Introduction
Learning is a continuous process that does not end with age. As such, there occurs an impasse with regard to whether or not old people can learn new lessons. The question that should be asked is what kind of learning is open to the aged, after defining what comprises old age, middle age and youth, then research can be done in this regard. Therefore, learning can be viewed as comprising academic knowledge or the use of certain motor skills that had previously been lying dormant or in other words, had remained underdeveloped due to being unused. In this study, no distinction was made regarding the two forms of learning or any other form of learning whatsoever. Considering, for example, people learning from unsavory news (Moutsiana et al, 2013). In this regard, the learning process was taken in its entirety, and three age groups were considered for comparison purposes. The youths were taken to be under thirty five years, middle age was taken to be thirty six to fifty years and the aged were fifty one years and above. Furthermore, there was no differentiation between male respondents and female respondents during the data collection period as each of the sexes were given an equal chance of participating in the study. The study was guided by the hypothesis that aging significantly affects learning ability in a negative manner. The null hypothesis, on the other hand, was that there was no significant effect of aging on learning ability.
There happens to be an enormous number of people who have advocated for the transformative nature of the computer in the school learning process, and the trend is such that school going children are introduced to computer based pedagogy at a tender age (Buckingham, 2007). It may be inferred from these statements that learning is best done when a person is young as opposed to learning at an older age. However, such statements do not in any way imply that the aged people cannot imbibe new knowledge, rather, the question that arises when dealing with lessons and knowledge acquired when one is old is where that new knowledge can be applied. As a young learner, one would have a plethora of places in which the acquired knowledge can be applied, not so with the aged. Therefore, aged people need to have a paradigm shift in this respect.
Data collection
Data collection was done through literature searches for previously done studies into the capacity of learning for the three aforementioned age groups. The sampling was not done at random, but rather by purposive means through which only those articles whose titles had the keywords ‘learning’ and ‘aged’ were selected for the study. These articles were set aside for further analysis of whether they were carrying data obtained from studies of human subjects or studies involving animals like rats and/or rabbits. Any data obtained from animal subjects were privily discarded as not being pertinent to the current study. The data was obtained online through search engines like Google scholar, yahoo, and Bing. These search engines can be easily availed through the worldwide web, which was available throughout the data gathering phase of the research. Books written by psychologists were also consulted, since the learning process was considered to be a psychological process, and it is highly likely that previous studies had bearings on the lives of people having psychological problems. With all these resources being available in the local public library, it was not necessary to hold a consultative meeting with a professional in the field of motor and sensory development. Motor and sensory development also affects the process of learning in different age groups. To avoid bias in the data collection period, even encyclopedias and books were consulted purposively and exclusion and inclusion criteria were used for the demarcation of the scope within which the current study would proceed. By introducing the concept of inclusion and exclusion, bias in the data collection was avoided. For instance, literature detailing the study of people who were mentally ill was excluded, while data obtained from people who were relatively mentally healthy were given priority.
Data Organization
The data obtained were arranged according to the charts shown below:
:
This pie chart shows the number of participants who would continue learning well into their old age
This pie chart details the percentage per group of participants who would continue learning well into their old age.
Data Analysis:
Taking the assumed mean age (A) for people who can continue learning well into their old age to be 22 years of age and the class interval (CI) to be 3, the mean age for such individuals was obtained as follows:
Arithmetic mean, X=A+fd×CIf
=22+-84×349
=22-5.143
= 16.857 years
Standard deviation, S1= √X-X2n-1
It follows that S1= √{(∑(X-X ̅ )^2 )/(n-1)} = 159.978/(49-1)
= 159.978/48
= 3.333
It was necessary to obtain the standard deviation from the mean because this value would help in the hypothesis testing, which was the next activity after having done the descriptive statistics as shown in the pie charts and tables above. The tables show that the sample size was large enough to warrant a z test over a t-test. Furthermore, since the significance level was given as 0.05, the critical values would be such that when using a two tailed statistic, ±z.025=±1.96. This had the implication that z values lying within the given confidence levels would cause the null hypothesis not to be rejected, and the reverse would also be true.
Computing the value of the z statistic was done using the formula:
z=X- μ0σ√n
±1.96 = (16.857 - µ0)/ (3.333/±7)
µ0 = 16.857 – (13.72/3.333)
µ0 = 16.857 – 4.116
µ0 = 12.741
Conclusions
Since the value of µ0 was less than that of the mean age and fell outside the region within which the null hypothesis should have not been rejected, it was concluded that aging significantly affected people’s learning abilities in a negative way. The data did support the research question and none of the questions remained unanswered.
References
Buckingham, D. (2007). Beyond Technology: Children's Learning in the Age of Digital Culture. Cambridge: John Wiley & Sons.
Miller, M., & Sorby, S. A. (2015). e-Learning Modules for Improving Lifelong Learning Ability. American Society for Engineering Education , 9.
Moutsiana, C., Garrett, N., Clarke, R. C., Lotto, R. B., Blakemore, S.-J., & Sharot, T. (2013). Human development of the ability to learn from. PNAS , 16396-16401.
Wu, H. G., Miyamoto, Y. R., Castro, L. N., Oleczky, B. P., & Smith, M. A. (2014). Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nature America , 312 - 324.
