Research Articles (Science, Mathematics and Technology Education)

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Pre-service science teachers' adaptive pedagogical reasoning in AI-supported lesson plans : a case with chemical equilibrium
Ndlovu, Bongani Prince; Khoza, Hlologelo Climant; Sibanda, Doras (Frontiers Media, 2026-06-09)
Although generative artificial intelligence (AI) is increasingly being integrated into lesson planning in teacher education, little is known about how pre-service science teachers adapt AI-generated content through pedagogical reasoning for contextually responsive science teaching. This study explored how AI influences pre-service science teachers' (PSSTs) adaptive pedagogical reasoning (APR) when planning lessons on chemical equilibrium in resource-constrained contexts. Drawing on the Refined Consensus Model of Pedagogical Content Knowledge, APR was conceptualised across four dimensions: identification and anticipation of students' thinking, adaptivity in lesson design, justification of instructional decisions, and alignment with learning goals and differentiation. An exploratory mixed-methods case study was conducted with 19 third-year PSSTs in a South African university. Participants developed traditional and AI-supported lesson plans using the Rationale for Lesson Design framework. Data were analysed using an APR rubric and Rasch analysis. Findings showed that AI-supported planning enhanced anticipation of students' thinking, justification of instructional decisions, and alignment with learning goals and differentiation. However, adaptivity in lesson design remained limited. The study suggests that while AI can scaffold deeper pedagogical reasoning, teacher education programmes should explicitly support adaptive instructional decision-making.
Governing generative AI in higher education : a global Delphi study on policy and practice
Crompton, Helen; Burke, Diane; Nickel, Christine; Bozkurt, Aras; Miao, Fengchun; Sharples, Mike; Greene, Jeffrey Alan; Parsons, David; Gill-Simmen, Lucy; Edmett, Adam; Pegrum, Mark; De Waard, Inge; Bonk, Curtis J.; Garcia, Manuel B.; Curry, John H.; Lindsey, LeeAnn; Yang, Mohan; Marshall, Stephen; Bali, Maha; Deutsch, Nellie; Le Roux, Suzaan; Benali, Mourad; Samsudin, Mohd Ali Bin; Tinmaz, Hasan; Bernacki, Matthew L.; Van Wyk, Mari; Singh, Lenandlar; Chigona, Agnes; Eaton, Lance; Xiao, Junhong; Velander, Johanna; Kim, Jinhee; Bellas, Francisco; Rajalakshmi, R.; Machado, Andreia de Bem; Palalas, Agnieszka; Yu, Sean (SpringerOpen, 2026-05-22)
As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.
Teachers' implementation of integrated science, technology, engineering and mathematics (STEM) education in Nigeria : roles of gender, qualification and teaching experience
Ogbu, Sunday; Ogbonnaya, Ugorji Iheanachor (Kamla-Raj Enterprises, 2026-07)
Some nations have already subscribed to STEM education and have continued to champion its progress, however, others seem to be lagging far behind in STEM education. This study, therefore, investigated the teaching of integrated STEM Education in Nigeria. Guided by four research questions and three hypotheses, the study employed a descriptive research approach. A sample of 207 STEM teachers drawn using proportionate and simple random sampling techniques participated in the study. A structured questionnaire developed by the researchers was used for data collection. The reliability index of the instrument, as obtained using Cronbach’s alpha, was 0.84. Descriptive and inferential statistics were used for data analysis. The findings revealed a low level of the implementation of Integrated STEM Education (ISTEME). Additionally, teacher qualification significantly influenced teachers’ implementation of ISTEME. However, both gender and teaching experience had no significant influence on the teaching of ISTEME. It is concluded that STEM education in Nigeria still operates at low level and teachers’ qualification has a significant role on its implementation. In light of the findings, recommendations were proffered.
A mathematics teacher’s classroom instructional decisions as a consequence of professional noticing : a pivotal teaching moments perspective of a single case study
Moremi, Koketso Clinton; Sekao, David (Elsevier, 2026-12)
Professional noticing is a necessary skill for the effective teaching and learning of mathematics. However, it is not an easy skill to master and execute efficiently. In this interpretivist-qualitative single case study, we used the categorisation of pivotal teaching moments (PTMs) to explore a mathematics teacher's instructional decisions upon noticing learners' mathematical thinking. The findings reveal that all five types of PTMs were evident in the observed lesson, whilst the teacher's instructional decisions included extending and making connections, as well as pursuing learner thinking. We acknowledge the limitation of not interviewing learners to gain more insights into the questions they posed, thereby creating PTMs which triggered the teacher's instructional decisions. To this effect, further research is needed to incorporate learners' (and possibly teachers') interviews to advance a better understanding of the PTMs and teachers' instructional decisions in mathematics classes.
Artificial intelligence in mathematics education of students with special educational needs
Lessing, Mary-Jane; Ogbonnaya, Ugorji Iheanachor (Scientia Socialis, 2025)
This study examined the role of artificial intelligence (AI) in supporting the mathematics education of students with special educational needs (SEN). Thirty peer-reviewed articles were analysed to examine (i) the types of AI technologies implemented in mathematics education, (ii) evidence on the effectiveness of AI interventions in improving learning, (iii) the benefits and limitations of AI in special education, (iv) comparisons of AI applications with traditional methods in terms of personalisation, engagement, and accessibility, and (v) applications of AI in supporting specific disabilities. The findings indicated that intelligent games, chatbots, personalised learning systems, and intelligent tutoring systems were the most frequently utilised AI-powered tools. All studies assessing effectiveness reported positive impacts on mathematical achievement, as well as benefits such as reduced mathematics anxiety, increased motivation, and enhanced independence among learners. Reported challenges included high costs, ethical concerns, teacher preparedness, and a digital divide limiting equitable access. Compared to traditional methods, AI offers more accurate diagnosis of learning difficulties, greater opportunities for personalised learning, and increased accessibility. AI showed promise in addressing specific disabilities such as dyscalculia and autism through adaptive and diagnostic tools. The review concluded that AI should be viewed as a complementary tool that extends teachers’ capacity to individualise mathematics instruction for students with SEN.
Has the gender gap narrowed in student admissions in engineering STEM courses? Evidence from Nigerian university
Orji, Emmanuel Ifeanyi; Onyeabor, Ebubechi G.; Omeje, Theresa O.; Ogbonnaya, Ugorji Iheanachor (Association of Scientists and Intellectuals of Kosovo, 2025-05)
Despite the emphasis on Sustainable Development Goals 4 and 5, which advocate for quality education and gender equity for all, the gender gap in the admission and enrolment of students in STEM fields, particularly in engineering, persists. The study, therefore, investigated the extent SDG has been achieved in the enrolment of students into Engineering courses. Faculty of Engineering in Nigeria University was used. The population of the is 6,159 (5,481 males and 678 females). Documentation analysis was used for quantitative data collection, while the Interview guide was used for qualitative data collection. Data were analyzed using frequency, percentages, and Chi-square (x2). Findings indicate that there exists a gender gap in enrolment among students admitted to Engineering courses in the university, among others. It was recommended that the government, parents, students, and teachers/lecturers have a lot of roles to play to see that this gender disparity or gap is narrowed.
Analysing mathematics teachers’ experiences during ICT integration into the CAPS FET Phase in South Africa
Strydom, Mariana Annalien; Mihai, Maryke Anneke (Routledge, 2026)
A preliminary investigation was conducted to gain insights into integrating information and communication technologies (ICTs) into mathematics. The investigation focused on the further education and training (FET) phase of the South African senior school curriculum. The technological pedagogical content knowledge (TPACK) framework was used as the theoretical framework. A qualitative research approach, with a convenience and purposive sampling strategy, was employed. Eight Grade 10–12 teachers from well-resourced schools in Gauteng were selected. Data were collected through semi-structured interviews and thematically analysed. The findings revealed that most participants had optimistic views and integrated ICTs into their teaching practices. When analysing their views and reported teaching practices using the TPACK framework, it was found that participants primarily integrated ICTs to increase productivity. However, owing to the demands of the FET curriculum, it was a major challenge for teachers to find time to identify new technologies that align with the FET curriculum. Further research is recommended to reconsider the functions and roles of support teams for time-intensive curricula, such as the FET mathematics curriculum, to improve the integration of ICT into mathematics lessons.
Validating a questionnaire for vocational teachers to monitor classroom-based assessment practices
Setlalekgosi, Mpho; Combrinck, Celeste (Common Ground Research Networks, 2026-03-26)
The challenge of a mismatch between school-based vocational education (VE) and practical work skills exists. Classroom-based assessment (CBA) is one of the tools that play a fundamental role in addressing this gap. However, teachers are confronted with reporting their CBA activities and challenges that can influence evidence-based decision-making. Measuring teachers’ CBA practices and challenges is pivotal for their improvement. With limited tools developed to monitor and evaluate CBA practices and challenges, teachers cannot thoroughly communicate and make improvements. The study developed a CBA questionnaire for teachers to evaluate and improve their CBA practices. The exploratory sequential mixed-methods approach was used to develop and refine items. The qualitative phase guided the instrument design and helped to obtain the needed quantitative data to validate the instrument. A total of 101 vocational teachers in Botswana answered the questionnaire, and the results were analyzed using the Rasch Measurement Model (RMM). Overall, there is sufficient evidence from validation that the developed questionnaire is internally reliable and valid. There was evidence of unidimensionality, with large and significant correlations of constructs as theoretically required. All items of the questionnaire met the model fit except one item. The instrument is suitable for evaluating teachers’ CBA practices and challenges. The study contributes a tool for teachers, lecturers, and educational managers to use in VE, especially in developing contexts. The questionnaire could stimulate more research and support for enhanced CBA. Teachers’ responses to the questionnaire can inform interventions and supportive actions that improve CBA.
Mathematics teachers' use of questioning when teaching quadratic functions
Ogbonnaya, Ugorji Iheanachor; Ibeawuchi, Emmanuel O.; Engelbrecht, Johann (Taylor and Francis, 2026)
In this qualitative study, we investigated how a group of five grade 11 mathematics teachers teach the topic of quadratic functions. The focus was on how they used questioning in their presentations of quadratic functions to their students. The teachers were observed while teaching the topic and interviewed after their lessons. We focused on two categories of questions, distinguishing between probing questions and questions to promote discussion around important concepts. We found that although some teachers would employ both types of questions productively to identify misconceptions and promote conceptual understanding, some teachers do not use either type of questioning approach, mainly to save time and not to confuse other students.
Social media applications as pedagogical tools for teaching
Abrahams, Bereldene Robin-Lee; Moodley, Kimera; Robberts, Anna Sophia (Independent Institute of Education, 2026-05-27)
Social media applications are increasingly prevalent, yet their educational potential remains underexplored. While these platforms can enhance communication and collaboration in formal and informal learning environments, their effective integration into education requires further investigation. Guided by the Technological Pedagogical Content Knowledge (TPACK) framework, this study examines how seven Grade 6 and 7 teachers at a public primary school in Cape Town, South Africa, integrated social media platforms like WhatsApp, YouTube, TikTok, Facebook, and Instagram into their lessons. Although literature highlights the benefits of social media in boosting student motivation and confidence, its role in primary education remains limited. This qualitative case study explored teachers' integration of social media tools and their impact on learning outcomes. Purposive and convenience sampling were used to select participants in a socioeconomically diverse school. Data were gathered through questionnaires, lesson plan analysis, and semi-structured interviews, offering a detailed understanding of teachers' experiences. While WhatsApp was frequently used, YouTube and TikTok were the preferred platforms, promoting creativity, collaboration, and student engagement. The findings suggest that while social media positively impacts educational outcomes, challenges persist due to limited training and technological knowledge. Further research should explore the long-term effects, the role of professional development, and the potential use of social media in summative assessments to enhance dynamic learning environments.
Can AI express empathy? Computational insights from mental health dialogues
Kehinde, Olasunkanmi; Adeyeye, Elijah; Ayanwale, Musa A.; Mojoyinola, Mubarak O. (Springer Nature, 2026-05-27)
The growing demand for mental health support has accelerated the adoption of AI-powered chatbots intended to alleviate gaps in access to care. The global AI mental health market is projected to reach 17.8 billion USD by 2030, yet it remains unclear whether AI-mediated therapeutic conversations differ fundamentally from human-delivered therapy in emotional expression and linguistic style. This uncertainty raises critical questions for clinical integration, user trust, and ethical deployment. This study investigated whether emotional expression patterns differ between AI mental health conversations and human therapy sessions. A total of 1,000 AI chatbot conversations and 1,000 human therapy dialogues were extracted from open-access datasets and analyzed using computational text analysis and machine learning. Emotional intensity was measured across five domains (anxiety, depression, loneliness, positive affect, anger), alongside sentiment polarity, self-disclosure markers, conversational structure, and linguistic features. Statistical tests used Benjamini–Hochberg FDR correction and effect size estimation. A Random Forest classifier with 5-fold cross-validation evaluated the distinguishability of AI and human conversations. Results showed that emotional expression was statistically indistinguishable across most domains. Machine learning classification yielded only 48.3% accuracy, equivalent to chance performance. Key distinguishing features were structural rather than emotional word count and sentiment accounted for 44% of predictive value. These findings suggest that users express emotions in comparable ways to AI and human therapists. AI chatbots should be positioned as accessibility tools that supplement but never replace clinical care.
Mapping neural network research in education through bibliometric analysis
Ayanwale, Musa Adekunle; Olatunbosun, Stella Oluwakem; Bamiro, Nurudeen Babatunde (Springer Nature, 2026-03-25)
Neural networks are reshaping educational research and practice; yet evidence syntheses often treat them as part of a broader “AI in education” landscape, obscuring neural-network-specific trajectories, contributors, and themes. This study addresses that gap by providing a focused, methodologically transparent bibliometric mapping of neural network research in education. We analysed 704 Scopus-indexed journal articles published between 2010 and 2023, following PRISMA 2020 for identification, screening, and inclusion. Bibliometric performance analysis and science mapping were conducted using VOSviewer (for co-authorship, co-occurrence, and density visualisations) and the R-based Bibliometrix package (for descriptive indicators and thematic evolution). Network construction applied fractional counting and association-strength normalisation, with robustness checks using alternative thresholds. Findings show accelerated publication growth from 2019 onward, culminating in the highest output in 2023. The United States, the United Kingdom, and China lead productivity, while key institutions and author clusters function as collaboration hubs. Keyword co-occurrence reveals five dominant thematic clusters: (1) AI and machine learning foundations, (2) deep learning and neural network architectures, (3) learning analytics and personalised learning, (4) ethics, fairness, and explainability, and (5) higher education digital transformation. Thematic evolution indicates a shift from early automation-oriented work toward applied learning analytics and, more recently, governance concerns such as academic integrity and responsible AI, alongside emergent terms linked to generative AI. These results provide a replicable baseline for tracking intellectual structure, collaboration patterns, and ethical priorities in the field of neural network scholarship in education. We recommend strengthening interdisciplinary teams, expanding Global South participation through open infrastructure and partnerships, and prioritising transparent, fairness-aware designs when translating neural network research into educational policy and practice.
Leveraging psychological data and transparent machine learning for student success prediction in digital learning systems
Ayanwale, Musa Adekunle (Elsevier, 2026-06)
The growth of digital and blended learning has positioned Learning Management Systems (LMS) as core infrastructures for instruction and assessment. Although these platforms generate extensive behavioural data, the transparency and equity of machine learning (ML) models used to predict academic outcomes remain underexamined, particularly in low-resource contexts. This study evaluated the predictive performance, explainability, and fairness of six supervised ML classifiers-Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Random Forest, XGBoost, and Neural Networks using behavioural traces from Thuto (Sakai-based LMS) and survey-derived psychosocial indicators from 850 undergraduates at the National University of Lesotho. Automatically extracted LMS indicators (attempt frequency, discussion engagement, feedback access) were integrated with measures of digital learning self-efficacy, perceived feedback usefulness, trust in AI-supported assessment, and AI ethics awareness. Following preprocessing and a stratified 70–30 train–test split, models were trained and evaluated using accuracy, F1 score, MCC, AUC, confusion matrices, and statistical parity diagnostics. Logistic Regression achieved the highest accuracy (0.714), while Random Forest demonstrated the strongest discrimination (AUC = 0.774) and a favorable balance of classification errors, supporting its suitability for early identification of at-risk learners. Explainability analyses consistently highlighted digital self-efficacy, perceived feedback usefulness, and discussion engagement as dominant predictors across models. Fairness diagnostics indicated comparatively balanced positive prediction rates for linear and tree-based models, whereas neural networks showed instability consistent with overfitting. The findings emphasize that interpretable, fairness-aware modelling can strengthen the trustworthiness and pedagogical value of predictive analytics in digital higher education. Future work should extend fairness evaluation beyond statistical parity and examine real-time implementation through early-warning dashboards and adaptive feedback.
Digital transformation and public value creation in higher education : a PRISMA-ScR review and evidence-synthesized framework of digital competencies, institutional readiness, and governance pathways
Nwaigwe, Hope Chinenyenwa; Ayanwale, Musa Adekunle; Ukeje, Ikechukwu Ogeze; Aja, Ngene Innocent; Ekwunife, Raphael Abumchukwu; Atukpa, Emeka Izekwe; Nwigwe, Charity Ndidiamaka; Egba, Vivian Ndidiamaka (MDPI, 2026-05)
This study examines how digital transformation in higher education institutions (HEIs) contributes to public value creation, moving beyond efficiency-oriented narratives toward broader societal outcomes. Using a PRISMA-ScR approach, the study systematically reviews 47 peer-reviewed articles published between 2013 and 2025 across major academic databases. The review maps the evolution of scholarship and identifies the key mechanisms through which digital transformation influences public value. The findings reveal three interrelated dimensions shaping outcomes: digital competencies, institutional readiness, and governance alignment. Digital competencies enable the effective adoption and use of technologies, while institutional readiness—comprising digital infrastructure, leadership capacity, and organizational culture—acts as a mediating condition influencing implementation success. Governance alignment, including regulatory coherence, accountability mechanisms, and stakeholder engagement, plays a moderating role in determining whether digital transformation initiatives generate inclusive and socially beneficial outcomes. In addition to positive outcomes such as improved access, service quality, and transparency, the review identifies critical risks—including digital inequality, data governance challenges, and algorithmic bias—that may constrain public value creation, particularly in resource-constrained and Global South contexts. Building on these findings, the study develops the Global Digital Transformation—Public Value Creation (G-DTPVC) framework as an evidence-synthesized model derived from the reviewed literature. The framework specifies key constructs, causal relationships, and indicative measures to support future empirical research and policy application. By linking digital transformation processes in HEIs to broader public value outcomes and Sustainable Development Goals (SDGs 4, 9, and 16), this study advances theoretical understanding and provides actionable, context-sensitive guidance for policymakers and institutional leaders seeking to foster inclusive, accountable, and resilient higher education systems.
Virtual escape rooms as a game-based learning strategy through the lens of the ARCS model
Snyman, Ciska; Van Wyk, Mari; Moodley, Kimera; Botha, Tanita (University of Limpopo, 2025)
Generation Z students, who have grown up immersed in digital technologies, bring distinct expectations to their learning experiences. Yet, their motivation in online and hybrid learning environments remains a significant challenge. The study examines virtual escape rooms as a game-based learning strategy to enhance student motivation, aligned with a mixed-methods approach, combining quantitative surveys and qualitative interviews to measure motivation in a Microsoft Excel virtual escape room. Drawing on 461 voluntary survey responses and five in-depth interviews, the authors designed and facilitated the intervention within a compulsory university first-year digital literacy module. Results indicated that the immersive, gamified design of escape rooms boosted student motivation substantially, promoted collaborative learning, and enhanced critical thinking. Students reported greater motivation through attention-grabbing game elements, real-world applications, confidence-building challenges, and satisfaction from task completion. The study concludes that virtual escape rooms can serve as an effective, innovative strategy to promote active learning and student motivation. While international literature often generalises Generation Z as digitally fluent, this study acknowledges that South African students may have varied technological experiences due to differing access and educational backgrounds. Educators in Southern Africa could implement a similar digital strategy, like virtual escape rooms, to improve student motivation.
Using diagnostic assessment to determine the goal of the research lesson : a lesson study case
Radingwane, Mahlatse; Sekao, David; Sibiya, Mandlenkosi Richard; Moremi, Koketso Clinton (University of Limpopo, 2025)
Two-tier diagnostic assessment, unlike single-tier diagnostic assessment, is credited for affording teachers a deep understanding of learners’ misconceptions in mathematics, thereby improving teaching and learning. The purpose of this interpretivist case-study paper was to identify grade 6 learners’ misconceptions of fractions, using a two-tier diagnostic assessment, thereby determining the goal of the research lesson within the Lesson Study (LS) context. Data was collected via administration of a two-tier diagnostic test. Although some procedural and factual misconceptions were revealed, conceptual misconceptions appeared to be most dominant in the learning of fractions. However, the interplay between the three categories of misconceptions seemed to be inevitable. Therefore, we conclude and recommend that, instead of focusing on only one of the three categories of misconceptions related to fractions, the goal of the research lesson within the LS setting should encompass all three categories of misconceptions and address them in an integrated manner.
Unlearning and re-learning : exploring science teacher educators' experiences during the transition to emergency remote teaching in the COVID-19 pandemic
Khoza, Hlologelo Climant; Maseko, Bob (University of Limpopo, 2025)
During the COVID-19 pandemic, lecturers were required to transition from traditional face-to-face teaching to emergency remote teaching (ERT), necessitating a process of unlearning and re-learning pedagogical practices. This study examines the experiences of science teacher educators as they navigated this transition, with a specific focus on the nature of their adaptation, the aspects of teaching they unlearned and relearned, and the mechanisms that facilitated this process. Given the fundamental differences between face-to-face and online teaching, this study is framed using the theoretical constructs of border crossing and figured worlds. The research involved five teacher educators from institutions primarily engaged in face-to-face instruction. Data were collected through written narratives and follow-up interviews, and analysed using both narrative analysis and analysis of narratives approaches. The findings indicate that none of the participants experienced a smooth transition to ERT. Instead, their experiences were characterized as either hazardous or manageable, as they were compelled to unlearn and relearn various teaching practices, such as assessment strategies. Several key mechanisms facilitated this process, including critical reflection, collaborative engagement within professional communities, the utilisation of online resources, and student feedback. These findings provide valuable insights for teacher educators navigating similar crises and offer implications for fostering adaptability and resilience in times of educational disruption.
Self-regulated learning : a correlate of achievement motivation among senior secondary school adolescents in Nigeria
Muhammed, Shuaib Abolakale; Omidire, Margaret Funke (University of Limpopo, 2025)
Transition to senior high school introduces students to heightened academic expectations, requiring the formulation of effective strategies for achievement. This research investigates self-regulated learning (SRL) practices and achievement motivation among Senior Secondary School adolescents in Nigeria, using a descriptive survey design on sample of 400 secondary students selected through simple random and stratified sampling methods. Data were collected via the “Self-Regulated Learning and Achievement Motivation Questionnaire” (SRLAMQ). Data were analysed using percentages, means, rank order and Pearson’s Product Moment Correlation. Findings indicated a significant achievement motivation level, with an aggregate mean score of 3.22 and a moderate positive correlation between SRL habits and achievement motivation (r = 0.307, p < 0.05). Notably, gender differences emerged, with females showing a stronger correlation (r = 0.344) compared to males (r = 0.246). Age analysis revealed significant relationships for students aged 14–17 years (r = 0.352) and 18 years and older (r = 0.369), while no significant relationship was noted for ages 10–13 years. There were also positive correlations between SRL practices and achievement motivation based on school types. The study advocates for targeted interventions to enhance SRL practices among adolescents in Nigeria and a qualitative investigation into the gender differences noted.
Meqoqo under the Baobab : expanding participatory possibilities of (un)conferencing in South African higher education
Ganas, Rieta; Pather, Subethra; Krull, Greig; Frade, Nelia; Godsell, Sarah; Govender, Nereshnee; Govender, Rosaline; Jacobs, Anthea; Maluleka, Paul; Marhaya, Luyanda; Mawonga, Sisonke; Monareng, Balitiye Michelle; Ramrung, Arthi; Van Heerden, Leanri (University of Limpopo, 2025)
Conference participants often find dialogic value in conversations held in between conference sessions. As part of its annual event, the Higher Education Learning and Teaching Association of Southern Africa (HELTASA) grappled with how to disrupt traditional pre-conference workshop structures and foster a more inclusive, and participatory environment for scholarly engagement. This study explores the expansion of HELTASA’s (un)conference methodology through facilitated sessions of collective learning and teaching conversations on the first day of the event. Drawing on HELTASA’s (un)conference approach, the study is grounded in the principles of Ubuntu (human interconnectedness) and Ukama (relationality) to promote the values of community, balance, and reciprocity. Using the symbolic spirit of the Baobab tree as life, resilience, and interconnectedness, the Meqoqo (stories and conversations) under the Baobab sessions were conceptualised to support storytelling and dialogue that honoured diverse perspectives and lived experiences. Through a narrative inquiry methodology, an analytic framework towards planning and enacting methodological change was created and used to analyse the reflective narratives generated by the Meqoqo facilitators. The findings highlight that the intentional first day dialogic design of the (un)conference facilitated Ubuntu, Ukama, community building, and knowledge exchange through collaborative learning and peer-to-peer interaction. This enabled (un)conference participants to address contextually relevant, contradictory and contentious issues in higher education. This demonstrates that stretching the participatory boundaries of scholarly gatherings can foster more inclusive, equitable, and dialogic participation. The study contributes strategies for equitable participatory approaches that support cross-boundary and transversal thinking through dialogue, voice, and embodied action.
Emotional intelligence dimensions as predictors of secondary school physics students’ academic achievement in Nsukka, Enugu State, Nigeria
Orji, Emmanuel Ifeanyi; Ogbonnaya, Ugorji Iheanachor (University of Limpopo, 2025)
Many studies suggest that Emotional intelligence (EI) is a predictor of academic success more than the intelligence quotient (IQ). However, some findings, especially in STEM-based subjects, show mixed evidence. The present study, therefore, sheds more light on the prediction of emotional intelligence in the academic achievement of physics students. A correlation survey design was employed with a sample of 360 senior secondary physics students from Nsukka Local Government Area, Enugu State, Nigeria. Data collection was done using a questionnaire and an achievement test. Cronbach's alpha reliability calculation revealed an overall index of 0.85 for the questionnaire, while Kuder-Richardson’s reliability coefficient of 0.62 was obtained for the achievement test. Multiple regression, regression ANOVA, and t-tests were used to analyse the data. Findings revealed that of the EI components, only self-awareness and social skills significantly predicted achievement(p<0.001), whereas self-regulation, motivation, empathy, and overall EI did not. Given the findings, physics teachers should design cooperative learning tasks that foster social skills and self-awareness.

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