Construction and psychometric analysis of teachers mobile learning acceptance questionnaire

Abstract

Purpose The mobile technology-pedagogy nexus has been instrumental in shifting the focus of information and communications technology in education from e-learning to mobile learning (ML). Learning with mobile technologies is more accessible, flexible, situated, personal, collaborative and lifelong. The use of technologies for educational purposes is the primary focus of higher education institutes. The successful implementation of ML rests on its acceptance by higher education teachers. It is, therefore, pertinent to determine the factors that impact higher education teachers’ acceptance of ML. This study aims to identify these factors and develop a reliable and valid instrument to measure higher education teachers’ acceptance of ML. Design/methodology/approach A sequential exploratory research design, a type of mixed method research was used for the study. A sound conceptual framework and rigorous scale development process provided the background for data collection and validation. Probability proportionate to size sampling technique was used to gather data from 212 higher education teachers from 42 higher education institutes. Teachers gave their responses on five-point Likert type items. The responses obtained were subjected to exploratory factor analysis, which provided a nine-factor solution. The factors were further validated through confirmatory factor analysis. Findings Teachers’ mobile learning (ML) acceptance questionnaire (TMLAQ), a 32-item questionnaire was developed to measure acceptance of ML among higher education teachers. A detailed literature review, interviews and focus group discussions with teachers facilitated the identification of nine constructs or antecedents of ML acceptance. These constructs were named as: perceived usefulness, ease of use, self-enhancement, constructivist belief (CB), technological barriers, attitude and behavioral intention. The scale possesses sound psychometric properties such as reliability, face validity, content validity and construct validity. Practical implications This instrument can serve as an authentic, valid and reliable measure of higher education teachers’ ML acceptance. It can be used by organizations to assess teachers’ perceptions and aid in the successful ML implementation. Originality/value There is a lack of measurement instrument that caters to wide angle view of teachers’ perception toward ML in the Indian context. This comprehensive scale will bridge this gap. Two new research constructs: CB and self-enhancement were found to be crucial from the teachers’ point of view. These constructs have not been explored in previous technology acceptance studies. To the best of the knowledge, such a comprehensive study has not been undertaken yet in the Indian context. This study can serve as a model for conducting similar kinds of studies in other developing nations.

Factors influencing university teachers’ use of a mobile technology-enhanced teaching (MTT) platform

“…In this study, multiple intention-based models were adopted to explore university teachers’ beliefs about various aspects of the use of mobile technology-enhanced teaching (MTT) platforms. Among the theoretical intention-based models, the technology acceptance model (TAM) is the most used theoretical model for predicting and explaining users’ intentions to use mobile learning systems (Hsu, 2016 ; Lai et al, 2016 ; Mittal & Alavi, 2020 ), including teachers’ intentions (Beteille et al, 2020 ). In the TAM framework, perceived usefulness (PU) and perceived ease of use (PEU) are two key determinants of a particular technology adoption, such as mobile learning systems.…”

“…Hsu ( 2016 ) used PU and PEU as the two determinants of mobile-assisted language learning use, while Lai et al ( 2016 ) adopted PEU as one predictive factor of high education teachers’ mobile learning environment use. Recently, Mittal and Alavi ( 2020 ) found that both PU and PEU were significant factors in the context of university teachers’ mobile learning acceptance.…”

Best practices in the use of mobile learning by university teachers of Didactics Language-Literature

“…The use of the m-learning method in education has been growing in recent years (MITTAL;ALAVI, 2020). This can be understood as the training process, which is carried out from any place and at any time, thanks to the use of mobile terminals with a wireless connection, which allows students to access the necessary data, either through the network or in documentation stored in the device (AL-EMRAN et al, 2020).…”

Exploring Chinese EFL Teachers’ Acceptance of Mobile-Assisted Language Learning (MALL)

“…Ain Nadzimah Abdullah and Arshad Abdul Samad are with the School of Education, Taylor's University, Subang Jaya, Selangor, Malaysia (e-mail: ainnadzimah.abdullah@taylors.edu.my, arshad.abdsamad@taylors.edu.my). environment (p. 171) [5]." Later, the notion of Mobile-Assisted Language Learning (MALL) was proposed as a subset of Computer-Assisted Language Learning (CALL) and m-learning.…”

“…Researchers (such as Ozdamli & Uzunboylu [9]; Liu, Tao & Cain [10]) argued that while most college language learners had positive attitudes towards MALL, many language teachers were undecided, apprehensive, and had misgivings about it. Researchers, Mittal and Alavi [5], have suggested that the successful implementation of MALL for language education relies heavily on EFL teachers and their acceptance. However, Chen and Jia [11] in their review of studies conducted in China disclosed that they tended to focus on students whereas studies on in-service EFL teachers' perceptions and acceptance of the use of MALL have been neglected.…”