Chapter 8: Discussion of Research Question Two and Research Question Three
5. RESULTS OF RESEARCH QUESTION ONE 1 Research Question One
5.1.8 E-Interactive learning
135 5.1.7 Learning Styles
Table 5.6: Spearman’s Correlation Coefficient on Previous Qualifications
ACADPERF Learning style Spearman's Rho ACADPERF Correlation
Coefficient
1.000 -.010
Sig. (2-tailed) . .744
Learning style
Correlation Coefficient
-.010 1.000
Sig. (2-tailed) .744 . a. Listwise N = 1025
Table 5.6 illustrates that the Spearman’s Rho positive correlation coefficient r = -.010 computed for learning styles and academic performance (ACADPERF) was negative with significance or p- value = .744 which is greater than Alpha = 0.01 or greater than Alpha = 0.05. This implies that no significant correlation exists between learning style and academic performance (ACADPERF).
This result is different when compared to the study by Soghra et al. (2013) who reported that there was a significant relationship between learning style preferences and academic performance of students and the reason could be that this study was conducted in a developed country.
136
Table 5.7: Spearman’s Correlation Coefficient on Interactive Learning
ACADPERF LCI LII LLI
Spearman's Rho
ACADPER F
Correlation Coefficient
1.000 .050 .017 .066*
Sig. (2-tailed) . .110 .581 .034
N 1025 1025 1025 1025
LCI Correlation Coefficient
.050 1.000 .452** .412**
Sig. (2-tailed) .110 . .000 .000
N 1025 1025 1025 1025
LII Correlation Coefficient
.017 .452** 1.000 .554**
Sig. (2-tailed) .581 .000 . .000
N 1025 1025 1025 1025
LLI Correlation Coefficient
.066* .412** .554** 1.000
Sig. (2-tailed) .034 .000 .000 .
N 1025 1025 1025 1025
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
The researcher then conducted Spearman’s correlation coefficient test on each of them to establish if correlation exists.
For learner-content interaction (LCI), Table 5.7 illustrates that the Spearman’s Rho correlation coefficient r = .050 computed for learner-content interaction and academic performance (ACADPERF) was positive with significance or p-value = .110 which is greater than Alpha = 0.01
137
or Alpha = 0.05. This implies that no correlation exists between LCI and academic performance (ACADPERF).
It is interesting to note that this result is contrary to the study by Nesliha and Mustapha (2016) who found that there was a significant relationship between LCI and academic performance and the reason may be that this was carried out outside developing countries.
Table 5.7 further revealed that a positive correlation does exist between LCI and academic performance (ACADPERF) of female distance e-learners although the strength of the correlation was weak. But no significant correlation exists between LCI and academic performance (ACADPERF) of male distance e-learners.
It is worthy of note that this result is in disagreement with the study carried out in a developed country by Nesliha and Mustapha (2016) who found that there was no statistically significant difference between the interaction and academic performance of male and female students and the reason may be due to the background of the students.
For learner-instructor interaction (LII), Table 5.7 illustrated that the Spearman’s Rho correlation coefficient r =.017 computed for learner-instructor-interaction and academic performance (ACADPERF) was positive with significance or p-value = .581 which is greater than Alpha = 0.01 but less than Alpha = 0.05. This implies that no correlation exists between LII and academic performance (ACADPERF).
This is an interesting result since it is different when compared to the study by Diedrich (2010) and Knoell (2012) who found that there was a significant relationship between LII and academic performance of students. This reason could that their studies were conducted in a developed country and because of the geographical location of the students.
Table 5.7 further revealed that a positive correlation does exist between LII and academic performance (ACADPERF) of female distance e-learners, although the strength of the correlation was weak. But no significant correlation exists between LII and academic performance (ACADPERF) of male distance e-learners.
138
This result was consistent with another study by Coldwell, Craig, Paterson and Mustard (2008) who found that students who participated more frequently in discussion forums obtained significantly higher grades. It is interesting to find out that although the above study was conducted in Australia, a developed country, a similar finding surfaced in a developing country like Nigeria.
For learner-learner interaction (LLI), Table 5.7 illustrates that the Spearman’s Rho correlation coefficient r = .066 computed for learner-learner interaction and academic performance (ACADPERF) was positive with significance or p-value = .034 which is greater than Alpha = 0.01 but less than Alpha = 0.05, This implies that a positive correlation exists between LLI and academic performance (ACADPERF) although the strength of the correlation was weak. It is noteworthy that this result is aligned with the study carried by Rugendo (2014) in Kenya, a developing country like Nigeria, which reported that LLI influenced academic performance of distance learners.
Table 5.7 further reveals that a positive correlation does exists between LLI and academic performance (ACADPERF) of female distance e-learners although the strength of the correlation was weak. But no significant correlation exists between learner-learner interaction and academic performance (ACADPERF) of male distance e-learners.
Interestingly, this result was quite consistent with another study by Coldwell et al., (2008) and Kunhi Mohamed (2012) who reported female students were more actively engaged in online discussions and outperformed than their male counterparts in online courses despite the fact that the study was carried out in Australia.
139
Table 5.8: Spearman’s Correlation Coefficient on E-Interactive Learning by Splitting Based on Gender
Gender ACADPERF LCI LII LLI
Spearman'
s Rho Female ACADPERF Correlation Coefficient 1.000 .121** .108* .105*
Sig. (2-tailed) . .009 .018 .023
N 474 474 474 474
LCI Correlation Coefficient .121** 1.000 .484** .401**
Sig. (2-tailed) .009 . .000 .000
N 474 474 474 474
LII Correlation Coefficient .108* .484** 1.000 .569**
Sig. (2-tailed) .018 .000 . .000
N 474 474 474 474
LLI Correlation Coefficient .105* .401** .569** 1.000
Sig. (2-tailed) .023 .000 .000 .
N 474 474 474 474
Male ACADPERF Correlation Coefficient 1.000 -.013 -.067 .034
Sig. (2-tailed) . .756 .114 .430
N 551 551 551 551
LCI Correlation Coefficient -.013 1.000 .421** .418**
Sig. (2-tailed) .756 . .000 .000
N 551 551 551 551
LII Correlation Coefficient -.067 .421** 1.000 .536**
Sig. (2-tailed) .114 .000 . .000
N 551 551 551 551
LLI Correlation Coefficient .034 .418** .536** 1.000
Sig. (2-tailed) .430 .000 .000 .
N 551 551 551 551
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
140
Table 5.9: Summary of ANOVA by Splitting Learner-Content Interaction Based on Gender
Gender Sum of Squares df Mean Square F Sig.
Female Between Groups 5.427 4 1.357 3.251 .012
Within Groups 195.737 469 .417
Total 201.165 473
Male Between Groups .082 4 .020 .051 .995
Within Groups 218.539 546 .400
Total 218.621 550
Table 5.10: Summary of ANOVA by Splitting Learner-Instructor- Interaction Based on Gender
Gender
Sum of
Squares df
Mean
Square F Sig.
Female Between Groups 4.631 4 1.158 2.763 .027
Within Groups 196.534 469 .419
Total 201.165 473
Male Between Groups 1.580 4 .395 .994 .410
Within Groups 217.041 546 .398
Total 218.621 550
141
Table 5.11: Summary of ANOVA by Splitting Learner-Learner- Interaction Based on Gender
Gender
Sum of
Squares df
Mean
Square F Sig.
Female Between Groups 3.525 4 .881 2.091 .081
Within Groups 197.639 469 .421
Total 201.165 473
Male Between Groups .788 4 .197 .494 .741
Within Groups 217.833 546 .399
Total 218.621 550