Cameroon suffers from plights aplenty. If we consider each one, whether it be a shortage of clean water or inadequate transportation solutions, generations of improvements would stand on one foundational difficulty: insufficient education. Whereas in many developed countries where basic human rights are often satisfied, education can still be improved upon and often is needed. However, in developing countries, the need to improve education is compounded by a lack of basic human requirements. The children are often walking miles to school, grouped in a single classroom per grade level, and taught by a single teacher. These accounts come from interviews with individuals who attended primary and secondary school in Cameroon. Other issues cited were the inconsistent ability to afford uniforms and books leading to absenteeism, needed income from children working after primary school completion instead of continuing their education, and the lack of an appropriate teaching program.
In 2015, the literacy rate for the United States’ population was 86%. This amounts to 32 million adults (age 15 or older) who cannot read at or above a basic level (statisticbrain.com, 2015). This same statistic, as of 2012, for Cameroon was 71% (UNICEF, n.d.). The disparity between these values represents only one measure by which to gauge the severity of this wicked problem. A 15% difference is an ocean to cross in terms of what it takes to improve a country’s literacy by a single percentage point.
Moving forward with this project relies partially on having accurate current data, but mostly on implementing a program in lieu of a grand research endeavor to gain this current data. The beginning steps are, at best, an educated guess. I strive to put as much education into the guesses as possible, however, with conditions as they are, it would take a grand scale of monetary investment to compile the necessary data and further verify it.
There is an innate flaw in all data collected across the globe: the human aspect. Measurements in science experiments are broken down into their most basic elements in order to remove as many biases that ‘human error’ can introduce into the study. When it comes to education, or any entity where self-reporting may be involved, there comes a desire to falsify the data. Without incentive, there is little hope of improvement. However, with incentive, humans can put their own self-interests in front of that of the study.
Bearing this in mind, there are some difficulties in measuring education. There are several items to consider in collecting data. In order to properly measure education improvement, I will need to measure literacy rates, general test scores, and application/acceptance to higher learning. Obviously this could be done over the course of a single year, but some data will not show the improvement potential that the decision variables could attain. For instance, measuring the application numbers to higher education institutes after only a single year of improvements in all secondary schools may show only a small change, or most likely, no significant change at all. If a student has spent 10 years in a subpar education system and given only a single year of improvements, I would not expect significant results. If a student begins in the improvement program in primary school and continues through the end of secondary school, they would be given a better opportunity to absorb the improvement programming.
All of the results cannot just be measured across a decade to determine when a change needs to take place in the improvement programming. After consulting with individuals who work in the education training industry, a new program should be implemented three to four months before making changes. In this small time frame, detailed measures must be used to determine significance of results.
In using previously collected statistical data from international organizations, it would be quite difficult to verify this information. While I could speculate what I would do with $1 million and a plethora of available human research hours, it is better for me to realistically determine what I can do. Assuming my budget to be nil, I am reliant only on available hours.
As discussed earlier, humans may have a desire to falsify their data in order to make themselves look better. In a single example, the Democratic People’s Republic of Korea, better known as North Korea, is cited in every study I found as having a 100% literacy rate as of 2008 (UNESCO, n.d.). While not impossible, it is unlikely that every individual age 15 and older is capable of reading in this country. The mere reporting of 100% for the countrywide literacy rate suggests some deception. It is this type of deception amidst governments being caught in scandals and corruption that needs to be considered when analyzing global data statistics. It is probably better for my own programs to start measures anew that will collect what is needed.
On a similar note, teachers looking to increase their career longevity and overall status in education may also be inclined to falsify data on behalf of students’ improvements. I do not have a thorough solution for this at present, but it is an aspect of which I am aware and will aim to mitigate.
The specific measures will almost entirely be numerical, given the nature of test scores and literacy rates. For more dynamic measures, such as higher education pursuits, categorical data will need to be compared. This can include in- versus out-of-country applications as well as the establishments that receive applications.
Education is not created equal across the world. Cameroon’s data could be compared to other countries of similar size and demographics; however, this can be misleading. Each country has its own culture and with that comes a unique set of goals and values. Thomas Jefferson once wrote that all men are created equal. Despite his excellent choice of higher learning, the creation of man is perhaps where the equality ends. While it is romantic to think there is a universal language in education that can aid every human, this could be a mistake. Having taught overseas, I have first-hand experience with the stylistic differences between two countries’ views on education. Learning foreign languages, for instance, is entirely different between the United States and Europe. Each population shows value in different ways and as they are free to learn in their own style, it would perhaps be irresponsible to compare them to each other. In perhaps too philosophical of an ideal for business analytics, Ernest Hemingway once wrote “There is nothing noble in being superior to your fellow man; true nobility is being superior to your former self.” I believe the proper analysis of the data to be collected should remain in comparison only with previous versions of itself. This will show where a particular group is on their own ladder. This is a single method to mitigate individuals or entire populations from looking at how far their neighbors have climbed up the ladder. While this project is attempting to tackle the issue of education, the issues of global benevolence and mutual aid carry their own wickedness.
The comparison of this data will be slow at first, as all data is starting from ground zero. It is important to bear in mind, however, that this project is not for immediate results, but rather to improve generation after generation into the future.
Specific tests will include clustering data by schools, regions, gender, and ages. This will provide any trends that may occur. Additionally, tests of significance with 90 or above confidence levels will be needed to show correlation between the various variables being manipulated. In one element of the program, teachers will go through training courses to learn how to specifically teach their assigned grade levels. Test scores will be compared through these t-tests to show whether the programs are having a significant effect. Further teacher training, combined with public awareness initiatives on these programs and a push to better provide financially viable uniforms and school supplies will be tested on absenteeism through the grade levels. Within the toolbox of correlation tests, I can drill down with samples of student and teacher populations to better determine the cause of such issues as the lack of teacher education, absenteeism, proper government funding and student performance.
In continuation with financially viable uniforms and school supplies, the Dantzig’s simplex algorithm can be used to solve where to purchase items in order to minimize costs. I would need the supply costs from various vendors as well as the counts of student populations. This tool would also provide the ranges in which variances could occur in real life where no alterations would need to be made to the purchase orders. Student populations along with order completions would fluctuate simply by the nature of both of those factors.
Once results begin to come in, the iterations of the programs may start. Before improving upon any particular element of the program, the measurements and metrics must be reviewed for proper collection methods. For example, should one teacher be significantly more lenient in grading than another, improvement results can be thrown off. The importance of this will be part of the training program, but the final analysis will still need a check on this to ensure whether an improvement is actually needed or if the current program may continue.
These improvements, as stated, will be assessed every quarter for adjustments to each element of the program. In addition to numerical data collections, improvements will also need to be based on survey results. Immediate results may show statistical improvement, however, a survey that signifies gross unhappiness with the program could predict long-term failure for the program. As a temperature gauge, my interviews with Cameroonians thus far have signified not only a desire for education improvement, but a real sense of urgency in the matter.
Future sustainability is the ultimate impediment to this wicked project. A simple five-year improvement plan can be completely undone in the subsequent ten years if the project does not evolve as the people do. The data collection will need to become more sophisticated, with “real-time” results capabilities. This will help make predictions on where the program’s blind spots are. At present, gender is a very specific issue in education for developing countries. Girls are fifty percent more likely than boys to never have the opportunity to read and write in primary school (UNESCO, 2016). This is simply one issue which is known, but has not been widely improved upon over the last two decades. Similar issues may arise which need to be recognized as they are developing.
 These measurements are crude at best, as there are numerous websites citing varying levels of literacy for both of these countries. The values given represent statistics that were found on multiple websites, while single-occurrence percentages were ignored.
Illiteracy Statistics. (2015, December 2). Retrieved August 21, 2016, from http://www.statisticbrain.com/number-of-american-adults-who-cant-read/
UNESCO. (n.d.). Education. Retrieved August 21, 2016, from http://data.uis.unesco.org/Index.aspx?DataSetCode=EDULIT_DS
UNESCO. (2016, July). Leaving no one behind: How far on the way to universal primary and secondary education? [PDF].
UNICEF. (n.d.). Statistics. Retrieved August 21, 2016, from http://www.unicef.org/infobycountry/cameroon_statistics.html#117