Predicting the Determinants of Dynamic Geometry Software Acceptance: A Two-Staged Structural Equation Modeling — Neural Network Approach

Chen, Chiu-Liang

doi:10.18178/ijiet.2020.10.6.1403

Cited by 2 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the findings of Padmanathan and Jogulu (2018) and Mutambara and Bayaga (2020c), one can conclude that the successful adoption of GeoGebra by pre-service teachers is contingent on their acceptance of it. However, little is understood about GeoGebra's acceptance for learning circle theorems (Chen, 2020). Mukamba and Makamure (2020) observed a scarcity of studies focusing on factors that pre-service teachers consider important when accepting GeoGebra.…”

Section: Introductionmentioning

confidence: 99%

“…A considerable amount of research has been carried out on the use of GeoGebra in the mathematics classroom (Aman et al, 2020;Belgheis & Kamalludeen, 2018;Chen, 2020;Johar, 2021;Septian & Monariska, 2021;Venter, 2015). Venter (2015) investigated in-service teachers' acceptance of GeoGebra.…”

Section: Introductionmentioning

confidence: 99%

“…Septian and Monariska (2021) focused on what motivates learners to use GeoGebra for learning mathematics. However, there are very few studies that have focused on factors that influence the acceptance of GeoGebra (Aman et al, 2020;Belgheis & Kamalludeen, 2018;Chen, 2020;Johar, 2021). http://ijlter.org/index.php/ijlter Chen (2020) and(Johar, 2021) assessed the acceptance of GeoGebra by university students.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are very few studies that have focused on factors that influence the acceptance of GeoGebra (Aman et al, 2020;Belgheis & Kamalludeen, 2018;Chen, 2020;Johar, 2021). http://ijlter.org/index.php/ijlter Chen (2020) and(Johar, 2021) assessed the acceptance of GeoGebra by university students. Aman et al (2020) and Belgheis and Kamalludeen (2018) looked at the factors that pre-service teachers consider important when accepting GeoGebra, but these were all conducted in developed countries, so their generalization to developing countries may be limited.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Acceptance of the GeoGebra Application in Learning Circle Theorems

Philemon

Chibisa

Mabusela

2022

IJLTER

View full text Add to dashboard Cite

The learning area of circle theorems is one of the most difficult topics in geometry, resulting in low student performance. GeoGebra has been shown in studies to enhance learners' proficiency in circle theorems. However, pre-service teachers' use of GeoGebra is not at the expected level in Eswathini. The adoption of an information system is reliant on its acceptance by individuals. However, little is known regarding pre-service teachers' use of GeoGebra to understand circle theorems. The goal of this study was to investigate pre-service teachers' perceptions of GeoGebra's suitability for learning circle theorems. A cross-sectional survey design was used in this investigation, with a total of 187 pre-service instructors as participants. The model explained 74.9% of the variance in the acceptability of GeoGebra for learning circle theorems by Eswatini pre-service teachers. According to the findings, task-technology fit, system quality, system compatibility, perceived ease of use, perceived usefulness, perceived attitude toward, and user satisfaction account for 74.9% of the variance in actual use. The study's findings revealed that rural Eswatini pre-service teachers' reported attitude toward using the mathematics software application GeoGebra for learning circle theorems was the strongest direct predictor of actual use. This research shows that pre-service teachers' views toward technology integration in education should be positive for educational learning applications to be successfully adopted in Eswatini teacher training institutes.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Acceptance of the GeoGebra Application in Learning Circle Theorems

Philemon

Chibisa

Mabusela

2022

IJLTER

View full text Add to dashboard Cite

show abstract

“…Prior research (Maphalala & Adigun, 2021;Mutambara & Bayaga, 2020) has also confirmed that PU and PEOU are the most powerful predictors of technology acceptance in education. PU and PEOU are also influenced by other external factors such as social influence (SI), computer self-efficacy, and facilitating conditions (PR) (Al Kurdi et al, 2020;Chen, 2020;Lim, 2018aLim, , 2018b. According to Chen (2010), the strongest predictor of preservice teachers' acceptance of technology in the classroom is their selfefficacy.…”

Section: Literature Reviewmentioning

confidence: 99%

Rural STEM Preservice Teachers’ Acceptance of Virtual Learning

Mutambara

Chibisa

2022

IJLTER

View full text Add to dashboard Cite

Teaching and learning of Science, Technology, Engineering and Mathematics (STEM) to preservice teachers in rural universities has always been a challenge, resulting in poor student performance. The outbreak of COVID-19 has made it exacerbated this owing to lockdown restrictions in most institutions including universities. Consequently, universities switched to virtual learning (VL), even though most of them (especially rural universities) were not ready for it. This worsened the plight of struggling rural STEM students who had to make do with this new VL. Hence, this study focussed on rural STEM preservice teachers’ acceptance of virtual learning. Prior studies have shown that adoption of a new information system depends on its acceptance by users; however, very little is known about the acceptance of VL by rural STEM preservice teachers. Based on the technology acceptance model, the study proposed and used the STEM preservice teacher acceptance virtual learning model to investigate factors that predict rural STEM preservice teachers' actual use of VL. Partial least squares structural equation modelling was used to analyse data from 250 valid questionnaires. The model explained 74.6% of the variance in rural STEM pre-service teachers' actual use of VL. Latent variables, facilitating conditions, attitude towards use, and perceived ease of use had a direct impact on the actual use of VL. Attitude to use also played a mediating role between actual use and predictors, perceived enjoyment, perceived social influence, computer self-efficacy, and perceived usefulness. It was concluded that rural STEM pre-service teachers embrace VL given the desperate pandemic situation.

show abstract

Predicting the Determinants of Dynamic Geometry Software Acceptance: A Two-Staged Structural Equation Modeling — Neural Network Approach

Cited by 2 publications

References 47 publications

Acceptance of the GeoGebra Application in Learning Circle Theorems

Acceptance of the GeoGebra Application in Learning Circle Theorems

Rural STEM Preservice Teachers’ Acceptance of Virtual Learning

Contact Info

Product

Resources

About