CHAPTER V
PRESENTATION, INTERPRETATION AND ANALYSIS OF DATA
This chapter provides the results, discussion, and analysis of the study. It is organized according to the specified statement of the problem. First on the list is the nature of the respondent, followed by the respondent’s academic and computer background. Scores on the conducted retention ability test is discussed on the later part of the chapter and so as how the computer usage affects it. Relationships between variables are not only discussed and interpreted but is also proven with the help of the corresponding statistical tests. Two statistical tests were used, the T-test and the Pearson Product-Moment Correlation. Finally, other affecting factors on the retention ability are also discussed on the last part. These affecting factors include the teacher, environment and the topic of the lesson/ relayed information.
Respondent’s Profile
The respondents were 16-18 years old, second year Bachelor of Science in Computer Science students of University of the Philippines – Cebu College. Basically this study didn’t tackle much of who the respondent would be as long as he/she could meet the basic requirement which is to be a second year Computer Science student of the University of the Philippines – Cebu College.
In the first questionnaire, the respondents were asked about their grades in CMSC 11, CMSC 21 and so as their General Weighted Average to serve as a background on how the respondent’s have performed during the earlier days in their college life.
Based on the figure on General Weighted Average of the respondents, we can see that almost all of them have had a good standing during the Second Semester of Academic Year 2010-2011. Since CMSC 21 is a follow up subject for CMSC 11, we can track down the changes of the student’s performance in his one year stay in UP Cebu. The respondents had an average of 1.65 on their grades in CMSC 11.
Null Hypothesis (Ho):
Using the paired T-test which was previously defined on the study’s methodology, we will arrive at a P-value of 0.5599. The p-value measures consistency by calculating the probability of observing the results from the sample of data or a sample with results more extreme, assuming the null hypothesis is true. The smaller the p-value of the unpaired t-test, the greater the inconsistency. Traditionally, researchers will reject a hypothesis if the p-value is less than 0.05. Sometimes, though, researchers will use a stricter cut-off (e.g., 0.01) or a more liberal cut-off (e.g., 0.10). The proponents have only used the 0.05 cut-off since the number of respondents is only limited. The general rule is that a small p-value is evidence against the null hypothesis while a large p-value means little or no evidence against the null hypothesis. Please note that little or no evidence against the null hypothesis is not the same as a lot of evidence for the null hypothesis. By conventional criteria, the P-value of 0.5599 is considered to be not statistically significant since it is greater than the cut-off which is 0.05. Thus, the difference in the grades of the respondent’s in CMSC 11 and CMSC 21 are not that varied in order for it to be statistically significant. Confidence interval which is the mean of CMSC 11 grades minus CMSC 21 grades equals 0.0750. This means that the means of the two variables (grades in CMSC11 and CMSC 21) are almost equal that will only bring insignificance in comparing the two.
The result tells us that earlier signs of retention have not been seen in the respondents’ academic performance. This can imply two things: (1) that the students are intelligent enough that they can easily cope with their studies even if they may have less memory capability, (2) that the nature of the course does not need much of the memorization skills of the respondents. We still conclude that there is no significant difference between the respondents’ grades in CMSC 21 and CMSC 11.
Number of Years Since The First Use of Computers
The Washington Times (2004) stated in one of their articles that the greater the exposure of children (1-3 years old) to televisions or any media gadgets the greater it affects their ability to focus by the age of 7. 
Based on the given results of the survey, the respondents have already used computer for more or less 9.15 years which also means that they have accessed such media devices when they were still about 7.85 years old.
Mathematically speaking, with the use of Pearson Product-Moment Correlation we would arrive at a value of 0.015813339. This value is indeed positive but as we see this decimal value which has a significant value that starts on the hundredths position means that the correlation is very close to zero. A correlation of 0 means there is no linear relationship between the two variables. Then again, it would lead us to having the years of computer usage to be statistically insignificant in this particular study.
Studies such as the one stated in the Washington Times suggested that there are certain behavioral changes on toddlers when they are frequently exposed to media devices. But if we are to dig deeper we can see differences because the context of the article was based on America. Computers were first introduced during the 80's and 90's in the Philippines – WikiPilipinas (Philippine Encyclopedia) as compared to the States on which the first commercial computer was introduced during the early 50’s. Americans have already incorporated the use of computers earlier in their lifestyle as compared to Filipinos.
Financial state of the respondent’s family might have also affected the results since three respondents were from Bracket E1 (PhP 80,001- PhP 135,000 – annual family income), one from Bracket D (PhP 135,001 – PhP 250,000), one from Bracket C (PhP 250,001 – PhP 500,000), and the rest were from Bracket B (PhP 500,001 – PhP 1,000,000). Financial state affected the results since only the richer people can afford computers especially in a third world country like the Philippines.
Computer Usage in Home and School
According to Severin and Tankard (as cited by Quijano, 2000), the greater the media dependency, the greater the possibility for the audience’s feelings, behaviors and mostly the audience’s intellects to be changed. This dependency can be shown through the number of hours that the respondents spent in using their computers. The fourth part of the study’s questionnaire was all about computer usage. Specific time durations were asked to the respondents for five days. The results are presented below:
The respondents use an average of 4.5 hours in using their computers at home while only 1.5557 hours at home. This shows that even if there is internet connection at school, students would still prefer to use personal computer/laptops at home.
Based on the graph above we can see that there are five average users which means that they use their computers for about 3.1 hours to 5 hours per day, three of the respondents were more than average which means that they use their computers for about 5.1 hours to 10 hours per day and only one of the respondents was less than average which means that he/she uses the computer for about 0 hours to 3 hours per day. Only one respondent was a super user which means that he/she uses the computer from 10 hours above per day.
Based on the number of hours of computer usage, the respondents were then divided into two groups. Group A are the frequent users while Group B was the not so frequent users.
A theory developed by Ball-Rokeach and De Fleur(as cited by Quijano, 2000) called dependency theory which stated that people have various reliance on media and these dependencies may differ from individual to individual. This can be well seen in the data presented above. Almost half of the respondents were more inclined to Home usage while the other half was on School Usage. The respondents were then divided into two. The first group which is Group A frequently uses their computers at home rather than in school while the second group is the other way around. Group A consumes 84.54% of their computer usage at Home while 15.42% at school. Group B uses 60.67% in school while 39.33% in their homes.
Retention Ability
A brief recall from the methodology, three tests were conducted to each of the respondent to measure their retention ability. From a class simulation where there is an acting teacher, the students were given a test on how well did they retain the information that was previously discussed on the class discussion. The three types of test were given on different time intervals. The first one was for 15 minutes, 1 hour and the last one was for one day.
Presented on the table above are the scores of the respondent from each group during the 15-minute interval retention ability test. Group A had an average of 87% while Group B had an average of 88%. Based on the recency effect theory of Miller and Campbell (1959), our mind retains information that is recently gathered from certain events and observations in our surroundings. Thus the more recent the information is, the greater chances that it will be retained. Since this certain test was conducted after 15 minutes, it is expected that there shall be not that much difference on the means of the scores between two groups.
Respondents | Scores on Retention Ability Test (1Hour) |
A1 | 80 |
A2 | 100 |
A3 | 20 |
A4 | 75 |
A5 | 90 |
B1 | 100 |
B2 | 95 |
B3 | 85 |
B4 | 100 |
B5 | 100 |
After the 15 minute interval test, another test with one hour interval was conducted the results are as follows:
Table 2.2 Scores on Retention Ability Test for 1 Hour
Group A had an average of 73% while Group B had an average of 93%. Based on the recency effect theory it is expected that that all the respondent’s scores shall decrease. Since Group A are more frequent computer users their scores implies that there is indeed a lesser retention that might be due to computer usage. The researchers did not expected an increase of score in Group B. This increase might be due to other factors such as the teacher, topic, environment or maybe due to the nature of the respondents on which it has been previously stated that Group B uses computers lesser than Group A. Either way, the results on this part of the test showed that the respondents that were more avid computer users had lower retention ability scores.
After the 15 minute and the 1 hour interval tests, the researchers have also conducted another test which was conducted after 1 day from the class discussion. Here are the results for the final test:
Scores on Retention Ability Test (1Day) | |
A1 | 70 |
A2 | 35 |
A3 | 25 |
A4 | 35 |
A5 | 55 |
B1 | 80 |
B2 | 75 |
B3 | 80 |
B4 | 100 |
B5 | 100 |
Table 2.3 Scores on Retention Ability Test for 1 Day
Group A had an average of 44% while Group B manages to have maintained a nice score of 87%. The results all goes back to the recency effect theory. Severin and Tankard (as cited by Quijano, 2000) stated that the greater the media dependency, the greater the possibility for the audience’s feelings, behaviors and mostly the audience’s intellects to be changed. This can obviously be shown as Group A’s score is relatively lower compared to Group B which were less exposed to computers.
Summary of the results and significant correlations can be seen on the next page.
Time (min) | Retention Ability (%) |
15 | 87.5 |
60 | 84.5 |
1440 | 65.5 |
CORR = | -0.995174055 |
Table 2.4 Pearson Product-Moment Correlationfrom Time and Retention Ability
Retention ability for 15 minutes was 87.5% and after 60 minutes the score decreased to 84.5 % only. After one day interval or about 1440 minutes, the results of the retention ability test of both groups have decreased to 65.5%.
Tables 2.1, 2.2, and 2.3 present scores of the different respondents on the different time-framed tests given to them. As what we can see on table 2.4 there is a -0.995174055 correlation between the span of time and the scores on the retention ability test. Therefore we could say that the amount of information that has been gathered from previously discussed topic lessens as time passes. The -0.995174055 is a very strong negative correlation because it is close to -1. Based on the standards of the Pearson-Product Moment Correlation a negative value means that there is an inverse relationship between the two variables time and computer usage. A number close to the limits (-1 and 1) means that the relationship between the two variables is statistically significant.
The negative relationship between time and retention ability can be proved with the help of the recency effect theory on which it was stated that a person can remember more recent information as compared to older ones.
Retention Ability and Computer Usage in Home
This part of the chapter talks about the retention ability and computer usage in home. Based on Figure 4.1, respondents in Group A uses most of their time using the computers at home that’s why this part is specific only to the behavior of Group A.
Based on the pie chart on the previous page, Group A respondents uses most of their time using the computers at home. Located on the right side of the figure are their scores on the retention ability test. It can be seen that from the 15 minute span, they got 87% correctly, while in the one hour span they got 73%, and finally on the one day span, they got 44%. Based on the numbers stated, we can obviously see that there is a great decrease on the retention ability of these students as time passes.
This leads us back to the dependency theory developed by Ball-Rokeach and De Fleur(as cited by Quijano, 2000) which stated that every individual has unique dependencies on media devices. Basically, people who use computers at home are most likely to use it until they get bored. Since there is no time constraint between usage after usage, these people tend to use it all the way until their eyes gets worn. As what the researcher’s have observed on the respondents, they use their personal computers at home not just for academic purposes but for personal satisfaction also. This includes staying connected to their family playing, stalking, social networking, reading online articles, watching music videos, downloading videos and many more activities that don’t have a great positive effect upon the respondent’s well-being.
Retention Ability and Computer Usage in School
This part of the chapter talks about the retention ability and computer usage at school. Based on Figure 4.1, respondents in Group B use most of their time using the computer at school and by that, this part of the chapter is dedicated to Group B only.
Based on the presented figure on the previous page, the respondents in Group B spend 60.666% of their computer time in school while only 39.334% at home. It can be seen that from the 15 minute span, they got 88% correctly, while in the one hour span they got 96%, and finally on the one day span, they got 87%. Group B is able to maintain a good level of scores as compared to Group A.
Respondents in Group B comprised students with laptops. Some of the respondents do not have a free Wi-Fi access on their boarding house which means that they need to find a cheaper place to access the internet, these might include internet cafes, Starbucks or for some, they just use the free internet access in school. Free internet access in School is only limited thus, they only have to do their school-related tasks if they want to minimize their expense. Another common situation for these respondents with laptop is when they don’t have classes and they still have a lot of vacant time. In this situation, even if the respondent doesn’t have internet access, they still can use their laptops. Basically, in this situation the respondents are coding some java programs on their spare time. Among all the respondents in Group B, there was only one who didn’t have a personal computer/laptop with him. Based on his current situation, he uses the laboratory time in the Computer Science subjects to do most of his academic tasks. Since laboratory time is only limited to one and a half hour only, the respondent said that he tends not to involve any unrelated tasks while inside the laboratory. He added that, he cannot even do all sorts of tasks for that given time so he still goes to the internet café to finish his tasks. Indeed, there is a greatly varied dependency among these respondents but one thing is for sure, their dependency was wisely used.
Retention Ability and Overall Computer Usage
In this part of the chapter, the effects of computer usage on retention ability will be tackled. Furthermore, this part of the chapter is also divided into sub-topics in order for the readers to be guided with the flow of presentation and interpretation of data. First would be the retention ability of both groups, then significance in the results for computer usage and retention ability between Group A and Group B will be presented and interpreted.
As what we can see on the graph above, Group B has a better retention than Group A. Group A or the group with the higher computer usage has only scored an average of 68% on the 3 retention ability tests. Group B or the group with lesser computer usage has scored 90.33% on the retention ability tests. Later on in this part of the chapter we shall find out if these implications are statistically correct and if it is also significant. If we are to calculate the standard deviation or the measure of the dispersion of a set of data from its mean based on the formulas and equations that was stated in the study’s methodology, we will arrive at Group A = 21.931 while Group B = 4.9328. This implies that Group B has a closer set of values, which can also be seen on Figure 3.1. A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values. It means that most of your data falls within a distance of 21.931(Group A) and 4.9328(Group B) from your mean, and that values outside of that range are not of much interest in regard to the statistics.
This specific part aims to find out if the values between the two groups are statistically significant.
Respondent # | Group A | Group B |
1 | 5.93 | 4.4 |
2 | 4.88 | 4.5 |
3 | 13.68 | 1.4 |
4 | 7.6 | 4.3 |
5 | 9.66 | 4.9 |
Table 4.1 Computer Usage of Each Respondent in Group A and Group B
Null Hypothesis (Ho):
There is no significant relationship between the number of hours of computer usage of Group A and Group B.
If we are to use the t-test that was previously defined on this study’s methodology, it will lead us to having the two-tailed P value equals 0.0295. By conventional criteria, this difference is considered to be statistically significant. Based on the general rule on T-test, “a small p-value is evidence against the null hypothesis while a large p-value means little or no evidence against the null hypothesis”, we shall then reject the null hypothesis. Since 0.0295 is a number lesser than 0.05 which is our cut-off then we accept the value to be statistically significant.
Respondent # | Group A | Group B |
1 | 80 | 93.33 |
2 | 73.33 | 81.67 |
3 | 38.33 | 85 |
4 | 66.67 | 91.67 |
5 | 81.67 | 100 |
Table 4.2 Retention Ability Scores of Each Respondent in Group A and Group B
Null Hypothesis (Ho):
There is no significant relationship between the retention ability scores of Group A and Group B.
Calculating for the value of using unpaired t-test, we shall arrive to a two-tailed P value equals 0.0305. Since the p value is less than the 0.05 cut-off then by conventional criteria, this difference is considered to be statistically significant. We shall then reject the null hypotheses. Therefore, there is a significant difference between the retention ability scores of the respondents from Group A and so as from Group B.
Using the Pearson Product-Moment Correlation we will now then correlate the values of the two given variables which is Computer Usage and Retention Ability and to check if there is a statistical significance between the two.
Table 4.3 on the next page presents the computer usage (in hours) and retention ability scores (in percentage) of each respondent.
Respondents | Computer Usage (Hours) | Retention Ability (Percentage) |
R1 | 5.93 | 80 |
R2 | 4.88 | 73.33 |
R3 | 13.68 | 38.33 |
R4 | 7.6 | 66.67 |
R5 | 9.66 | 81.67 |
R6 | 4.4 | 93.33 |
R7 | 4.5 | 81.67 |
R8 | 1.4 | 85 |
R9 | 4.3 | 91.67 |
R10 | 4.9 | 100 |
Correlation = | -0.77664286 |
Table 4.3 Retention Ability Scores and Computer Usage of Each Respondent
Based on the standards of Pearson Product-Moment Correlation,a correlation of -1 means that there is a perfect negative linear relationship between variables. Having a correlation value of -0.77664286 which is closer to -1 means that there is a negative relationship between the computer usage and the student’s retention ability. The more often the student uses the computer the more likely he is to experience lesser retention.
The scatter plot shown on the next page depicts a negative relationship between the number of hours of computer usage and retention ability. On the x-axis of the graph is the number of hours of computer usage and on the right is the scores on the retention ability test. We can see on the figure on the next page how varied the plotted points of each the respondents.
Even if the points are varied, they are still closely located to the line of Pearson’s Correlation which is -0.77664286 and by that itself, we can say that the data are statistically significant and since the slope of the line is negative, negative relationship between the two variables will then be easily observed.
Respondent’s Field of Interest
In order to determine the true effects of the respondents’ varying fields of interest on their retention ability, the proponents have presented a list of topics with popular interest and asked the respondents if which one would they favor. Located on the next page are the results of the conducted survey.
The fields of interest that was presented were fashion, photography, music, dance, religion, artificial intelligence, computer gaming, blogging, social networking, books, animals, social relationships, business, food and some other fields that the respondents might want to add. The top three topics were Music with nine votes, Computer Gaming with eight votes and Social Networking with nine votes. The three uninteresting topics were Business with only one vote, Blogging with 2 votes and Animals with 2 votes.
The results of this part were used by the proponents as a tool of selecting which topic they shall discuss during the class simulation.
Teacher and the Student’s Retention Ability
This part of the chapter will basically discuss how the teacher or the teaching style can affect the student’s retention ability.
Recalling the treatment of Group A and Group B. Group A was given a teacher who discusses the topic clearly and who uses visual aid as a tool in making the respondents understand more. Group B on the other hand was given a teacher whose approach was more on a textual basis. Group B’s lecturer jus gave an overview of the subject and gave the respondents some materials to read.
To further expound the differences between the two groups, a comment box was provided on the questionnaire for the respondents to write their feedbacks. In the comment area of the questionnaire, most of the respondents from Group A said that their teacher was effective and the use of visual aids have helped them in understanding the topic. Contrary to what Group A said, Group B was more negative on their comments. Some even said that the teacher was lame and uninteresting.
If we are to compare the respondent’s rating on the teacher and student’s retention ability with the use the Pearson Product-Moment Correlation, we would arrive at the following correlation values for Group A and Group B. (see Table 5.1 and 5.2):
Respondent | Rating | Retention Ability |
A1 | 10 | 80 |
A2 | 10 | 73.33 |
A3 | 9 | 38.33 |
A4 | 9 | 66.67 |
A5 | 10 | 81.67 |
CORREL = | 0.803198645 |
Table 5.1Group AStudent Rating on Teachers and Retention Ability (Correlation)
Respondent | Rating | Retention Ability |
B1 | 4 | 93.33 |
B2 | 5 | 81.67 |
B3 | 2 | 85 |
B4 | 6 | 91.67 |
B5 | 5 | 100 |
CORREL = | 0.328086402 |
Table 5.2 Group B Student Rating on Teachers and Retention Ability (Correlation)
Group A’s teacher used an audio-visual approach in teaching that might have lead to a very high positive correlation among the teacher rating and student’s retention ability. A correlation value of 0.803198645 means that the more comfortable the student is to the teacher, the greater the retention ability would be.The same implication goes with Group B. Group B’s teacher only used the textual approach where the student’s were just asked to read an outline of notes for a certain topic with a bit of pure reading which might have lead to a low but still positive correlation of 0.328086402.
Environment and the Student’s Retention Ability
This part of the chapter will basically discuss how environment can affect the student’s retention ability.
Both the simulated class lecture and the exam proper for Group A were conducted inside the Computer Science Laboratory (AS 243). Basically the environment is favorable for class discussion since the place is peaceful and clean. Group B’s environment was noisy and very distracting since the class lecture was conducted on another Computer Science Laboratory (AS 241) but unlike the other Group, the respondents from Group B was staying with their classmates who are so noisy due to pressure on the deadlines of their school tasks.
Using the Pearson Product-Moment Correlation to compare the results on the respondent’s rating on the environment and their retention ability scores; we will arrive at these following conclusions:
Respondent | Rating | Retention Ability |
A1 | 9 | 80 |
A2 | 10 | 73.33 |
A3 | 9 | 38.33 |
A4 | 10 | 66.67 |
A5 | 7 | 81.67 |
CORREL = | -0.270443858 |
Table 6.1 Group A Student Rating on Environment and Retention Ability (Correlation)
Respondent | Rating | Retention Ability |
B1 | 5 | 93.33 |
B2 | 5 | 81.67 |
B3 | 8 | 85 |
B4 | 6 | 91.67 |
B5 | 5 | 100 |
CORREL = | -0.390238084 |
Table 6.2 Group B Student Rating on Environment and Retention Ability (Correlation)
As what we can see on the table, Group A which is having a more favorable environment which is silent and calm had given a negative correlation of -0.270443858.Group B also which had a stressful environment had given -0.390238084. This means that the noisier or the more interactive the environment is, the greater the retention ability of the students will be.
This negative correlation might be due to the reason that some students might want to work on a more interactive environment. Another factor might be due to grounds that the number of respondents might be so small in order for the study to have a more precise result. Most likely, retention might have just taken over the results leaving the environment as an insignificant factor.
Crosling, Thomas, &Heagney (2008) pointed out in their book that their solution to student retention is the Activating Work Groups on which the students do tasks or assignments in groups of 4 and 5. This implies with the help of each member and the interaction between them, the lessons will be more interesting thus there shall be an increase in the student’s ability to focus. Furthermore, another research in Nigeria by Chianson, Kurumeh&Obida (2010) has also concluded that students who were subjected to “cooperative learning” was able to retain the concepts in circle geometry compared to those students who were taught with the conventional way which is more individualistic. This tells us that a more interactive environment will help increase the retention ability of the students.
Topic and the Student’s Retention Ability
This part of the chapter will basically discuss how environment can affect the student’s retention ability. With the help of Pearson Product-Moment Correlation we can arrive at these following values:
Respondent | Rating | Retention Ability |
A1 | 10 | 80 |
A2 | 10 | 73.33 |
A3 | 10 | 38.33 |
A4 | 9 | 66.67 |
A5 | 8 | 81.67 |
CORREL = | -0.412683646 |
Table 7.1 Group A Student Rating on Environment and Retention Ability (Correlation)
Group A was given the three most interesting topic but did still yield a negative correlation of -0.412683646. Negative correlation means that the more comfortable the respondent is to the topic, the lesser will his/her retention ability will be. This phenomena which was unexpected by the researchers might be due to the retention ability of the respondent. Even if the respondent was comfortable of the said topic, he/she might have not remembered things that well during the 1 day interval. And also, this might also be due to the population of the study. Having 5 respondents in this particular controlled group might not be that sufficient for the study. Presented on the next page is the correlation between the student’s rating on the topic and the retention ability of the respondents from Group B.
Respondent | Rating | Retention Ability |
B1 | 6 | 93.33 |
B2 | 6 | 81.67 |
B3 | 5 | 85 |
B4 | 8 | 91.67 |
B5 | 6 | 100 |
CORREL = | 0.253552206 |
Table 7.2 Group B Student Rating on Environment and Retention Ability (Correlation)
Group B was given the three most un-interesting topic but still managed to produce a positive correlation. This positive correlation means that the more comfortable the respondent is to the topic, the higher the retention ability of the student will be.
To further show the difference between the two groups, the Group A respondents wrote on their comment page that the topics were interesting because it was basically comprised with information that they want to have.
This study is not be gender sensitive but 100% of the respondents in Group B were male, not to mention that one of the not so interesting topics that was discussed was Fashion, specifically Haute Couture. It was expected that these topics were most likely very unfamiliar to them but these topics were definitely striking which might have caused the high retention ability of the respondents from Group B.
To wrap everything up, the study yield a positive result showing that the more frequent the respondents use their computers, the more likely that they will have a lesser retention ability.
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