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ENG2601 EXAM PACK 2026 - DISTINCTION GUARANTEED

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Well-structured ENG2601 EXAM PREPARATION PACK - DISTINCTION GUARANTEED. Contains recent exam questions and answers, and Summarised study notes. All you need to pass the 2026

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ENG2601
EXAM PACK
1
 Recent exam questions and answers
 Summarised study notes
 Exam tips and guidelines




+27 81 278 3372
DISCLAIMER & TERMS OF USE
 Educational Aid: These study notes are intended to be used as educational resources and should not be seen as a
replacement for individual research, critical analysis, or professional consultation. Students are encouraged to perform
their own research and seek advice from their instructors or academic advisors for specific assignment guidelines.
 Personal Responsibility: While every effort has been made to ensure the accuracy and reliability of the information in
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,UNIVERSITY EXAMINATIONS




Oct/Nov 2025

ENG2601


Applied English Language Studies: Further
Explorations


100 marks
72 hours (3 days)


First examiners: Dr S Maluleke
Second examiner: Dr V Mgijima



This paper consists of 5 pages.

Instructions:
• This exam paper must be typed.
• The examination will be conducted as an open-book, take-home examination of 10
hours and is based on this examination question paper.
• Please answer all the questions and ensure questions are answered in accordance
with mark allocations.
• Use the Arial font size 12 for the body of your answer and 1.5 line spacing for your
paragraphs.
• Ensure that the work you submit is not in any way password protected or locked for
editing.
• Keep a record or proof of submission to safeguard your own interests.
• Ensure that the cover page must have your name, student number and module code.
• Your submission must be uploaded in a single PDF format.
• Students experiencing technical challenges should contact Dr. Sydney Maluleke

, (), Dr. Vukile Mgijima (), Dr. Joseph Moyo
() or Mr. Given Ranko (). Students must
reach out for help before the end of the examination session. Only communication
received via the student’s myLife account will be considered.


Read the text below and answer the question set.

How South Africa can prepare for a data-driven education system
Published: January 21, 2021 3.34pm SAST
Authors: Mmaki Jantjies and Paul Plantinga
le
There are significant disparities in South Africa’s education system. Schools are divided into
quintiles, from one to five; the poorest, in quintile one, struggle enormously with a lack of
resources and support. They also tend to have poorer educational outcomes. That has a
direct effect on university admission and outcomes.
One of the government’s attempts to address these inequalities is through technology. This
began as early as 2003 with the Draft White Paper on e-Education. These and similar
policies aim to resource more marginalised schools, universities and colleges with digital
tools. This, in a bid to “leapfrog” access to interactive learning content and improved
administrative capabilities. COVID-19 lockdowns have made this approach “imperative …
now the only thing we can do”, according to the country’s Ministry of Basic Education.

More and more, data and data-driven tools are emerging as a central feature of this digital
response. Developers of these technologies promise a new level of insight and automation
that mimics human intelligence. They argue this will bring greater efficiency and
effectiveness to both teaching and learning as well as to administrative processes. They
suggest that performance dashboards, automated assessments, chat bots and adaptive
learning technologies can mitigate many of the challenges faced by the country’s teachers,
lecturers, district managers and university administrators.

There’s a growing global evidence base to support these sorts of approaches. For instance,
teachers in under-resourced schools with large classes could use technology to gather
individualised data. With this they could develop more personalised learning experiences
for pupils based on their strengths and weaknesses.

Data is the backbone of these tools. The growth of machine learning and other intelligent
applications has been spurred by the increased collection and availability of data. Such data
underlies the kinds of adaptive applications and emerging technologies that are proposed

, for use in the education system.

We collaborated on a guide that examines how South Africa can ensure its data policy and
governance takes some of the lessons and concerns from previous education technology
implementations into account. It also considers the practical steps needed for this to
happen. The guide is part of a series curated by the Policy Action Network (PAN), a project
by South Africa’s Human Sciences Research Council (HSRC).

Here are some of the things a data policy for South Africa’s education system should
consider.

Technology impact

Experience shows that simply providing technology to teachers or students has a limited
effect on educational outcomes. The benefits of online, assisted learning and behavioural
interventions also vary depending on how technology is used, and in what context. This is
highlighted in working papers that review the effectiveness of educational
technology globally and in developing countries.

In South Africa, questions about effectiveness are amplified. That’s because of concerns
about unequal Internet access. Cost-effectiveness and teacher perceptions are also
issues.

Data management

A key issue centres on how data is collected, shared and used. It’s crucial that personal
information should be kept private. Education institutions need to comply with the Protection
of Personal Information Act (POPIA), which comes into force effect later in 2021.

Another question concerns sharing and reuse across the wider spectrum of education data.
This ranges from the content of books and journal articles to administrative data, such as
student enrolments and graduations. Sharing or publishing this data in a responsible way
can stimulate the development of many creative and useful applications. But data sharing
intersects with evolving copyright laws and debates around ownership and reuse. These
will have implications for data-driven innovation in the sector.

A third point is to reckon with well-documented concerns about bias embedded in existing
data which is being used in decision-support applications. If this isn’t dealt with, data-driven
applications may reinforce historical prejudices and practices related to education.

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