«VOLUM E 1 1, N UM B E R 1 I S SN 2 1 6 8 - 0 6 1 2 F L ASH DR I V E I S SN 1 9 4 1 - 9 5 8 9 ON L I N E T h e In s t it ut e f o r Bu s i n e s s an ...»
Table 2: Background profile and preparedness for eLearning
Results in Table 2 further show that 146 (68.9%) participants were men and 66 (31.1%) were women.
Besides that proportion of women lecturers prepared for eLearning 34 (33.0%) was marginally higher than GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 470 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 the proportion of those unprepared 32 (29.4%). However, the proportion of men prepared for eLearning 69 (67.0%) was lower than the proportion of those unprepared 77 (70.6%). However, the analysis did not find a significant relationship between lecturers’ gender and preparedness for eLearning [computed χ2 = 1.039 (corrected for continuity), df = 1 and p-value = 0. 243]. This suggests that no gender was more competent in computing than the other; hence, none was likely to be more prepared than the other. This is however, inconsistent with the findings of Luan, Aziz, Yunus, Sidek and Bakar (2005), who investigated gender differences in ICT competence among academicians at the Universiti Putra Malaysia. The study noted that female and male academicians were significantly different in the application of software packages such as word processing, spreadsheets and presentation tools. However, in Egypt, Houtz and Gupta (2001) found that male lecturers were more confident and had a greater usage of computers compared to their female counterparts. Besides, Venkatesh and Morris (2000) noted that male lecturers were more likely to accept new technological innovation than their female colleagues.
Up to 151 (71.2%) academic staff reported holding PhD degrees, 56 (26.4%) held masters certificates, while 5 (2.4%) had bachelor’s degree qualifications. Besides, the results summarised in Table 2 show that the proportion of PhD holders unprepared for eLearning was higher than the proportion of those prepared.
Conversely, the proportion of masters’ degree holders prepared for eLearning was higher than the proportion of those unprepared. Based on this pattern, a computed Chi-square (χ2) value of 11.031 was obtained, with 2 degrees of freedom and p-value of 0.004, which is significant at 0.01 error margin;
suggesting up to 99% chance that lecturers’ preparedness for eLearning significantly associated with educational attainment. Thus, masters’ degree holders, being relatively younger people, were likely to be more competent in computing; hence, better prepared for eLearning than PhD holders. These findings are consistent with those reported by Roberts, Hutchinson and Little (2003) who assessed barriers to the use of technology for teaching among Dutch universities. The study noted that professors and associate professors were less likely to use ICT tools in their teaching than junior lecturers.
The results in Table 2 further indicate that most participants, 155 (73.1%), were earning KES 90,000 or more; 21 (9.9%) were in the KES 80,000 to 89,000 bracket; 17 (8.0%) averaged at between KES 70,000 and 79,000, while 11 (5.2%) reported an income of KES 60,000 to 69,000. In addition, the proportion of lecturers unprepared for eLearning in the top income bracket was higher than the proportion of those prepared. Contrastingly, the proportion prepared for eLearning in the category of less than KES 60,000 was higher than those unprepared. The analysis yielded a computed Chi-square (χ2) value of 11.707, with 5 degrees of freedom and p-value of 0.039, which is significant at 0.05 error margin; suggesting up to 95% chance that preparedness for eLearning varied significantly across the income categories. More specifically, top earners were less competent in computing than low earners. Similarly, Venkatesh and Morris (2000) found a positive correlation between the frequency of computer use and lecturers’ average income. The study noted that although lecturers in higher income brackets had a greater access to personal computers than those in lower income scales, more than one-half did not use computers consistently to support their work due to limited ICT skills.
Lecturer’s Computing Competence & Preparedness for ELearning
Computing competence is the ability to handle a wide range of computer applications for various purposes and can be enhanced through appropriate training programme, covering the application of basic software packages for word processing, spreadsheets, presentation, statistical analysis, internet browsing and emailing (van Braak, 2004). This subsection focuses on the training in software tools, training duration, funding sources for training, competence in using software tools and challenges associated with computing competence. Out of 212 participants, 156 (73.6%) had accessed training in word processing tools; 119 (56.1%) had trained in spreadsheets; while 135 (63.7%) had accessed training in presentation tools. The results further show that 102 (48.1%) had trained in statistical analysis tools; 127 (59.9%) had trained in internet browsing tools; while 107 (50.5%) had undergone training on the use of e-mailing tools. In addition, GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 471 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Table 3 shows that among participants who had accessed training in all the software tools, the proportion of those prepared for eLearning was higher than the proportion unprepared. The study found that most participants, 156 (73.6%) were trained in word processing tools, followed by presentation tools, 135 (63.7%), Internet browsing, 127 (59.9%) and spreadsheets, 119 (56.1%). Only about one-half, 50.0% and 48.1% of the participants had accessed training in e-mailing and statistical analysis tools, respectively. The pattern suggests that training was a critical component lecturer’s preparedness for eLearning.
Table 3: Proportion of participants trained on software tools
The results summarized in Table 3 show that lecturers’ preparedness for eLearning significantly associated with training in various tools including word processing, presentation, Internet browsing, spreadsheets and statistical analysis. The results suggest that training in all the software tools, except emailing was likely to have significant influence on lecturers’ preparedness for eLearning. Notably, emailing tools were considered as a means for communication for personal and academic purposes, which had become more important than surface mail. This explains why there was no significant variation in preparedness for eLearning based on competence in working with emailing tools. The results amplify the importance of training in software tools. In this regard, participants who reported having some training were better prepared for eLearning than those who had not trained. Similar findings were obtained by Son et al. (2007) who noted that teachers who had some prior training in software packages were using computers in the classrooms more than their colleagues who had not undergone such training. The study further noted that among factors influencing teachers’ computing skills were previous training was the most important, accounting for up to 80% of variance in computing competence.
The duration of training is also a critical factor likely to influence computing competence and preparedness for eLearning; the longer the duration, the better the competence and vice versa. For this matter, those who had trained in various software tools were requested to indicate the duration for which training was received.
The results show that the duration of training for word processing tools averaged at 3.3 weeks (95% CI 2.3presentation tools averaged at 2.0 weeks (95% CI 1.1-2.9); while the training for Internet browsing averaged at 1.7 weeks (95% CI 0.9-2.5). More still, mean duration of training for spreadsheets tools was
2.4 weeks (95% CI 1.2-3.6); statistical analysis tools was 2.2 weeks (95% CI weeks (95% CI 0.5-4.0); and GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 472 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 e-mailing, 2.04 weeks (95% CI 0.9-3.2). The results show that mean duration of training in word processing tools was the longest at 3.3 weeks, while the shortest training duration was in internet browsing at 1.7 weeks. Although there was no significant variation in the duration of training across the software tools, the common denominator is that the trainings were too short for a beginner; and barely matched the scope of software programmes such as Microsoft Word, Microsoft Excel and statistical analysis tools such as SPSS, Epi info, SAS or CSpro.
Compared to the guidelines provided by the Computer Society of Kenya (CSK), the reported durations of training are way below the recommended standards. For instance, training in word processing packages should take between 4-6 weeks; suggesting that participants who had accessed training in word processing tools will require further training to effectively cover the curriculum. One-way Analysis of Variance was performed to determine if there was any significant variation in the duration of training between participants prepared for eLearning and those unprepared. The results revealed lack of significant variation in the training duration for all the software tools, suggesting that training duration was standard for all participants, regardless of whether they were prepared for eLearning or not. Key informant interviews revealed that training for most software tools were obtained from commercial colleges, whose curricula were standardised to suit their commercial interests. However, reduction of course contents to a period of two weeks, rather than 6 weeks, means that trainees with little or no prior computing experience are seriously disadvantaged.
Most participants sponsored themselves for training in the software tools. More specifically, 108 (69.2%) participants sponsored themselves for training in word processing tools; only 40 (25.6%) were sponsored by the employer (University of Nairobi). For spreadsheet tools, up to 84 (70.6%) participants sponsored themselves, while 29 (24.4%) were sponsored by the employer. In the case of presentation tools, 102 (75.6%) sponsored themselves, while 28 (20.7%) had been facilitated by the employer. The pattern was similar for training in statistical analysis, internet and e-mailing tools. Furthermore, Table 4 shows that among self-sponsored trainees, the proportion of participants unprepared for eLearning was higher than the proportion prepared; contrastingly, among those sponsored by the employer; the proportion of those prepared for eLearning was higher.
This suggests that training facilitated by the employer was likely to be more intensive than the training acquired through self-sponsorship. However, given that only about one-third of the participants had benefited from employer-sponsored training in software tools, key informant interviews revealed that the University training programme for academic staff was not supportive. Based on previous training or lack of training and experience, participants were requested to rate their competence in working with various software tools, indicative of their preparedness to operate in an eLearning environment.
Competence in using software tools
The purpose of training is to help beneficiaries develop their skills and competence. For this reason, participants were requested to rate their competence in applying each of the software tools on a scale of 0 to 10, which was them transformed into a scale of 50% and 50% or more. Those whose scores for all the tools averaged below 50% were considered to be less competent and unprepared to work in an eLearning environment; while those whose scores averaged at 50% or more were considered competent and prepared for eLearning. Based on this principle, up to 139 (65.6%) participants were found to be below average in applying word processing tools; another 73 (34.4%) were above average. In the case of spreadsheets, 121 (57.1%) participants were below average; 91 (42.9%) reported a score above average. For presentation tools, those below average were 122 (57.5%), while 90 (42.5%) were above average. In statistical analysis tools, those above average were 53 (25.0%); the majority (75.0%) were below average.
Table 4: Sponsorship for training in software tools GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 473 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1
In addition, 168 (79.2%) were above average in working with internet browsing tools; only 44 (20.8%) were below average. For e-mailing, those above average were 167 (78.8%). These findings suggest that most participants were relatively more competent in working with internet, e-mailing and word processing tools than presentation, spreadsheets and statistical analysis tools. Furthermore, Table 5 shows that among those who were above average in working with word processing tools, the proportion prepared for eLearning, (84.5%) was higher than the proportion unprepared (47.7%).
Based on the result, bivariate analysis obtained a calculated Chi-square (χ2) value of 30.089 (corrected for continuity), with 1 degree of freedom and a p-value of 0.000, which was significant at 0.01 error margin.
This suggests that lecturers’ preparedness for eLearning significantly associated with their competence in working with word processing tools. Consequently, participants whose competence in working with word processing tools was above average were likely to be better prepared for eLearning than those whose competence was below average. This prompted rejection of the null hypothesis (H01), stating that lecturers’ competence in word processing has no significant relationship with their preparedness for eLearning for not holding true to empirical evidence.