Pre-Service Teachers’ Computer Self-Efficacy and the Use of Computers

Abstract

The purpose of this study was to examine the effects of pre-service teachers’ computer self-efficacy on their use of computers. The research used a quantitative design whose data were collected by using a structured five-point Likert scale questionnaire with responses ranging from 1(strongly agree) to 5(strongly disagree). Simple random sampling was used to select a representative sample of 400 participants from a population of 4000 pre-service teachers, of which 332 of them were successfully returned, yielding a response rate of 83%. The study employed the Technology-Acceptance Model with eight constructs, namely; demographic influence (DI), social influence (SI), basic computer skills (CS), access to computers (AC), perceived ease of use (PEOU), perceived usefulness (PU), computer self-efficacy (CSE), and actual computer use (AU). Factor analysis was used to analyse the data generated from the survey, with the help of PLS-SEM, using the SmartPLS software version 3.0. The findings of the study indicated that each of the identified factors in the model had a significant effect on CSE. In essence, the identified explanatory variables explained 73.7% of the variance in CSsE. The four independent variables explained 45.4% of the variance in PU of computers and 66.5% of the variance in PEOU of computer use. The CSE model also explained 60.6% of the variance in computer use. In order to develop a strong CSE for pre-service teachers, it is recommended that higher education institutions ensure that all students have access to the necessary computers, proper connectivity, and skilled technicians.

Rural STEM Preservice Teachers’ Acceptance of Virtual Learning

“…The explanatory power of this model is also greater than the 40% of the original TAM developed by Davis et al (1989). This finding supports the proposal made by several authors (Chibisa et al, 2021;Venkatesh et al, 2003a;Zarafshani et al, 2020), who proposed that adding context-related external variables to the TAM improves its explanatory power.…”

Acceptance of the GeoGebra Application in Learning Circle Theorems

“…Thus, the TAM postulates that PU is predicted by PEOU, and they are both influenced by external factors (Davis et al, 1989). The TAM is also considered as a well-established and robust technology acceptance theory (Chibisa et al, 2021;Mutambara & Bayaga, 2021).…”

Translation and Validation of the Brief Inventory of Technology Self-Efficacy (BITS): Simplified and Traditional Chinese Versions

“…CSE research has shown extensive support for CSE’s role in computer and technology use (e.g., Chibisa et al, 2021; Hsia et al, 2014; Thongsri et al, 2020). For example, CSE mediated the relations between several predictor variables and computer use in a sample of pre-service teachers in South Africa (Chibisa et al, 2021).…”

“…Computer skills are considered necessary for successful education and employment (Peng, 2017; Stošić, 2015). Consequently, there has been an interest in factors contributing to employees’ and students’ adoption and use of technology (Chibisa et al, 2021; Ferdousi, 2019). Of these factors, one of the most emphasized in current research is computer self-efficacy (CSE; Gupta & Bostrom, 2019).…”

“…CSE is a person’s confidence in their ability to successfully engage with computers and similar technology (Compeau & Higgins, 1995). Over the past decades, a robust literature base has emerged regarding the positive predictive role of CSE on the use of technology (e.g., Chibisa et al, 2021; Compeau & Higgins, 1995). As a result, numerous measures of CSE have been developed (see Gupta & Bostrom, 2019, for a review).…”