Maximising intervention success: A grade 10 analysis
We all want every learner to thrive in their education and ultimately excel in their final NSC exams. However, not all learners progress at the same pace. For example, according to SA-SAMS data visualised on the Data Driven Districts (DDD) Dashboard, 65% of Grade 10 learners did not pass Term 2 2023 and are therefore considered “at risk”.
This article explores how data can determine which intervention will have the most impact on these Grade 10 learners, in terms of in helping them pass the academic year.
Providing effective support requires understanding the root causes of poor performance, which can be challenging and costly to identify. It also means having the necessary resources, including financial support, human capital and time, to address those causes.
Living in a world with unlimited needs but limited resources means we cannot pinpoint the cause of underperformance in every struggling learner, nor do we have the resources to provide tailored interventions for each learner, even when we can identify the causes accurately.
So, what's the solution?
One answer lies in identifying and implementing interventions that will have the greatest impact in achieving our goals. Data - when analysed effectively – helps us do it. Predictive data insights answer questions like “what might happen to X if Y occurs?” We can then adjust the inputs and assumptions used for these predictions, allowing us to evaluate and compare various scenarios.
This not only enables us to focus resources on the actions that will have the most impact, but also ensures those interventions are well informed and therefore more likely to succeed.
The analysis below is based on Term 2, 2023 data and applies the rules set out by the National Policy Pertaining to the Programme and Promotion Requirements (NPPPPR) to determine which high-enrolment subject intervention is most likely to improve the overall learner pass rate for the next term. The findings might surprise you!
Before we delve into the results, please note that two assumptions have been applied:
We assume that a subject intervention will lead to a 10% increase in report marks for all learners in that subject. For instance, if a learner scored 45% in Geography in Term 2, we assume they will achieve 55% (45% + 10%) in Term 3 due to the intervention.
We assume that learners will perform the same in Term 3 as they did in Term 2 for all other subjects. This allows us to isolate and measure the impact of each subject intervention scenario. After evaluating all scenarios, we compare them to identify which one is likely to have the greatest impact on the overall promotion rate.
The following results are based on real Term 2, 2023 school data from a single province (which we do not disclose). We compared the impact on overall pass rates for the predominant Home Language subject, Geography, Life Sciences, Mathematics, Mathematical Literacy and Physical Science.
So, what does the data say?
Figure 1 shows that a 10% increase in report marks for Life Sciences promises the greatest impact (2.9% increase) on the overall pass rate. Second was Mathematical Literacy with a 2.8% increase. While these percentage increases may seem small, they represent thousands of Grade 10 learners.
Surprisingly, a 10% increase in the report mark for the primary Home Language subject (for every learner) only increased the overall pass rate by 0.2%. This is likely because many learners do not fail their Home Language subject, and those who do also struggle in other subjects. This means they need to pass more than just their Home Language subject to pass the term.
The next graph shows that what is true for the province might not hold for individual schools within it. For example, in School A, a 10% increase in report marks for Mathematics could lead to 20% more learners passing the term. This 20% increase represents 125 learners! Similarly, a 10% increase in report marks for Life Sciences could lead to 16% more learners passing the term. A district or provincial intervention focused on improving performance in Mathematical Literacy would likely be of little value to School A.
The next figure shows that in contrast, in School B, a district or provincial intervention focused on improving performance in Mathematical Literacy would likely bring significant value to the school, with a 21% increase in learners passing Term 3.
The final graph shows why it’s important to look beyond what seems obvious. While it may not seem beneficial to focus on Home Language at a provincial level, that does not mean that some schools would not benefit from such an intervention. Take School C, for example. In School C, a Home Language intervention (increasing report marks by 10% for each learner) would result in 10% more learners passing the term, with very small increases resulting from other interventions.
The analysis shows how tailored data-driven interventions consider the differences across schools. This personalised approach leverages data insights to customise intervention designs (eg, which subject, which learners, etc.) ensuring optimal relevance and impact. In contrast, blanket interventions overlook these distinctions, potentially leaving some schools underserved or even unnecessarily overwhelmed.
Are you surprised, confused, or intrigued by the findings? Please share your thoughts and feedback by clicking here.
There is so much more we can do with the data we have. We can increase or decrease our assumption regarding the potential impact of a subject intervention, or we could analyse and compare all subjects. We could even analyse the effects of two or more interventions at a time. By combining education criteria, skills, and information to create informed interventions, we can deploy our scarce resources towards interventions that promise the greatest "return on effort" in achieving our specific learner outcome objectives.
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