Cloneria Nyambali Jatileni

Purpose: This study examined pre-service and in-service teachers’ perception of computational thinking (CT) education and their intention to integrate it in STEM education in Sub-Saharan Africa. The study used a theoretical approach to delve into an important educational context, as recent literature suggests the potential of developing learners’ problem-solving skills through CT education and the need to integrate CT into the classroom. Design/Approach/Methods: The study developed a hypothesis to understand factors influencing teachers’ perception and intention to integrate CT in STEM education in Sub-Saharan Africa. Data from 476 respondents across the region were analyzed using structural equation modelling. Findings: The study findings show that teachers’ attitudes, such as interest in CT, career development in CT, and comfort with teaching CT, significantly influence their intention to integrate CT into STEM classrooms. The study also revealed that teachers’ perceived knowledge of CT significantly influences their intention to integrate CT in the classroom. Furthermore, it was found that the in-service teachers are more influenced by their perceived knowledge of CT than their pre-service counterparts. Originality/Value: Implications of these findings were presented, and the study contributes to the literature on CT education and teachers’ development for integration of new concepts in STEM classrooms.

The bring-your-own-device policy (BYOD) in schools has recently attracted considerable research interest. BYOD allows students to learn subjects like mathematics using personal mobile devices. Accordingly, BYOD can increase students’ desire to learn mathematics in school. In the study we report on here, we assessed 9th grade students’ (n = 500) behavioural intentions to learn mathematics using personal mobile devices in school. Data were collected from 12 urban and rural Namibian public schools through a paper survey. Drawing on the theory of reasoned action with an added facilitating condition component, we predicted students’ behavioural intentions to learn mathematics based on BYOD. The descriptive results show that most students own personal mobile devices and are eager to use them as part of their learning in school. Principal component and confirmatory factor analyses validated the 4-component model. The results indicate satisfactory construct validity. Structural equation modelling was used to examine the influence of the factors on the students’ behavioural intentions. The structural equation modelling results show that the theory of reasoned action and facilitating condition factors predicted students’ behavioural intentions toward BYOD for mathematics learning in school. The findings suggest that educational policymakers should amend existing policies to allow students to learn mathematics using personal mobile devices in school.

Students’ use of personal mobile devices (PMDs), including smartphones, tablets, and laptops, in mathematics classrooms globally has become feasible due to the implementation of the bring your own device (BYOD) initiatives and policies in schools. Students’ academic use of PMDs in mathematics learning has been associated with increased motivation, enjoyment, and enhanced performance. While debates continue worldwide on the bans of PMDs in schools, it is noteworthy that students in Namibian basic education are not permitted to use PMDs in schools. This study employed a comparative nonexperimental quantitative research design to assess the perceived usefulness (PU) and behavioural intention (BI) to use PMDs in mathematics classrooms among 500 Namibian students and 209 teachers. The study employed a volunteer purposeful sampling method to select participating students and teachers from government schools in the Omusati and Khomas regions. Student selection was based on self-selection from full-class mathematics groups. Similarly, the study utilised volunteer purposeful sampling to recruit grades 4–12 mathematics teachers. Utilising structural equation modelling, the findings revealed that students expressed more positive ratings for PMD usefulness and intention than did teachers. Furthermore, PU significantly predicted students’ BI to learn mathematics with PMDs in school. Multigroup confirmatory factor analysis supported configural, metric, and scalar invariance in PU and BI factor structures across both participant groups. However, when students’ latent means of PU and BI are set to 0, the teachers’ latent mean parameter estimates are negative, suggesting potential disparities. Teachers should recognise and leverage students’ positive perceptions of PMDs to enhance motivation and engagement in mathematics learning. Simultaneously, ensuring consistent assessment across participant groups is essential, while addressing teachers’ PMDs reservations through professional development and support remains crucial.

With the push to integrate Artificial Intelligence (AI) from kindergarten to twelfth-grade levels comes a need for equipped teachers. However, there needs to be more initiative in professional learning opportunities, which demands the required effort to ensure teachers learn the AI content they will be teaching. To design an effective professional development program, understanding teachers' existing knowledge, interest and disposition towards AI is crucial to devise strategies that could best support the teachers. As a result, this study aims to investigate in-service teachers' perspectives of teaching AI in schools from the perspective of planned behavior theory. Using a 7-factor scale of AI Anxiety, AI Readiness, AI Relevance, Attitude towards using AI, AI for Social Good, Confidence in AI and Behavioral Intention. This study sampled 159 in-service teachers in Namibia. The teachers' responses were analyzed with SmartPLS using Structural Equation Modelling and various Multigroup Analysis techniques. This study found that teachers' behavioral intention to teach AI depends on a combination of factors, including the relevance of AI, attitude towards using AI, the use of AI for social good and confidence. Meanwhile, AI Anxiety and readiness could not be linked to the intention to teach AI. We discussed our findings, highlighted the study implication, and suggested future directions.