https://ir.unisa.ac.za/handle/10500/2748 2026-05-05T12:05:27Z 2026-05-05T12:05:27Z Hlatshwayo, Mthokozisi Alfred https://ir.unisa.ac.za/handle/10500/32331 2026-04-07T09:36:54Z 2025-08-01T00:00:00Z

Investigating how Artificial Intelligence (AI) can be leveraged to optimise the distribution and allocation of social grants in South Africa Hlatshwayo, Mthokozisi Alfred This study investigates the potential of Artificial Intelligence (AI) technologies to enhance the distribution and allocation of social grants in South Africa, addressing inefficiencies, high costs, and administrative complexities. Social grants are vital for reducing poverty and improving welfare, but the current system faces challenges such as lengthy processing times, inconsistent data, and limited accessibility. A qualitative research design was employed, utilising semi-structured interviews with 20 stakeholders, including policymakers, officials from the South African Social Security Agency (SASSA), and beneficiaries. Data analysis used thematic analysis to assess the feasibility of AI integration. Pilot studies of Electronic Know Your Client (EKYC) and biometric verification across select provinces provided empirical insights. In order to enhance decision-making, lower fraud, expedite beneficiary verification, and optimise resource allocation within South Africa's social grant ecosystem, this study explores the potential applications of artificial intelligence (AI) technologies, including machine learning, predictive analytics, natural language processing, automated document verification, and anomaly detection. The study looks at international best practices, assesses the possible advantages and disadvantages of integrating AI in South Africa, and reviews policy preparedness in light of the nation's larger Fourth Industrial Revolution (4IR) goal. Findings reveal that AI technologies could mitigate inefficiencies by automating data verification, enhancing fraud detection, and streamlining decision-making processes. Pilot implementations of Electronic Know Your Client (EKYC) and biometric systems across Gauteng, Eastern Cape, and North West provinces demonstrated varied outcomes. Gauteng saw a 5% improvement in fraud detection but faced challenges with system integration. The Eastern Cape struggled with downtimes, while the North West achieved a 10% reduction in identity theft but encountered transaction inaccuracies. This research contributes a comprehensive AI integration model tailored to South Africa’s socio-economic context, providing actionable recommendations for policymakers. Key considerations include robust infrastructure, workforce training, regulatory updates, and ethical safeguards to ensure fair and inclusive AI deployment. The study advances the discourse on AI in public administration, offering practical insights for achieving greater efficiency, transparency, and equity in social grant distribution.; Lolu cwaningo luhlola ukuhlanganiswa kobuchwepheshe be-Artificial Intelligence (AI) ohlelweni lokusabalalisa izibonelelo zenhlalakahle eNingizimu Afrika ukuze kuthuthukiswe ukusebenza kahle, kuncishiswe ukukopela, futhi kwandiswe ukufinyeleleka. Uhlelo lwezibonelelo zenhlalakahle lwaseNingizimu Afrika, olulawulwa yi-South African Social Security Agency (SASSA), lubalulekile ekunciphiseni ububha nasekuthuthukiseni inhlalakahle yezenhlalo. Nokho, izinselelo ezinjengokungasebenzi kahle kokuphatha, ukukopela, nokulinganiselwa kwezinsiza kuthinta ukusebenza kahle kwalo hlelo. Lolu cwaningo lusebenzisa indlela yokucwaninga yekhwalithethivu, lusebenzisa izingxoxo ezihlelekile neziphathimandla ezibalulekile, okuhlanganisa izikhulu ze-SASSA, abenzi benqubomgomo, nabathola izibonelelo. Ukuhlaziywa kwezindikimba zedatha, okuhlangene nocwaningo lwamapayona lwe-Electronic Know Your Client (EKYC) kanye nebiometric verification ezifundazweni ezikhethiwe, kuhlaziywe imiphumela yocwaningo. Ucwaningo lwapayona lubonise amandla e-AI ekutholeni ukukopela, kwathi eGauteng kwaba nokwehla ngo-5% kokukopela, kwathi eNyakatho Ntshonalanga kwaba nehlazo lokuncipha kuka-10% ekuntshontshweni kobunikazi. Nokho, izinselelo ezifana nokwehla kwezinhlelo zesistimu nokungahlangani kahle kwezinhlelo eMpumalanga Kapa ziveza isidingo sokuthuthukiswa kwezingqalasizinda. Imiphumela ikhombisa ukuthi i-AI ingathuthukisa kakhulu ukuphathwa kwezibonelelo zenhlalakahle ngokuzenzekelayo ekuqinisekisweni kwabamukeli, ekunciphiseni izikhathi zokucubungula, nasekukholeni ukusebenza kwamasistimu atholayo. Nokho, ukuthuthukiswa kwe-AI okuphumelelayo kudinga ingqalasizinda eqinile, ukuqeqeshwa kwabasebenzi, kanye nokuqapha kwezomthetho ukuze kuqinisekiswe ukusetshenziswa okuzuzisayo nokulungile. Lolu cwaningo lunikeza isibonelo sokuqaliswa kwe-AI esenzelwe isimo seNingizimu Afrika, lunikeza izincomo ezisebenzayo kubenzi benqubomgomo. Ucwaningo lufaka isandla engxoxweni enkulu mayelana nokwethulwa kwe-AI ezinsizeni zomphakathi, lugqamisa isidingo sendlela elinganiselayo eqhathanisa izinzuzo zokusebenza kahle kanye nemithelela yesimilo nezenhlalo. Ngokubhekana nezinselelo zokwethulwa kwe-AI, lolu cwaningo lunikeza isiqondiso sendlela yokusebenzisa i-AI ukwenza uhlelo lwezibonelelo zenhlalakahle eNingizimu Afrika lube olusebenzayo, olungcono, nolungagwegwesi.; Patlisiso ena e hlahloba ho kenyelletsoa ha mahlale a Artificial Intelligence (AI) tsamaisong ea kabo ea meputso ea sechaba Afrika Boroa ho ntlafatsa ts'ebetso, ho fokotsa bomenemene, le ho ntlafatsa phihlello. Tsamaiso ea meputso ea sechaba ea Afrika Boroa, e tsamaisoang ke South African Social Security Agency (SASSA), e bohlokoa ho fokotseng bofuma le ho ntlafatsa boiketlo ba sechaba. Leha ho le joalo, mathata a kang ho se sebetse hantle tsamaisong, bomenemene, le mefokolo ea lisebelisoa a sitisa katleho ea tsamaiso ena. Patlisiso ena e sebelisa mokhoa oa lipatlisiso tsa boleng bo phahameng, e sebelisa lipuisano tse hlophisitsoeng le ba boholong, ho kenyeletsoa liofisiri tsa South African Social Security Agency (SASSA), ba etsang melao, le ba amohelang meputso ea sechaba. Tlhatlhobo ea data e entsoeng ka ho sekaseka lihlooho tsa bohlokoa, hammoho le teko ea pele ea Electronic Know Your Client (EKYC) le biometric verification liprofinseng tse ling, e fana ka leseli ka bokhoni ba AI tsamaisong ena. Teko ena e bontšitse hore AI e ka fokotsa bomenemene, moo Gauteng ho bileng le phokotso ea 5% ea bomenemene, ha Profense ya North West e bone phokotso ea 10% bosholu ba boitsebiso. Leha ho le joalo, mathata a kang ho putlama ha tsamaiso le mathata a kopano ea sistimi Profinseng ea Eastern Cape a bontša tlhoko ea lintlafatso tsa motheo. Liphuputso li bontša hore AI e ka ntlafatsa tsamaiso ea meputso ea sechaba ka ho nolofatsa ts'ebetso ea netefatso ea ba amohelang meputso, ho fokotsa nako ea ts'ebetso, le ho ntlafatsa lits'ebetso tsa tlhahlobo ea bomenemene. Leha ho le joalo, katleho ea AI e hloka hore ho be le motheo o tiileng oa meaho, koetliso ea basebetsi, le taolo e matla ho netefatsa hore AI e sebelisoa ka toka le ka nepo. Patlisiso ena e fana ka mohlala oa ho kenya AI ts'ebetsong ka mokhoa o lumellanang le maemo a Afrika Boroa, e fana ka likhothaletso tse sebetsang ho ba etsang melao le ba ikarabellang ho tsamaiso ea meputso ea sechaba. Patlisiso ena e kenya letsoho lipuisanong tse pharalletseng mabapi le AI le lits'ebeletso tsa sechaba, e hatisa tlhokahalo ea mokhoa o leka-lekaneng o hlahlobang melemo ea ts'ebetso hammoho le litlamorao tsa boitšoaro le tsa sechaba. Ka ho sebetsana le mathata a kenyelletsong ea AI, patlisiso ena e fana ka tataiso ea ho sebelisa AI ka tsela e ntlafatsang le e lumellanang le litlhoko tsa sechaba tsa Afrika Boroa. Abstract in English, Isizulu and Sesotho

2025-08-01T00:00:00Z Ramphele, Letsukulo Frans https://ir.unisa.ac.za/handle/10500/32280 2026-03-29T08:32:16Z 2025-08-01T00:00:00Z

Mining Limpopo Education Management Information System (EMIS) to model intra-provincial learner migration/mobility patterns and their underlying causes Ramphele, Letsukulo Frans Spontaneous learner migration is an ongoing concern in South African schools, posing challenges to educational planning and resource allocation. This phenomenon refers to learners transitioning prematurely to alternative learning spaces without prior planning. It is more prevalent in rural provinces of South Africa, including Limpopo, where urban schools tend to be more affluent and well-resourced than rural schools. The complex interplay of personal, environmental, and socio-economic factors driving learner migration decisions often complicates predictive efforts, necessitating robust computational models for improved understanding and decision-making. This study utilised a longitudinal dataset from the Limpopo Education Management Information System (EMIS) records, spanning ten years (2011–2020), and identified biographical and structural variables that influence learner migration. These variables were used to develop three theory-based learner-migration indices: likelihood of migration, reason for migration, and distance of migration. The study applies Cross-Industry Standard Process for Data Mining (CRISP-DM) to guide the technical data-mining process and Design Science Research (DSR) to provide a broader framework that positions learner-migration computational models as reusable artefacts for educational planners. This methodological framework was grounded in Ravenstein and Everett Lee’s theories of migration and Hein de Haas’s aspiration–capability framework. Building on this methodological foundation, Feature Selection (FS) was performed using four techniques – Boruta, RPART, AdaBoost.M1, and J48 – to determine salient input features for the predictive models. Boruta demonstrated the most consistent feature importance scores, with a variance of 19.85 compared to 21.39 (RPART), 24.60 (AdaBoost.M1), and 24.60 (J48). The learner migration indices were optimised using the Social Ski-Driver (SSD) and Culture Algorithm. Both optimisers achieved commendable and comparable results, with the average F1 score metric for the three indices consistently surpassing 0.8 on a time series learner migration dataset spanning ten years. The CA-derived hyperparameter set was selected for the final model due to its low variance in the F1 score weights of the three indices and strong alignment with the Berger-Tal multidisciplinary framework's convergence principles on the exploration-exploitation trade-off. While previous studies on learner migration have primarily emphasised external factors such as a school’s poverty ranking, curriculum performance, the language of instruction, and legislative frameworks as the sole drivers of migration, this study reveals that migration is also influenced by biographical factors such as learner age, gender, home language, and socio-economic status. These insights are not just academic findings but may have direct implications for educational policy development and resource allocation strategies, offering a balanced understanding of migration dynamics. The developed models and their indices and metrics may support education planners in responding proactively to learner migration challenges. Abstract and text in English

2025-08-01T00:00:00Z Faro, Khusu Stanley https://ir.unisa.ac.za/handle/10500/32228 2026-03-15T13:16:29Z 2026-01-28T00:00:00Z

An investigation into the effects of e-learning readiness and student expectations on academic results in distance education Faro, Khusu Stanley This study sought to determine the combined effect of e-learning readiness and student expectations of academic results in distance education. The target population consisted of students registering at a higher education institution for the first time for a first-year programming course at the University of South Africa (Unisa). A questionnaire in the form of a web survey was used for the widely dispersed respondents due to its ability to reach a large number of them. The study was initially intended to be quantitative, but due to the low response rate, a descriptive quantitative research approach was adopted. While this study could not determine a conclusive relationship between students’ e-learning readiness, their expectations in an online environment and their academic performance, several interesting findings pertaining to student e-learning readiness and expectations in an online learning environment were identified. This study concluded that most students have positive expectations regarding e-learning and that most were, therefore, ready for it.; In hierdie studie is daar probeer om die gekombineerde uitwerking van e-leer-gereedheid en studente-verwagtings op akademiese resultate in afstandsonderrig te bepaal. Die teikenpopulasie het bestaan uit studente wat vir die eerste keer by ’n hoëronderwysinstelling registreer vir ’n eerstejaarsprogrammeringskursus by die Universiteit van Suid-Afrika (Unisa). ’n Vraelys in die vorm van ’n webopname is gebruik vir die wydverspreide respondente vanweë die vermoë daarvan om ’n groot aantal van hulle te bereik. Die studie was aanvanklik bedoel om kwantitatief te wees, maar weens die lae responskoers is ’n beskrywende kwantitatiewe navorsingsbenadering gevolg. Hoewel die studie nie ’n onweerlegbare verhouding tussen studente se e-leer-gereedheid, hulle verwagtings in ’n aanlyn omgewing en hulle akademiese prestasie kon bepaal nie, is daar wel verskeie interessante bevindings geïdentifiseer wat verband hou met studente se e-leer-gereedheid en verwagtings in ’n aanlyn leeromgewing. Daar is in die studie tot die gevolgtrekking gekom dat die meeste studente positiewe verwagtings het oor e-leer en die meeste studente dus gereed was daarvoor.; Lolu cwaningo luhlose ukuthola umphumela wokulungela ukufunda nge inthanethi kanye nokulindelwe ngabafundi ngemiphumela yemfundo yezamabanga amade. Isibalo sabafundi okwenziwe kubo ucwaningo sihlanganisa abafundi ababhalise esikhungweni semfundo ephakeme okokuqala, ukwenza unyaka wokuqala eNyuvesi yaseNingizimu Afrika (iUnisa). Kusetshenziswe uhlu lwemibuzo ngokohlelo lwesaveyi ye-inthanethi ukuze kutholakale abantu abaningi abazophendula, ngoba lolu hlelo luyakwazi ukusabalalela kubantu abaningi. Ucwaningo ekuqaleni lwaluhlose ukubandakanya abantu abaningi, kodwa ngenxa yesilinganiso esiphansi sezimpendulo, kwasetshenziswa ucwaningo zinombolo ikhwantithethivu. Nakuba lolu cwaningo lungazange lukwazi ukuthola ubudlelwano obuqinisekile phakathi kokulungela kwabafundi ukufunda nge-inthanethi, abakulindele endaweni yokufunda nge-inthanethi nangemiphumela yabo yokufunda, kwatholakala okuningana okujabulisayo okuphathelene nokulungela kwabafundi ukufunda nge-inthenethi kanye nokulindelwe endaweni yokufunda nge-inthenethi. Lolu cwaningo luphethe ngokuthi iningi labafundi lilindele okuhle mayelana nokufunda nge-inthenethi nokuthi iningi labo selikulungele. Abstract in English, Afrikaans and Zulu

2026-01-28T00:00:00Z Abdullahi, Aisha Muhammad https://ir.unisa.ac.za/handle/10500/32020 2025-01-21T07:19:23Z 2024-05-01T00:00:00Z

A persuasive educational technology-based tool for motivating junior secondary school female students in Nigeria to enrol in STEM Abdullahi, Aisha Muhammad Background: Science, Technology, Engineering, and Mathematics (STEM) fields have made significant contributions to our modern world's advancements. However, the number of women in STEM professions in Nigeria is overwhelmingly low. Concerns have been raised about the growing demand for STEM skills and the shortage of human resources to meet this demand. There is advocacy to encourage female students to pursue STEM disciplines to meet the demand for STEM skills. Despite this, no research has been conducted in Nigeria to increase female students' enrolment in STEM using the attitude and behaviour change approach, also known as the persuasion approach. This is despite the strong support for research that focuses on the affective aspect of STEM learning, which deals with students’ values, and believes to encourage female students' enrolment in STEM pathways. Aim: The main aim of this thesis is to develop a framework that guides the design of persuasive educational technologies (PET) based on the persuasion approach to motivate junior secondary school female students in Nigeria to enrol in STEM classes. Setting: The participants were drawn from three different secondary schools in Nigeria's Kano State. The research delved into the natural environment of the participants to ensure that contextually rich information was gathered from them to inform the study. Method: Data was collected from the research participants through interviews and a focus group session and analysed using a qualitative research approach. Following data analysis, the Design Science Research (DSR) strategy was used to guide the framework design. The DSR strategy's rigorous iteration phases ensured that the framework was redefined several times until a rigorous artefact was produced. Results: According to the study’s findings, certain external factors (school factors, family factors, and cultural factors) influence female students' attitudes toward STEM professions, and it is their attitude towards STEM professions that guides their decision to pursue a STEM pathway. A motivational push from a PET can encourage female students to pursue STEM careers. Conclusion: A PET tool developed following the PET4STEM framework has the potential to change female students' attitudes toward STEM professions and consequently their decision to pursue STEM careers in the future. The theoretical contribution of the study is the creation of a new PET4STEM framework that provides information on key components of a PET tool to promote STEM enrolment among Nigerian junior secondary school female students. Second, it establishes, empirically, the factors that influence female students' enrolment in STEM classes. Finally, it proposes convincing strategies for combating female students' negative perceptions of STEM subjects. Teachers, parents, and researchers who are unable to develop a PET tool can use these strategies. Text in English

2024-05-01T00:00:00Z