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«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 ...»

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In Malaysia, Koo (2008) reported that up to 85% of lecturers in public universities were limited in terms of computing skills, which in turn, affected their application of computers in their teaching. The functionality of such lecturers was significantly constrained by skill limitations in computing, which delayed the adoption of eLearning by more than half of public Malay universities. Still in Malaysia, a study conducted by Selim (2007) noted that due to inadequacy of computing skills, more than 80% of Malay lecturers in public universities lacked confidence in computer use. In Bahrain, Al-Ammari and Hamad (2007) found that the perceived usefulness of computers and the perceived ease of use were significantly associated with lecturers’ intention to integrate ICT in their teaching activities. The study also found that computer selfefficacy positively influenced lecturers’ intention to use computers in their work. The perceived usefulness, perceived ease of use and self-efficacy regarding computer use among lecturers are critical elements of institutional preparedness for eLearning.

Still in Asia, Lu et al. (2005) found that the intention to use eLearning websites among university lecturers in Taiwan significantly associated with lecturers’ competence in using computers. The study further noted that competent lecturers were more regular in visiting eLearning websites than those lacking computing skills. The study emphasised the role of universal training for academic staff to facilitate transition to an era of technology-aided university education. Nanayakkara and Whiddett (2008) noted that the decision of lecturer’s to embrace eLearning significantly correlated with the level of computing skills in online content design. In relation to this finding, the study revealed that about two-thirds of lecturers at the Bay of Plenty Polytechnic in New Zealand reported a low level of computing skills. Yet again, ICT training was identified as the most crucial avenue through which institutions of higher learning can improve computing skills among their academic staff.

In the United Kingdom, Thomas and Stratton (2006) revealed a strong positive relationship between ICT training, computing competence and computer use. Lecturers who had had some training in ICT applications were more competent than those lacking training. Besides, up to 70% of trained lecturers were of the opinion that the manipulation of ICT tools was easy. In this regard, the frequency of computer use was higher among those who perceived the manipulation of ICT tools to be easy. The study also found that trained lecturers were consistently using computers to support course delivery than those who were yet to undergo such training. In relation to institutional preparedness for eLearning, the study reported a strong relationship between the proportion of ICT competent lecturers and the number of departments that had integrated eLearning.

In Africa, studies relating computing competence and institutional preparedness for eLearning remain scarce. The few existing documentations are concentrated in the south and western parts of the continent.

For instance, Thurab-Nkhosi et al. (2004) found that inadequate computing competence among lecturers was one of the key constraints to eLearning at the University of Botswana (UBeL initiative). In this regard, the study revealed that only 20% of the surveyed participants rated their computing proficiency as excellent, the majority expressed discomfort with computers. In Namibia, Mpofu (2004) reported that more than twothirds of lecturers were not using computers to facilitate course delivery, despite the motivational support provided by the universities, which include ICT training, universal access to computers at the workplace GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 467 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 and higher allowances for trained lecturers. Low computing competence significantly associated with negative attitudes towards ICT, which affected the level of computer use. In Nigeria, Tella (2007) found that low level of computing skills was the key factor influencing the confidence to utilise ICT equipment and software tools to support course delivery. The study found a significant relationship between computing skills and fear regarding computer use. In this regard, teachers lacking computing skills expressed a low level of confidence in computer use.

Kenya is one of the countries experiencing a dearth of academic literature on lecturers’ computing competence and preparedness for eLearning. Although the University of Nairobi has been a leading icon in Open and Distance Learning (ODL) activities within the East African region, eLearning is still at the early stages of development. A study conducted by Gakuu (2006) revealed that the use of ICT-based instructional modes was limited at the University of Nairobi; however, lecturers expressed a positive attitude towards computer use and eLearning. Moreover, lecturers’ attitude towards computers and eLearning was not significantly different across University colleges. Key deficiencies noted in Gakuu’s study included inadequate linkage between infrastructural facilities, lecturers’ computing competence and preparedness for eLearning. Besides, the study did not bring out the extent of ICT training needs among lecturers at the University. The objective of this study was to determine the relationship between computing competence and lecturers’ preparedness for eLearning at the University of Nairobi.





METHODOLOGY

This study was founded on the positivist philosophy of social research, holding that in social sciences, information derived from sensory experience is the exclusive source of all authoritative knowledge.

Besides, the world is external and objective; and that the observer is independent of the phenomena being observed. The positivist thought assumes that valid knowledge can only be found in scientific knowledge (Ashley & Orenstein, 2005). Based on the positivistic thinking, a cross-sectional survey design with both quantitative and qualitative approaches was applied to guide the research process (Babbie, 1973; Fowler, 1993). Whereas, the quantitative approach elicited information used for descriptive and inferential purposes using self-administered questionnaires, the qualitative approach obtained in-depth information through key informant interviews.

Primary data was collected in May 2011 from lecturers and administrative staff at the University of Nairobi.

Although the study focused on lecturers’ preparedness for eLearning, the inclusion of administrative staff was based on their crucial role in policy formulation, implementation and enforcement, which influence the work environment in which lecturers operate. Their inclusion in the study was purposed to identify policy gaps regarding ICT strategies, plans, budgetary allocations and ICT development, which are likely to influence lecturers’ preparedness to function in an eLearning environment. Unpublished data from the office of Deputy Vice Chancellor, Finance and Administration showed that the University had 958 academic and 108 administrative staff at the time of the study.

With a finite population of lecturers, one of Fisher’s formulae for sample size determination was applied to obtain a sample size of 213 participants. Stratified random sampling was applied to select the lecturers, with the stratification being based on colleges, gender and cadre. This ensured proportionate representation of all colleges; male and female lecturers; as well as assistant lecturers, lecturers, senior lecturers, associate professors and professors. Proportionate samples from each stratum were obtained by first, calculating the sampling fraction, as a quotient of the sample size (ni) and the population (Ni). Table 1 shows the proportionate sample sizes from each college.

From each stratum, simple random sampling was applied to select respondents. In addition, purposive sampling procedure was applied to select administrative staff, based on their availability and accessibility at the time of the study. The sample included 6 principals, 6 deputy principals, 6 registrars, 21 assistant registrars, 20 deans and directors, 13 associate deans and deputy directors; as well as 36 administrative GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 468 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 assistants. Three sets of instruments, including a self-administered survey questionnaire for lecturers, a key informant interview schedule for administrators and an observation schedule were used to source the data.

The tools were pretested on 20 lecturers and 10 administrators, which was equivalent to about 10% of the computed sample sizes for each category. Data was obtained by issuing questionnaires to lecturers, which were collected after two weeks. Administrators were interviewed at their places of work; the investigator sought informed consent from each participant. In this regard, participants were briefed about the study, purpose, potential benefits and that participation was on voluntary terms.

Table 1: Proportionate samples of academic staff for each college

–  –  –

Both quantitative and qualitative techniques were applied to process and analyse. Quantitative data were analysed at three levels, namely univariate, bivariate and multivariate. Univariate analysis yielded frequency distributions and percentages; bivariate analysis obtained cross tabulations with Chi square (χ2) tests; while multivariate applied binary logistic regression to obtain beta co-efficients and odds ratios. All the quantitative analyses were performed using the Statistical Package for Social Sciences (SPSS) and MsExcel packages. In addition, qualitative data were organised and summarised in line with the thematic areas;

described to produce summary sheets; followed by systematic analysis and interpretation. Details about the methods applied in this study have been described in various publications, including Babbie (1973), Fowler (1993), Aldrich and Nelson (1984), Nachmias and Nachmias (1996), Mugenda and Mugenda (1999), Wuensch (2006), as well as Best and Khan (2004).

RESULTS

The study covered 212 lecturers from all the colleges of the University of Nairobi, including 104 (49.1%) from the College of Humanities and Social Sciences (CHSS); 19 (9.0%) from the College of Biological and Physical Sciences (CBPS); 24 (11.3%) from the College of Health Sciences (CHS); 29 (13.7%) from the College of Education and External Studies (CEES); 20 (9.44%) from the College of Agriculture and Veterinary Sciences (CAVS); and 16 (7.5%) from the College of Architecture and Engineering (CAE). In terms of gender, lecturers from CHSS included 56 (53.8%) men and 48 (46.2%) women; from CBPS were 16 (84.2%) men and 3 (15.8%) women; while from CHS were 20 (83.3%) men and 4 (16.7%) women. The CEES provided 23 (79.3%) men and 6 (20.7%) women; at CAVS 17 (85.0%) men and 3 (15.0%) women participated; while lecturers from CAE included 14 (87.5%) women and 2 (12.5%) women. In addition, the study involved 96 administrative staff, including 34 (35.4%) administrative assistants, 6 (6.3%) college registrars and 15 (15.6%) assistant registrars; 10 (10.4%) departmental chairpersons; 10 (10.4%) faculty deans and 6 (6.3%) associated deans; as well as 8 (8.3%) directors and 7 (7.3%) deputy directors. The administrative staff included 64 (66.7%) men and 32 (33.3%) women.

ELearning preparedness Lecturers’ preparedness for eLearning was measured in terms of perceived computing competence, referring to the ability to execute commands and manipulate a range of software applications for various purposes. In this regard, participants were requested to rate their competence on each of the following computing software tools on a scale of 1-10: word processing, spreadsheets, presentation, statistical analysis, internet browsing and e-mailing. The participants’ ratings for each software tool were summed GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 469 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 and mean scores determined. Resultant quotients were then rated on a scale of 0-49% and 50-100%.

Participants whose mean scores were less than 50% were considered to be below average; thus, were likely to be unprepared to function in an eLearning environment. Conversely, those whose mean scores were above 50% were considered to above average, and likely to be prepared for eLearning. Based on the principle, out of 212 participants, 103 (48.6%) had a mean score of 50 percent or higher; while 109 (51.4%) scored less than 50 percent; suggesting that slightly more than one-half of the lecturers were below average in terms of computing competence.

Lecturers’ background profile and eLearning preparedness The results presented in Table 2 show that out of 212 participants, 97 (45.8%) were in the 40 to 49 years age bracket; 4 (25.5%) were aged between 50 and 59 years, while 22 (10.8%) were in the 30 to 39 years bracket. Besides, another 22 (10.8%) reported to be 60 years or higher, while 8 (3.9%) were aged below 30 years. Table 2 further shows that the proportion of lecturers unprepared for eLearning in the 50+ age category was more than the proportion of those prepared in the same age category. Conversely, the proportion of staff prepared for eLearning aged below 40 years was higher than the proportion of those unprepared. The pattern suggests that younger academic staff were likely to be more competent in working with software tools; hence, likely to be better prepared for eLearning than their relatively older colleagues.

Based on this, bivariate analysis obtained a computed Chi-square (χ2) value of 18.026, with 4 degrees of freedom and a p-value of 0.001, which is significant at 0.01 error margin; suggesting up to 99% chance that lecturers’ preparedness for eLearning significantly associated with age. Similar findings on the link between age and computing competence were reported by Venkatesh and Morris (2000) who assessed the role of gender and social influence on technology acceptance behaviour among academic staff of Indian public universities. The study found that younger lecturers were more receptive to new technologies than their older counterparts. In Jordan, Abbad, Morris and Nahlik (2009) found a negative correlation between lecturers’ age and eLearning delivery methods.



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