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Mpho Justice Khoza
Enabling early childhood care and education (ECCE) managers and practitioners to navigate the complexities of the function shift: A PALAR approach
(North-West University (South Africa), 2025) Thakalekoala, Koenane Joseph
Early childhood care and education (ECCE) is a crucial phase in a child's life that lays the foundation for holistic development. When nurtured well, it enables children to prosper throughout life and helps them develop into adults who contribute positively to the socio-economic development of the country. Yet, early childhood education remains one of the least developed sectors, characterised by poor infrastructure, underqualified staff, a lack of specialised programmes, limited access to quality services, and a lack of monitoring and support. To bring about improvement in the quality of and access to ECCE, a shift of responsibility for the function from the Department of Social Development to the Department of Basic Education took place in April 2022. This function shift aims to professionalise the ECCE sector, which is largely privately owned and informal. However, those that it affected the most, the ECCE managers and practitioners, were largely unprepared. This study explores their experiences related to the function shift and attempts to help them navigate the changes it requires. I approached this study through participatory action learning and action research (PALAR) methodology. Through PALAR principles, I aimed to establish a collaborative relationship with ECCE managers and practitioners in a semi-rural context. I recruited seven participants who were working in the field, two practitioners and five managers of ECD centres. We formed an action learning set and met weekly for two hours at a time. Data was collected through a combination of transcripts of our discussions and creative and visual methods, including drawings, collages and model building. I also kept a reflective diary that I used throughout the study to reflect on the process and progress, and my own learning. These various methods enabled participants to express themselves effectively in a playful and interactive manner. Guided by critical action learning theory, participants worked collaboratively to identify and navigate the complexities of the function shift. Five key themes emerged through the two PALAR cycles: 1) Participants' lack of knowledge, uncertainty and resistance towards the function shift, 2) Lack of support and training for ECCE managers and practitioners, 3) Emotional and financial challenges, 4) Competitive tendencies delaying collaboration and progress, and 5) Recognising the potential of collaborating to navigate the complexities of the function shift. This last theme emerged in cycle two and enabled them to develop specific actions to address the problems identified in cycle one. These actions included 1) the formation of support groups and promotion of teamwork, 2) promoting stakeholder involvement, 3) ensuring compliance and financial stability, 4) conducting further research, and 5) improving service delivery. Participants collaboratively generated a plan of action with timelines. This included an establishment of a hub that they named Rekopantswe. Participants iv established this hub to enable each other to navigate the complexities of the function shift, and further maximise the benefits thereof. Through this hub, participants can collectively advocate for relevant ECCE sector support to promote sustainability in the sector. This study contributes to improving the practice of ECCE and informs policy in that it highlights the need improved communication from government. It indicates the importance of practitioners forming communities of practice to collaboratively address sector challenges for equitable and inclusive support and monitoring. The study further showed how action learning can enable practitioners and managers to work together to learn with and from each other to support continuous professional development.
Finding the Bambara Groundnut That Can Thrive Anywhere
(North-West University (South Africa), 2026) Babalola, Olubukola Oluranti
A farmer plants Bambara groundnut seeds and waits. The seeds germinate. The plants grow. The pods begin to form. But there is a question that farmers have faced for generations: Will this variety perform just as well next season-and in a different place? The answer is not always simple. A crop variety that produces an excellent harvest in one environment may perform quite differently somewhere else. Rainfall changes. Temperatures change. Soils differ. Growing seasons vary. Even when farmers plant the same variety, the harvest may not be the same.
Will they or won’t they make it in time? The role of contextual and behavioral predictors in reaching deadlines of mandatory assignments
(ACM (Association for Computing Machinery), 2026) Imhof, Christof; Hlosta, Martin; Bergamin,Per
Procrastination and other forms of irrational delay are widespread among university students, leading to an array of potential negative consequences. While the reasons for this type of behavior are manifold and many facilitating factors have been identified, which of these factors are able to predict dilatory behavior in online/distance education has received comparatively little attention in the literature so far. In this study, we intended to compare the performance of two sets of objective predictors of delay, namely contextual variables based on characteristics of the assignment, and behavioral variables based on log data. Using historical data drawn from our university's learning management system, we calculated Bayesian multilevel models. The strongest and most consistent predictors of dilatory behavior turned out to be interval between the first click on the assignment and its deadline, the interval between the start of a block and the first click on the assignment, the number of clicks on the assignment, and the deadline type. The combination of both sets of predictors slightly improved the model's performance.
Investigating machine learning approaches for differentiating MGFs and SGRBs
(SISSA Medialab, 2025) Maheso, Dimakatso Jeannett; Razzaque, Soebur
Magnetar giant flares (MGFs) and short gamma-ray bursts (SGRBs) are short gamma-ray transients (SGRTs) with overlapping temporal and spectral characteristics, making them challenging to distinguish, especially when their redshift is unknown. In this study, we apply supervised machine learning using a Support Vector Machine to classify MGFs and SGRBs. Temporal parameters (including pulse rise times from Norris function fits) and spectral features (derived from Comptonized model fits over the 10 keV-40 MeV range) are extracted as input features for classification. We analyse 15 MGF and 101 SGRB samples from 10 Fermi-GBM sources. Classifier performance is assessed using leave-one-source-out cross-validation. The model successfully classifies most SGRBs, but struggles with MGFs due to limited training data and significant feature overlap. This work highlights the potential and challenges of incorporating machine learning into the automated classification of these SGRTs.