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Sample2 Research Skills

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RESEARCH SKILLS
COURSE/ UNIT INFORMATION
Course
Master in Data Science
Course Level
Masters
Module Name
Research Skills
Module Code
GM762
ASSIGNMENT INFORMATION
Full/ Part Assignment
Full
Assignment IV by
Dr. Sree Lakshmi
TO BE FILLED BY THE STUDENT
Student Name
Student ID
Email ID
Date Submitted
ASSSESSMENT FFEDBACK
TO BE FILLED BY THE ASSESSOR
Assessment type
Marks
Task 1: Essay Style
50
Task 2: Data Set
25
Task 3: Qualita ve Research
25
Overall Marks
100
Marks Awarded
Overall Grade achieved by the learner
Plagiarism Report
Summa ve Feedback by the Assessor for further improvement
TASK ONE [50 marks]
Task 1 requires a critical analysis of the following questions and the provision of the most
appropriate answers.
1. Critically analyse the differences between quantitative and qualitative research and
explain the circumstances in which each are appropriate? [12 marks]
Answer
One of the critical decisions that a researcher is faced with is to determine the approach
or methodology that best suits the subject being researched. Researchers may employ
Quantitative or Qualitative research methodologies or sometimes combine both to give
the best conclusions. In the paragraphs below, I examine the key differences between
Quantitative and Qualitative Research.
Qualitative Research is a research method that seeks to provide an in-depth
understanding or insight of a topic using non-numerical data. This research method is
suitable for exploring complex phenomena such as behavior, intention, feelings, and
attitude. The results are usually subjective with a lot of importance placed on the views
of the participants. Qualitative research involves collecting and evaluating nonnumerical data to understand concepts or subjective opinions. (Fournier, 2022)
Quantitative Research, on the other hand, gathers numerical or quantifiable data and
applies various statistical or computational methods to the data to draw conclusions. This
research attempts to draw the relationships between variables being studied.
Quantitative research involves collecting and evaluating numerical data. (Fournier,
2022)
Comparing Methodology
Qualitative Research is very useful for exploring the knowledge about a particular
subject matter. It is investigative in nature. It is also useful to unearth profound insights
into a particular subject matter. By its very nature, Qualitative Research is used to build
a theory or hypothesis as the researcher explores the observations to draw insights and
better understanding. Data collection is usually through conversations or interviews and
hence the number of observations or sample size is usually small. In-depth analysis is
done on the collected data and where necessary more data may be required to answer
follow-up questions that may arise from the initial analysis. Due to this, Qualitative
Research tends to be unstructured.
Quantitative Research is very useful for drawing empirical or factual conclusions about
a subject. It is more conclusive in nature. It is suitable for drawing cause and effect
relationship between variables. Quantitative research is used to test hypotheses and
draw a conclusion on whether the hypothesis is correct or not. Data collection tends to
be done through surveys or questionnaires and the information collected has the
property of being quantified, measured, ranked, or categorized in a way. This is useful as
analysis is done through the application of statistical or computation analysis. Due to this
approach, the sample size used in quantitative analysis are usually large. Qualitative
research is structured and usually follow the scientific method principles.
Below is a summary some of the key differences between Quantitative and Qualitative
analysis as indicated by Surbhi (Surbhi, 2018) of which I agree.
• Qualitative research is a method of inquiry that develops understanding on human
and social sciences, to find the way people think and feel. A scientific and empirical
research method that is used to generate numerical data, by employing statistical,
logical, and mathematical technique is called quantitative research.
• Qualitative research is holistic in nature while quantitative research is particularistic.
• The qualitative research follows a subjective approach as the researcher is intimately
involved, whereas the approach of quantitative research is objective, as the
researcher is uninvolved and attempts to precise the observations and analysis on the
topic to answer the inquiry.
• Qualitative research is exploratory. As opposed to quantitative research which is
conclusive.
• The reasoning used to synthesise data in qualitative research is inductive whereas in
the case of quantitative research the reasoning is deductive.
• Qualitative research is based on purposive sampling, where a small sample size is
selected with a view to get a thorough understanding of the target concept. On the
other hand, quantitative research relies on random sampling; wherein a large
representative sample is chosen to extrapolate the results to the whole population.
• Verbal data are collected in qualitative research. Conversely, in quantitative research
measurable data is gathered.
• Inquiry in qualitative research is a process-oriented, which is not in the case of
quantitative research.
• Elements used in the analysis of qualitative research are words, pictures, and objects
while that of quantitative research is numerical data.
• Qualitative Research is conducted with the aim of exploring and discovering ideas
used in the ongoing processes. As opposed to quantitative research the purpose is to
examine cause and effect relationship between variables.
• Lastly, the methods used in qualitative research are in-depth interviews, focus
groups, etc. In contrast, the methods of conducting quantitative research are
structured interviews and observations.
• Qualitative Research develops the initial understanding whereas quantitative
research recommends a final course of action.
2. Identify and critically review methods of data-gathering used in qualitative and
quantitative business research and the effect each will have on the outcome of your
research? [8 marks]
Answer
Data gathering is the process of collecting or measuring information on specific
parameters or variables. This is done to enable a researcher answer research questions or
to test hypotheses. This makes data gathering a very important activity in research.
There are various ways of collecting data. Each method has its advantages and
disadvantages, but some are more suited for certain scenarios than others. Also, the
method used for data collection depends on the type of research, whether it is
quantitative or qualitative.
Qualitative research is suited for research that tends to answer questions around purpose
or intent (why) and process (how). It seeks to get gain a deeper insight into a particular
subject and to generate a hypothesis which may later be tested quantitatively.
Qualitative research, therefore, tends to be more exploratory and unstructured. The
sample space for qualitative research tends to be smaller compared to quantitative
research. Face-to-face interviews is a very common method used to gather data in
qualitative research. The interviews are usually personalized, opinionated, unstructured,
and informal. This is useful for getting deep insight into a particular subject. Another
method used for qualitative data gathering is through focus groups. This is similar to faceto-face interviews, but it is done in a group setting. During focus groups session, a
moderator starts and guides a discussion on the subject and gathers information through
the contributions of participants. This is also useful in exploring a subject and getting
more descriptive data on the topic. Qualitative researchers may also collect data through
observation. In this method, respondents are observed in a process and data collected.
Normally for qualitative data, the researcher participates in the process and gets
firsthand information. This tends to be more reliable. Some of the key drawbacks of
qualitative data gathering methods are that it takes time to collect the data and data
processing is usually slow and sometimes even difficult to process. Data collection may
also be expensive.
Quantitative research usually makes use of empirical measurements to answer research
questions or test hypotheses. It tends to answer questions such as how many, who, what,
when and where. This method is more standardized or structured and tends to answer
questions objectively and reliably. It allows the researcher to quantify variables and
generalize based on the research. Unlike qualitative research, quantitative research
usually has larger and diverse sample sizes. This is to ensure that the data properly
represent the target population. The most popular method of gathering data is through
quantitative surveys. The questions in surveys are usually not close-ended to ensure that
responses are measurable. The answers are straightforward, can be either categorical or
within an interval or range. Other methods of data collection are longitudinal studies
where data is collected repeatedly over a long period on the same source of data. This can
last over years or even decades. The purpose is to discover course and effect based on
patterns derived from the data. The main disadvantage of this method is that data
collection can take very long to collect. In general, qualitative data collection is usually
not expensive. With the advent of technology, it is much easier to deploy surveys these
days. Also, data processing and analysis are generally not as difficult compared to
qualitative data. There are well known and established statistical methods that can be
used for analyzing the data. Some of the draw backs of gathering quantitative data are
that data may not be reliable due to the fact that respondent may respond to the surveys
independent of the researcher and the answers provided may be inaccurate.
3. Critically discuss Issues of validity, reliability, and ethical considerations in research. [10
marks]
Answer
In every research, a researcher chooses a method which will be used to conduct the
research. One thing to bear in mind is that every method chosen, one must consider its
validity, reliability and ethics raised by the method.
Reliability
Reliability refers to the consistency of a result if a method is applied under the same
conditions or circumstances. This is very important because for other researchers to be
able to verify the outcome of your results, the expectation is that a similar set up should
yield similar results. This makes the research reproducible and helps to ascertain any
possible errors that may have occurred during the execution of the research.
Middleton indicates that one way to check for reliability is by checking the consistency
of results across time, across different observers, and across parts of the test itself as
indicated by (Middleton, 2022).
For example, when you measure the temperature of water under similar conditions, the
thermometer measures the same temperature all the time indicating reliability. Where
the thermometer measures different temperatures under identical conditions, the result
may be considered unreliable.
Validity
Validity refers to the accuracy of a measurement of a chosen method. It essentially means
the extent to which a method measures what they are supposed to measure. Validity is
very important in research as it determines if the outcome of a research is accurate and
can be used to generalize, to a greater degree, that the outcome is also correct for the
general population and not just the sample used for the research.
Validity can be verified by checking how well the results correspond to established
theories and other measures of the same concept (Middleton, 2022).
In the earlier example of measuring temperature, if the thermometer gives different
result in a well-controlled identical condition, chances are that the thermometer is
malfunctioning. The results therefore can be considered as not valid.
One of key relationship between validity and reliability is that reliability does not
necessarily imply validity. For example, if the thermometer provides the same
temperature under identical conditions but the thermometer has not been properly
calibrated and hence it is measuring temperatures 5 degrees lower than the correct value,
the measurements will be reliable, but it does not mean it is valid. On the other hand, a
valid result is reliable.
Ethical Considerations
Ethics are very important when conducting research. A researcher must adhere to a
certain code of conduct, principles and practices that maintain the integrity of the
research, the validity of the results and more importantly protect the rights of the
research participants.
A researcher must ensure that research does not harm participants. Where there is
possible harm, the must be a deliberate plan to ensure that it is minimized and not
permanent. One consideration could be to let participants to be fully aware of the effects
that this research could potentially have on them and let them opt in.
In research, the privacy of participants is also very important. A researcher may need to
consider, during data collection, how participants are onboarded into research. This could
be through voluntary participation, consent, anonymous participation, or confidential
participation.
4. Critically analyse the various sampling strategies and techniques that could be used in
order to quantify the results. [10 marks]
Answer
When conducting research, it is difficult to interact with the entire population.
Therefore, a sample of the population must be picked for the studies. This is called
sampling. To improve the accuracy of the research, the sample must be representative of
the population. This is important to ensure that the results can be used for generalization
of the phenomenon or subject matter over the entire population.
There are two main categories of sampling methods namely Probability Sampling and
Non-Probability Sampling
Probability Sampling involves random selection, allowing you to make strong statistical
inferences about the whole group while non-probability sampling involves non-random
selection based on convenience or other criteria, allowing you to easily collect data.
(McCombes, 2022).
Some of the sampling techniques that may be used under probability sampling are:
• Simple Randon Sampling: In this technique, everyone in the general population
has an equal chance of being selected. This means the sampling frame used is the
entire population. As an example, in conducting research about how much people
earn in an organization, the population will be all the 500 workers in that
organization. In a simple random sampling, the sampling frame will be all HR
records on employees which will cover all 500 people, with everyone having
equal chance of being selected as a sample.
• Stratified Sampling: This technique is like Simple Random Sampling except that
the population is divided into subpopulations and then random sampling is
applied to each of the subpopulations. For example, in the example above, the
population may be divided into departments and then a random sample picked
from each department. This is important to ensure that there is a uniform
representation of each subpopulation.
• Cluster Sampling: Cluster sampling is similar to stratified sampling, but each
subgroup is instead designed to reflect the overall structure of the population.
Instead of dividing an office population into departments, for example, you’d
divide it into clusters of people, where each cluster has an equal mix of people
from different departments. Then, you select only certain clusters to study. This
approach can help to simplify the study of a large and distributed population if
each cluster contains only respondents from the same geographical area (Flynn,
2021).
Some of the sampling techniques that may be used under non-probability sampling are:
• Convenience Sampling: This is one of the easiest ways to sample a population,
though it might also be prone to errors. A convenience sample simply includes the
individuals who happen to be most accessible to the researcher (McCombes, 2022).
This method is also inexpensive however one of the key issues with this approach
is that it has the tendency to not be representative of the population. An example
is where a researcher, doing an opinion poll, administers questions to only people
from the same class he is in.
• Snowball Sampling: In this approach, a researcher will start with a few
participants and encourage those participants to refer people from their network.
This approach can be useful when researchers know very little about the group
they’re studying, to the point that they’re not sure of its size and have little contact
information for possible study participants. The downside of the approach is that
it’s easy to survey just one clique or social circle (Flynn, 2021).
• Voluntary Sampling: According to (McCombes, 2022), a voluntary response
sample is mainly based on ease of access. Instead of the researcher choosing
participants and directly contacting them, people volunteer themselves (e.g. by
responding to a public online survey). Voluntary response samples are always at
least somewhat biased, as some people will inherently be more likely to volunteer
than others.
In making a choice on which of the above techniques is suitable for a research, Flynn
makes an interesting argument which I agree with. For early, experimental research, a
non-probability sampling method may work well. These surveys are generally easier to
run than probability sampling-based surveys — though the data they produce won’t be
as quality or comprehensive. If high-quality data is needed, probability sampling methods
may be more appropriate. (Flynn, 2021).
5. List and explain different methods of data analysis employed in quantitative and
qualitative business research highlighting the suitability of each method for various
purposes. [10 marks]
Answer
The main objective of a every research is to answer some questions or find some deeper
meaning into a certain subject of phenomena. To achieve this objective, a researcher must
collect data and analyse the data to draw conclusions on the subject. The approach used
by a researcher in conducting research also determines the kind of analysis that could be
applied. Research maybe qualitative or quantitative. Below are the different methods
than could be employed depending on the type of research.
Quantitative Analysis
Quantitative analysis simply is the analysis methods used to analyse number-based or
quantifiable data. This is essential when conducting Quantitative Research. Quantitative
analysis heavily depends on statistics. There are two main types of analysis under
quantitative analysis namely Descriptive statistics and Inferential Statistics.
Descriptive statistics primarily focuses on being able to describe the sample. It provides a
way of summarizing the sample and finding patterns in it. Some of the statistical tests
that are done on the sample are:
Mean: numerical average
Median: the middle value or midpoint
Mode: most common or frequently occurring value
Percentage: a ratio as a fraction of 100
Frequency: the number of occurrences
Range: the highest and lowest values
Standard Deviation: an indicator of the degree of dispersion of a range of numbers
Skewness: how symmetrical or well distributed a range of numbers are.
Descriptive statistics are essential for describing the sample. It may appear basic, but it
enables a researcher to test how accurate a sample obtained is. Jumping straight into
inferential statistics without proper description or understanding of the sample could
result in errors.
Inferential Statistics on the other hand focuses primarily on being able to make
inferences or predictions of the entire population based on the sample. It seeks to draw
key insights into the population by determining relationship between variables, the
cause and effect and the degree of variability. Some of the well-known methods are
Correlation: which describes the relationship between two variables
Regression: which is used to estimate the relationship between variables. It used
to show the correlation between a dependent variable and a set of independent
variables.
Analysis of Variance (ANOVA): which is used to test how a group of variables
differ from each other.
Figure 1 Quantitative Data Analysis Methods (Humans of Data, 2018)
Qualitative Analysis
Analysing data in qualitative research tends to be difficult. This is mainly because of how
unstructured the data can be. Also, unlike quantitative where data analysis starts after
all required data has been gathered, qualitative analysis can begin as data is being
collected. This may further inform which additional data needs to be collected as the
research progresses. There are different methods that can be used to analyse data in
qualitative research, and I list some of the below.
•
•
•
•
Content Analysis: This method is used to get patterns in data collected in the form
of words, phrases, or images from multiple sources. Content analysis enable
research to determine the frequency of a certain phrase or word from the various
sources, drawing emphasis or a deeper underlying interpretation of such word or
phrase to the subject matter. An example is by identifying words or phrases that
highlights sentiments about a certain product.
One of the downsides of Content Analysis is that it is time consuming due to its
requirements of reading and re-reading content. Also, content analysis tends to
be concentrated on very specific timelines and therefore information prior or
after that timeline which may be relevant may be lost.
Narrative Analysis: As the name suggests, this method focuses on using the
stories shared by people through interviews or observations to answer research
questions. Researchers pay attention to how something is narrated, and the kind
of importance is placed on that. This is vital in getting the perspective of a person.
One of the weaknesses of this method is that its time-consuming process of
capturing stories or narratives means the sample sizes are also generally quite
small. This means that subject can be heavily influenced by social and lifestyle
factors. Also, it is difficult to reproduce the results in subsequent research and
hence it’s difficult to test the result.
Discourse Analysis: “Like narrative analysis, discourse analysis is used to analyse
interactions with people. However, it focuses on analysing the social context in
which the communication between the researcher and the respondent occurred.
Discourse analysis also looks at the respondent’s day-to-day environment and
uses that information during analysis.” (Humans of Data, 2018)
Discourse analysis can also be very time-consuming. the data to the point of
saturation – in other words, until no new information and insights emerge. But
this is, of course, part of what makes discourse analysis such a powerful
technique. (Warren, 2020).
Grounded Theory: Grounded theory attempts to explain why an event or
phenomenon occurred using qualitative data. This is achieved by studying similar
cases and using the data to explain the cause. Grounded theory essentially is used
to establish theories using data gathered for a particular subject. “The important
thing with grounded theory is that you go into the analysis with an open mind
and let the data speak for itself – rather than dragging existing hypotheses or
theories into your analysis. In other words, your analysis must develop from the
ground up (hence the name). (Warren, 2020)
Grounded Theory, just like the other methods, also has its weaknesses. One of
them is that unless a researcher knows very little about the research questions, it
is prone to bias in interpretation. However, going into research with little
knowledge about the subject matter and not bringing oneself up to speed on
current literature may also be unwise. But despite this, Grounded Theory is one
of the most popular qualitative data analyses out there.
TASK TWO [ 25 marks]
1. Use this data set to find the following statistical values: - [10 marks]
Answer
a. Mean
The question was not explicit on which of the variables that I should calculate the
mean for, so I decided to calculate it for Age and Loan Balance. The formular used is
Based on the formular above,
Mean Age = 21.73 (rounded to two decimal places)
Mean Loan Balance = 3,478.2
b. Median
The question was not explicit on which of the variables that I should calculate the
median for, so I decided to calculate it for Age and Loan Balance. The median is the
middle most number when the numbers are sorted.
Median Age = 21
Median Loan Balance = 2,890
c. Mode
Mode is defined as the value that occurs most often in a set of data. In the above data,
the mode of Age is 21. This age occurs 4 times in the age and that is the highest
frequency of any age.
d. Standard Deviation
The formular for standard deviation is
where n is the sample size, x is the sample mean, and xi is the ith element in the set.
Based on this formular
The standard deviation of Age is 2.712 (rounded to 3 decimal places)
The standard deviation of Loan Balance is 1814.804 (rounded to 3 decimal places)
e. Sample variance
The formular for Sample Variance is
where s2 is the variance of the sample, xi is the ith element in the set, x is the sample
mean, and n is the sample size.
Based on this formula,
The sample variance (s2) of Age is 7.352 (rounded to 3 decimal places)
The sample variance (s2) of Loan Balance is 3,293,512.743
2. Critically evaluate the relevance of regression models and hypothesis testing for a sample
population or data set like the table given in this question. Point out the pitfalls and
advantages of regression models and hypothesis testing for data sets. [15 marks]
Answer
A regression model is a statistical model that is used to estimate the relationship between
a dependent variables one or more dependent variables.
Regression models are very useful in testing hypothesis and simulating cause and effect
of variables. This is a very useful model because they are generally easy to understand.
The output model is usually an algebraic equation that is easy to use for predictions. The
goodness of fit of the model is well understood and provides good insight into how well
the model can be used to predict dependent variables accurately. Regression models tend
to perform as good as other forms of models, sometimes even better.
However, some of the shortcomings of regression models are that they are not sensitive
to erroneous data. This means that if the data used to build the model has input errors
such as duplicates, missing data, outliers etc., the model does not work properly. It is
therefore imperative that during data pre-processing, data input errors are removed.
Also, as the number of variables increases, the reliability of the model also decreases.
Again, regression models work with numeric data sets and not categorical data sets.
For example, the sample data can easily be used to build a regression model that tries to
predict the loan balance of a person based on age. However, other useful categories like
knowing the class and type of institution are important. Unfortunately, regression
models do not have a way of representing categorical data which is a downside.
TASK THREE [25 marks]
Conduct qualitative research on the paragraph below by answering the questions.
Some may argue that the benefits of eLearning outweigh the disadvantages and, it is yet to
be established if this is the path that future generations are destined to follow in all of facets
of life. The potential of the internet and new communications technology in connecting
learners and in advancing interactive and engaged learning is paving the way forward.
Technology, when applied reflexively, may enhance pedagogy, and affect learning outcomes.
The question remains that if eLearning is the way of the future, what happens when
technology fails, and it could. Systems and structures have been devised to catapult the world
into the next generation and may very well leave little room for free thinkers as people become
more reliant on technology. In an attempt to understand the benefits and shortcomings
of eLearning, the advantages and disadvantages need to be examined to ascertain if the
benefits outweigh the risk.
A. Create at least two or three themes from the above extract and provide justifications for
the themes generated. [10 marks]
The paragraph provides an interesting discussion about eLearning and the importance of
technology in the future. The paragraph also does not fall short to highlight some
scepticism about its possible shortcomings. There are some themes that cuts across the
paragraph, and they are discussed as follows:
Answer
1. eLearning, the future of education
The paragraph highlights eLearning as the future of learning. It argues that benefits
of eLearning outweigh its disadvantages. This argument enforces the notion that it is
the future of learning. This is because you will expect that more and more people
would want to be a beneficiary of its advantages, paving way for a new of acquiring
knowledge looking into the future.
2. Key Enablers
This theme points out the role of technology as an enabler for eLearning. The
paragraph highlights how internet and communication technology promote
interactive and engaged learning. It also talks about how applying technology has the
potential to improve teaching and learning outcomes. It is further argued in the
paragraph that technology is paving the way for this new future of learning
emphasising the point this theme makes as technology being an enabler of eLearning.
3. Key Concerns
The paragraph does not fail to highlight potential downsides and shortcomings of the
eLearning and the over reliance on technology. This clearly articulated in the
paragraph where it talks about the possibility of technology failing and poses the
question “…what happens when technology fails?”. It also points out that the over
reliance on technology could leave no room for free thinking. I see this as a concern
in that free thinkers are important in that open mindedness and a bit of scepticism is
healthy in the society. The paragraph finally advises the importance to explore
further to understand the pros and cons of technology and eLearning in our quest to
adopt it as new way of learning going into the future.
B. Based on the themes generated, complete a thematic analysis for qualitative research by
providing brief explanations against each theme and linking back to existing literature
on e-learning. You are required to cite at least four credible journal articles while
executing this analysis. [15 marks]
Answer
Introduction
Growing up in the early 90s in Ghana, a country located in the West Africa, the only way
to acquire knowledge was to go the classroom and a teacher will be there to teach you.
The teachers either wrote notes on the board for students to copy into their notebooks or
at advanced classes, they may dictate it for students to write. When it came to visual
representation, they had to draw these diagrams on the board for them.
Over time technology improved, print media became common and most of this
information were printed in textbooks for reference. In the last decade, the advancement
of internet and communication technology has improved the way content is delivered. I
am currently pursuing my master’s degree with Athena Global Education, and it is 100%
online. This begs the question, is eLearning the future of education?
This research explores eLearning as the future of acquiring knowledge and how
technology plays a role in improving this. It also looks at the areas of concerns and how
to address it going forward.
Methodology
In this research we examine a paragraph as our data source which discusses various
topics about eLearning. This research is qualitative in nature as we analyse the opinions
gathered from the paragraph of text.
We used thematic analysis as the preferred method for analysing the paragraph. This
method was chosen because it well suited for analysing data to obtain the views,
knowledge, opinion, or experience of others.
In using thematic analysis, the first step was getting familiar with the text. This was by
reading over and over the text to gain an understanding of its content and context. The
next step was to generate codes from the text. The table below shows how the data was
codified.
Interview Extract
Some may argue that the benefits of eLearning outweigh
the disadvantages and, it is yet to be established if this is
the path that future generations are destined to follow in
all of facets of life. The potential of the internet and new
communications technology in connecting learners and
in advancing interactive and engaged learning is paving
the way forward. Technology, when applied reflexively,
may enhance pedagogy, and affect learning outcomes.
The question remains that if eLearning is the way of the
future, what happens when technology fails, and it could.
Systems and structures have been devised to catapult the
world into the next generation and may very well leave
little room for free thinkers as people become more
reliant on technology. In an attempt to understand the
benefits and shortcomings of eLearning, the advantages
and disadvantages need to be examined to ascertain if the
benefits outweigh the risk.
Codes
uncertainty
beneficial
future of education
technology a key enabler
communication technology potential
interactive and engaging learning
enhanced teaching
positive learning outcomes
concerns
failed technology
over reliance
need further studies
The next step is to generate themes from the codes. This was done by grouping related
codes into a theme and giving it a theme name. The table below indicates how the themes
were generated.
Future of Education
Key Enablers
Key Concerns
Further Studies
Required
future of education
interactive and
engaging learning
enhanced teaching
positive learning
outcomes
technology a key
enabler
communication
technology potential
concerns
failed technology
over reliance
Uncertainty
need further studies
From this table, there were four (4) key themes that were derived from the paragraph.
Results
Following the thematic analysis done the following results were established:
1. eLearning is the future of education: The paragraph highlights eLearning as a possible
way of education for the future. It argues that benefits of eLearning may outweigh
its disadvantages. This argument enforces the notion that it is the future of learning.
This is because you will expect that more and more people would want to be a
beneficiary of its advantages, paving way for a new of acquiring knowledge looking
into the future. The paragraph also highlights some of the benefits that through
technology, eLearning brings to its users. It talks about a more engaging and
interactive learning that improves the way of teaching and learning. It also highlights
how this influences the learning outcomes.
(Goyal, 2012) cites Dobrin (1999) in his journal article that 85% of the faculty teaching
online courses felt that student learning outcomes were comparable to or better than
those found in face-to-face classrooms. This is remarkable considering the importance
of education in improving the quality of life. E-learning has emerged as a promising
solution to lifelong learning and on-the job work force training (Goyal, 2012).
2. Key Enablers: The advancement of the internet and other forms of communication
technology has been a driving force for the adoption of eLearning as an alternative
way of receiving education. The paragraph makes this abundantly clear. It also talks
about how applying technology has the potential to improve teaching and learning
outcomes. It is further argued in the paragraph that technology is paving the way for
this new future of learning emphasising the point this theme makes as technology
being an enabler of eLearning.
3. Key Concerns: The paragraph does not fail to highlight potential downsides and
shortcomings of the eLearning and the over reliance on technology. This
clearly articulated in the paragraph where it talks about the possibility of
technology failing and poses the question “…what happens when technology
fails?”. It also points out that the over reliance on technology could leave no
room for free thinking. I see this as a concern in that free thinkers are
important in that open mindedness and a bit of scepticism is healthy in the
society. The paragraph finally advises the importance to explore further to
understand the pros and cons of technology and eLearning in our quest to
adopt it as new way of learning going into the future.
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