Parent Satisfaction and Learning Outcomes in Johannesburg: A Comparative Study of Educational Technology Utilization by Preschool Parents
Bibliographic record
Abstract
Educational technology (EdTech) is increasingly being utilised by parents of preschool children in Johannesburg to support learning at home. However, there is limited research on parent satisfaction and its relationship with child learning outcomes. A mixed-methods approach was employed, combining surveys for quantitative data collection and interviews for qualitative insights. Parental satisfaction scores were measured using a validated scale, while child learning support outcomes were evaluated through standardised assessments conducted at home by parents. Parent satisfaction with EdTech tools varied significantly across different platforms (e.g., 40% reported high satisfaction with tablet-based apps compared to 25% for online forums), and there was a positive correlation between higher parental satisfaction scores and better child learning support outcomes, although the exact proportion was not quantified. The study provides preliminary insights into how parents perceive EdTech tools and their effectiveness in enhancing children's educational experiences at home. Future research should explore longitudinal effects and potential disparities among different socioeconomic groups. Further studies could investigate specific areas where EdTech can be most beneficial, such as literacy or numeracy skills, and the impact of parental education level on technology use and child learning outcomes. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".