. In this study, social influence was referred to the important people for the students who think that they should or should not use the mobile tablet in learning. 3 Methodology 3.1 Sample and procedure This study was focused on exploring students' behavioural intentions using tablets in learning. The sample size was 170 students aged between 13 and 17 years old from 164 http://www.i-jim.org Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… a population of 203 students in a private secondary school. No female students were involved in this study because all students in this school were male. All students used IPAD tablets for learning in school. They have been using tablets for more than six months at the time of this study. Subsequently, students were given a questionnaire to explore their behavioural intentions. Table 1 shows the survey items used in this study. The survey was based on the UTAUT model focusing on students' behavioural inten- tions to use tablets in learning. Questionnaire used was 5-point Likert scales, where (1) strongly disagree, (2) disagree, (3) neither agree nor disagree, (4) agree and (5) strongly agree. The data were analysed according to the partial least squares regression (PLS). This approach was used to explore their behavioural intentions for using tablets in learn- ing. SmartPLS 3.0 software was used to compute and evaluate the data obtained. The results of this study were based on two models namely measurement and structural models. This study did not involve moderator variables in the UTAUT model such as gender, age, experience and voluntariness of use. All students have similar tablet usage experience based on their demographics. Table 1. The survey items Behavioural Intention BI1: I intend to use the tablet in the future. BI2: I predict that I would use the tablet in the future. BI3: I plan to use the tablet in the future. Effort Expectancy EE1: My interaction with the tablet is clear and understandable. EE2: It is easy for me to become skilful at using the tablet. EE3: I find the tablet easy to use. EE4: Learning to operate the tablet is easy for me. Facilitating Conditions FC1: I have the resources necessary to use the tablet. FC2: I have the knowledge necessary to use the tablet. FC3: A specific person (or group) is available for assistance with tablet difficul- ties. Performance Expectancy PE1: I find the tablet useful in my learning. PE2: Using the tablet enables me to learn quickly. PE3: Using the tablet increases my knowledge. PE4: If I use the tablet, I will increase my chances of getting good knowledge. Social Influence SI1: People who influence my behaviour think that I should use the tablet. SI2: People who are important to me think that I should use the tablet. SI3: The teacher of this subject has been helpful in the use of the tablet. SI4: In general, the teachers support the use of the tablet. 4 Result Measurement model was evaluated using item load, convergent validity, reliability of the measure and discriminant validity. Table 2 shows the item loadings for the meas- urement model. An item is considered reliable if its load is greater than 0.70 [42]. If research is about exploration, 0.4 or higher is acceptable [43]. iJIM ‒ Vol. 14, No. 13, 2020 165 Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… Table 2. The item loadings for the measurement model Construct Loading Standard Deviation T- Value BI1 <- Behavioural Intention 0.884 0.022 40.209 BI2 <- Behavioural Intention 0.935 0.020 47.206 BI3 <- Behavioural Intention 0.903 0.019 47.271 EE1 <- Effort Expectancy 0.821 0.035 23.732 EE2 <- Effort Expectancy 0.803 0.043 18.665 EE3 <- Effort Expectancy 0.777 0.061 12.848 EE4 <- Effort Expectancy 0.587 0.095 6.147 FC1 <- Facilitating Conditions 0.811 0.044 18.343 FC2 <- Facilitating Conditions 0.824 0.047 17.701 FC3 <- Facilitating Conditions 0.578 0.103 5.630 PE1 <- Performance Expectancy 0.839 0.033 25.772 PE2 <- Performance Expectancy 0.696 0.063 11.084 PE3 <- Performance Expectancy 0.860 0.030 28.702 PE4 <- Performance Expectancy 0.808 0.036 22.587 SI1 <- Social Influence 0.768 0.055 13.985 SI2 <- Social Influence 0.761 0.057 13.332 SI3 <- Social Influence 0.788 0.045 17.495 SI4 <- Social Influence 0.779 0.042 18.533 Table 3 displays the reliability of measurement and convergence validity. The relia- bility of the measurements was checked using composite reliability and Cronbach's Al- pha. In general, the minimum value of composite reliability is 0.7 and if it is an explor- atory research, 0.6 or higher is acceptable [44]. Hair [45] stated that the minimum Cronbach's alpha value is 0.6. Convergence validity was evaluated through the ex- tracted average variance, which should exceed the standard minimum of 0.5 [44][45]. Discriminant validity as shown in Table 4 was assessed using the square root of the extracted average variance and latent variable correlations. The average squared vari- ance character extracted from each construct should be above the correlation shared between one construct and the other construct in the model [46]. The results shown in Table 2, Table 3 and Table 4 indicated that the measurement model was acceptable since all values met the standard level of exploratory research. Table 3. The reliability of measures and convergent validity Construct Reliability of Measure Convergent Validity Composite Reliability Cronbach's Alpha Average Variance Ex- tracted (AVE) Behavioural Intention 0.933 0.893 0.824 Effort Expectancy 0.838 0.754 0.567 Facilitating Conditions 0.787 0.603 0.557 Performance Expectancy 0.878 0.814 0.645 Social Influence 0.857 0.778 0.600 166 http://www.i-jim.org Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… Table 4. The discriminant validity measures Construct Discriminant Validity Behavioural Intention Effort Expectancy Facilitating Conditions Performance Expectancy Social Influence Behavioural Intention 0.908 Effort Expectancy 0.504 0.753 Facilitating Conditions 0.504 0.568 0.746 Performance Expectancy 0.559 0.612 0.599 0.803 Social Influence 0.509 0.579 0.650 0.585 0.774 The hypotheses was validated using path coefficients and R2 values [42] based on the structural model. The ability of the model was evaluated using R2 to describe the variance in the variables. The path coefficients were used to evaluate the statistical sig- nificance of the hypotheses. Figure 2 illustrates the results of the structural model. This model explained 39% of the variation in behavioural intention. Four path coefficients were also shown in Figure 2. First, the path coefficient between effort expectancy and behavioural intentions was 0.157, p>0.05, indicating that effort expectancy did not have any positive and significant influence on behavioural intentions. Second, the coefficient of correlation between facilitating condition and behavioural intentions was 0.140, p>0.05, indicating that facilitating condition did not have any positive and significant influence on behavioural intentions. Third, the path coefficient between performance expectancy and behavioural intentions was 0.285, p<0.05, indicating that performance expectancy had a positive and significant influence on behavioural intentions. Fourth, the path coefficient between social influence and behavioural intentions was 0.160, p>0.05, showing that social influence did not have a positive and significant influence on behavioural intentions. These results demonstrated only one hypothesis that con- firmed the predictions, namely performance expectancy, while others rejected them; that are, effort expectancy, facilitating conditions and social influence. Fig. 2. The result of the analysis iJIM ‒ Vol. 14, No. 13, 2020 167 Paper—The Behavioural Intentions of Secondary School Students to Use Tablet as a Mobile Learning… 5 Discussion and Conclusion Based on previous studies, there are positive findings on the use of mobile devices in learning. The effectiveness of the use of mobile devices such as smartphones and tablets in learning is due to learning content, student-content interaction and teacher [47][48][49]. However, this study showed contradictory findings on tablet use in learn- ing. The results of this study showed that only the indicator of performance expectancy positively and significantly influenced students’ behavioural intentions to use tablets in learning. Other indicators namely effort expectancy, facilitating conditions and social influence did not influence students’ behavioural intention to use tablets for learning. In conclusion, this study has successfully explored the use of tablets in learning. Students’ behavioural intention indicated that they are not yet fully receptive to the use of tablets in learning. The lack of learning content, non-interactive learning applications and their level of readiness to use technology in learning also contributed to the negative findings of the tablet use in learning. Strategic planning needs to be done in the future to optimise the use of technology in the education environment. Future studies should focus on methods to increase the use of the tablet in the mobile learning process. 6 Acknowledgement This research paper supported by Universiti Sultan Zainal Abidin (UniSZA) using FRGS Racer Fund, project number: RACER/1/2019/ICT01/UNISZA//1. Special Thanks to the Ministry of Higher Education Malaysia (MOHE) and Center for Research Excellence & Incubation management (CREIM) UniSZA for providing financial sup- port for the research. 7 References [1] Y. Mcfarlane, A. Triggs, P., & Wan, “Researching Mobile Learning - Interim Report to Becta Period: April - December 2007,” no. December 2007, pp. 1–30, 2008. [2] G. J. Hwang and C. C. 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Fornell and D. F. Larcker, “Evaluating structural model with unobserved variables and measurement errors,” J. Mark. Res., 1981. [47] M. Ebner, “Interactive lecturing by integrating mobile devices and micro-blogging in higher education,” J. Comput. Inf. Technol., 2009, https://doi.org/10.2498/cit.1001382. [48] “Curricular Use of the iPad 2 by a First-Year Undergraduate Learning Community,” Libr. Technol. Rep., 2012. [49] D. Nincarean, M. B. Alia, N. D. A. Halim, and M. H. A. Rahman, “Mobile Augmented Reality: The Potential for Education,” Procedia - Soc. Behav. Sci., 2013, https://doi.org/10. 1016/j.sbspro.2013.10.385 8 Authors Dr. Wan Mohd Amir Fazamin Wan Hamzah is a Senior Lecturer at the Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Malaysia. His research areas include learning analytics, e-learning, gamification, and machine learning. Associate Professor Dr. Mohd Hafiz Yusoff is an Associate Professor at the Fac- ulty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Malay- sia. He has worked for several years in the areas of e-learning system development and currently working on knowledge management system. (E-mail: hafizyusoff@ unisza.edu.my). Dr. Ismahafezi Ismail is a Senior Lecturer at the Faculty of Informatics and Com- puting, Universiti Sultan Zainal Abidin (UniSZA), Malaysia. His research focuses on computer games, computer animation, virtual reality, and augmented reality. (E-mail: ismahafezi@unisza.edu.my). Dr. Azliza Yacob is a lecturer and researcher at Terengganu Advanced Technical Institute University College (TATiUC). Her research interests include Computer pro- gramming, Quality control, education, and computer industry. Her main research con- centrates on Knowledge Management system. (E-mail: azliza@tatiuc.edu.my). Article submitted 2020-01-04. Resubmitted 2020-02-20. Final acceptance 2020-02-23. Final version pub- lished as submitted by the authors. iJIM ‒ Vol. 14, No. 13, 2020 171 https://doi.org/10.2498/cit.1001382 https://doi.org/10.1016/j.sbspro.2013.10.385 https://doi.org/10.1016/j.sbspro.2013.10.385 mailto:hafizyusoff@unisza.edu.my mailto:hafizyusoff@unisza.edu.my mailto:ismahafezi@unisza.edu.my mailto:azliza@tatiuc.edu.my