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. References Flynn, S., 2021. 6 Types of Data Sampling (and the Best Practices for Each). [Online] Available at: https://www.insightsforprofessionals.com/marketing/leadership/types-of-datasampling-best-practices [Accessed 20 November 2022]. Fournier, A. B., 2022. What Is the Difference Between Quantitative And Qualitative Research?. [Online] Available at: https://www.verywellmind.com/what-is-the-difference-between-quantitativeand-qualitative-research-4588136 [Accessed 24 October 2022]. Goyal, S., 2012. E-Learning: Future of Education. Journal of Education and Learning, VI(2), pp. 239-242. Humans of Data, 2018. Your Guide to Qualitative and Quantitative Data Analysis Methods. [Online] Available at: https://humansofdata.atlan.com/2018/09/qualitative-quantitative-data-analysismethods/#:~:text=The%20two%20most%20commonly%20used%20quantitative%20data%20an alysis,helps%20researchers%20summarize%20the%20data%20and%20find%20patterns. [Accessed 22 November 2022]. McCombes, S., 2022. Sampling Methods | Types, Techniques & Examples. [Online] Available at: https://www.scribbr.com/methodology/sampling-methods/ [Accessed 20 November 2022]. Middleton, F., 2022. Reliability vs. Validity in Research | Difference, Types and Examples. [Online] Available at: https://www.scribbr.com/methodology/reliability-vs-validity/ [Accessed 19 November 2022]. Roshi, L., 2022. E-learning Statistics 2022: Online Learning Facts [+ Infographic]. [Online] Available at: https://codeless.co/elearning-statistics/#:~:text=Key%20ELearning%20Statistics%20The%20eLearning%20market%20is%20forecasted,time%20to%20lea rn%20than%20in%20a%20traditional%20classroom. [Accessed 9 Novemeber 2022]. Surbhi, S., 2018. Difference Between Qualitative and Quantitative Research. [Online] Available at: https://keydifferences.com/difference-between-qualitative-and-quantitativeresearch.html [Accessed 24 October 2022]. Warren, K. (., 2020. Qualitative Data Analysis Methods 101. [Online] Available at: https://gradcoach.com/qualitative-data-analysis-methods/ [Accessed 22 November 2022].
