The predictive validity of scores obtained in first semester examination on performance in introduction to programming systems

A mini dissertation submitted to the Faculty Of Education in partial fulfillment of the requirements for the Degree Of Master Of Education (Research Methodology) in the Department of Educational Psychology and Special Needs Education at the University Of Zululand, 2017Introduction to Programming Systems is considered to be very difficult and has a very high average failure rate of between 30% and 40%. Some researchers have studied the characteristics of students who pass Introduction to Programming Systems without struggling and used those characteristics as predictors of success in Introduction to Programming Systems. This research studied the relationship between selected predictors (Calculus, Discrete Mathematics, Classic Mechanics and General Chemistry) and Introduction to Programming Systems. The study adapted a case study and correlation research design. A sample size of 399 was selected using a non-probability sampling method called convenient sampling. Data from only one university were used. SPSS’s Pearson correlation and multiple regression was used to analyse the collected data. The results showed that there is a positive correlation between the criterion (Introduction to Programming Systems) and the predictors. Multiple regression results showed that the ordinal strength of predictor was as follows: Calculus, Discrete Mathematics, Classic Mechanics and General Chemistry. Only General Chemistry had an insignificant effect on the criterion. The variation was 34 %

PREDICTING RURAL STEM TEACHERS’ ACCEPTANCE OF MOBILE LEARNING IN THE FOURTH INDUSTRIAL REVOLUTION

In South Africa, high schools’ Science, Technology, Engineering, and Mathematics (STEM) education is faced with many challenges. However, previous studies have shown that mobile learning (m-learning) can be used to lessen the challenges faced in STEM education. Despite the benefits that m-learning can bring into STEM classrooms, its adoption is still below the expected rate. The acceptance of m-learning depends on the attitude of its users. Most studies focused on learners’ acceptance of m-learning. However, very little is known about rural high school STEM teachers’ acceptance of m-learning in the Fourth industrial revolution (4IR) era. This study proposes a model, which extends the Technology Acceptance Model by introducing perceived social influence and perceived resources. Stratified random sampling was used to select 150 teachers to participate in the survey. A total of 114 valid questionnaires were collected, and data were analysed using partial least squares structural equation modelling. The proposed model explained 37.9 % of the variance in teachers’ behavioural intention to use m-learning in the 4IR era. Perceived attitude towards the use was found to be the best predictor of teachers’ behavioural intention, followed by perceived ease of use, perceived resources, perceived social influence, and lastly perceived usefulness

An Exploration of Stem Students' and Educators' Behavioural Intention to Use Mobile Learning

This study sought to find factors that Science, Technology, Engineering, and Mathematics (STEM) students and educators in a developing country consider important when accepting mobile learning. The study developed a new model by extending the Technology Acceptance Model (TAM) using the construct perceived resources. Using stratified random sampling, a total of 160 STEM students and 100 educators were selected to participate in this study. The study employed a quantitative design where partial least squares structural equation modeling was used to examine STEM students' and educators' behavioural intention to use mobile learning. The developed model explained 74.1% of the variance in STEM students' and educators' behavioural intention to use mobile learning. Perceived resources, perceived ease of use, and perceived usefulness variables explained 54.8% of the variance in attitudes of STEM students' and educators' behavioural intention to use mobile learning. Attitude was the strongest indicator of STEM students' and educators' behavioural intention to use m-learning.  The results indicated that both educators and students have a positive attitude towards mobile learning, given how important online learning is becoming nowadays. Additionally, there is no statistically significant difference between educators’ and students’ attitudes towards mobile learning. The implication is that developers of mobile learning systems should make their platforms easy to use and have more resources available for both teachers and learners to increase the overall acceptance of mobile learning in STEM subjects

Determinants of mobile learning acceptance among grade 12 learners, their parents and teachers in the rural King Cetshwayo District

A thesis submitted in fulfilment of the requirement for the Degree of Doctor of Education in the department of Maths, Science And Technology Faculty of Education, at the University of Zululand, 2020.Science, Technology, Engineering, and Mathematics (STEM) is faced with challenges, resulting in learners’ poor performance at the matriculation level, in South Africa. In trying to improve learners’ performance in STEM-related subjects in grade 12, the Department of Basic Education, and other stakeholders, encouraged the use of mobile learning in the classroom. However, the adoption of mobile learning is contingent on the user’s attitude towards it. Despite the call by the Department of Basic Education to use mobile learning, very little is known about rural school STEM learners’, their teachers’, and parents’ acceptance of mobile learning. In response to the lack of such limited and established studies in rural settings, this study proposed and used the South African Schools' Technology Acceptance Model (SASTAM) to investigate the factors that influence rural high school STEM learners’, their parents’ and teachers’ behavioural intention to use mobile learning for STEM learning. The SASTAM is based on the Technology Acceptance Model specifically, to examine significant differences between rural high school STEM learners’ and their parents’ and teachers’ acceptance of mobile learning. Identifying and understanding the factors that influence the acceptance of mobile learning is key to its successful implementation. The study used an explanatory sequential mixed method design to investigate mobile learning technology acceptance in rural high schools in King Cetshwayo District. Stratified random sampling was used to select 550 rural high school STEM learners, their parents, and teachers to participate in the survey. The results from 417 respondents were stored as data and were analysed using partial least squares structural equation modeling (PLS-SEM). After quantitative data analysis were conducted,12 participants were selected to take part in interviews. The SASTAM was validated using PLS-SEM. The results revealed that the variance explained by the model in the behavioural intention of learners, parents, and teachers was 44.3%, 39.7%, and 43.8% respectively. The data from the learners, teachers, and iv parents were combined and analysed and the variances in behavioural intention to use mobile learning, which was explained by the SASTAM, was 40.8%. Original Technology Acceptance Model variables (perceived attitude towards the use, perceived usefulness and perceived ease of use) had a direct influence on behavioural intention, and they also played mediating roles between the external variables (perceived social influence, perceived psychological readiness, perceived skills readiness and perceived resources) and behavioural intention to use mobile learning in a rural setting. Multigroup analysis results showed that, for parents and learners, three paths (perceived ease of use to perceived attitude, perceived resources to perceived ease of use, and perceived social influence to perceived attitude towards the use) were significantly different. In contrast, only one path (perceived resources to perceived attitude towards the use) was significantly different for learners and teachers. However, all the paths were significant in each group, meaning that SASTAM can be used to predict the acceptance of mobile learning for rural high school STEM learners, their parents, and teachers. The results of this study will both inform the Department of Basic Education of the factors that rural high STEM parents, learners and teachers consider important when accepting mobile learning, and advance the debate on the conceptual understanding of technology acceptance in education by refining the Technology Acceptance Model to suit the context, leading to a deeper understanding of factors that affect mobile learning acceptance in rural areas of developing countries

An Analysis of Rural-Based Universities’ Faculty Members’ Satisfaction with E-Learning: The Case of Developing Countries

The COVID-19 pandemic brought about considerable detrimental effects on higher education, especially in developing countries. Ironically, it also contributed positively towards one sustainable development goal (SDG4) through advancement in technology, particularly the implementation and use of digital technology among academics and students. This study focused on the analysis of rural-based universities’ faculty members’ satisfaction with e-learning by seeking answers to two research questions: (1) what are the factors that influence faculty members’ satisfaction with e-learning, and (2) is there a significant difference between instructors’ and students’ satisfaction with e-learning? A combination of the expectation confirmation model (ECM) and the technology acceptance model (TAM) was employed to develop the users’ satisfaction model (USM). A survey design was used in which quantitative data were gathered using a 7-point Likert scale questionnaire. The data were analysed using partial least squares–structural equation modelling, with the help of SmartPLS3. The results showed that 81.9% of the variance in faculty members’ satisfaction with e-learning can be attributed to the seven factors of the model. Multigroup analysis also showed that the USM may be used to predict and explain faculty members’ subgroups’ satisfaction with e-learning

Determinants of High School Learners' Continuous Use of Mobile Learning during the Covid-19 Pandemic

Every child has a right to education and attending school is a must in South Africa. However, school attendance was severely disrupted by the Covid-19 pandemic outbreak. Regardless, the academic process has to continue, hence the use of mobile devices as pedagogical tools for learning. The aim of this study therefore is to explore the determinants of high school learners' continuous use of mobile learning in order that the academic project may continue. The study employed a survey design in which quantitative data were collected using a seven-point Likert-type scale questionnaire developed by the researchers. A stratified sample of 500 high school learners participated in the survey of which 419 of them successfully completed the survey, giving a success rate of 83.8%. The remaining 16.2% submissions were spoilt and hence discarded. The study combined three models, namely the technology acceptance model (TAM), self-determination theory (SDT), and the expectation-confirmation model (ECT) in its analysis of the developed seven-construct model which used partial least squares structural equation modelling (PLS-SEM). SmartPLS v 3.0 was used to validate the measurement and structural models of the study. Results showed that all six variables identified for the model were good predictors of high school learners’ continuous use of mobile learning with 68% explained variance for satisfaction and 39.1% for continuous use. The study developed and validated a robust mobile learning model which is recommended to stakeholders for continuous use of mobile learning. Future researchers are encouraged to search for more determinants of continuous use of mobile learning that have not been identified in this study.</jats:p

Rural STEM Preservice Teachers’ Acceptance of Virtual Learning

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.</jats:p