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Volume 39, Number 2
Winter 2002


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The Quality and Utilization of Technical Education Trainers in Kenya

Moses W. Ngware
Egerton University

Fredrick Muyia Nafukho
University of Arkansas

The emergence of Institutes of Technology in Kenya can be traced back to 1971 when local self-help committees throughout the country raised large sums of money for the establishment of "Harambee" Institutes of Technology. Harambee means pulling or putting resources together on a self-help basis (Nafukho, 1994). The objective was to provide post-secondary education in vocational and technical training, which the government alone was unable to provide. Thus, the central government assisted the Institutes of Technology (IT) by providing learning facilities and paying the trainers. The parents and local communities, on the other hand, ensured a steady supply of physical facilities by contributing funds that were used in the construction of laboratories, workshops, classrooms, and student hostels.

Table 1 shows the distribution of IT by administrative provinces. It is evident that some provinces such as Central and Nyanza were advantaged in terms of having more Institutes of Technology, followed by Eastern and Western. The four provinces accounted for over three-quarters of the training institutes. Further, Nairobi and Northeastern provinces had no IT while Coast and Rift Valley each had one.

Table 1
Distribution of IT by Administrative Province
Province Number of Institutes %
Central 5 29.4
Coast 1 5.9
Eastern 3 17.6
Nairobi 0 0.0
North-Eastern 0 0.0
Nyanza 4 23.5
Rift Valley 1 5.9
Western 3 17.6

While most research has been focused on the external efficiency of technical training institutions (Worswick, 1985; Leibenstein, 1989; Jones 1999), limited studies have examined the issue of internal efficiency of technical training institutions, especially in Kenya. The term "internal efficiency" in this paper is used in three different ways. First, its meaning is related to the instruction process. The transformation of physical resources is combined in Institutes of Technology to produce outputs such as graduates with technical knowledge and skills. This quantitative relationship between inputs and outputs is referred to as "technical efficiency." Technical training in Kenya is very expensive, since all the equipment must be imported using scarce foreign exchange reserves. Therefore, managers of public institutions such as IT should be interested in the combination of inputs and outputs that produces the maximum output at the least cost. This is referred to as "economic efficiency." Thus, an increase in economic efficiency occurs when the same output is produced at a lower cost. Where the desired output by policy makers, in this case a qualified technical graduate, is produced, then the training can be considered to have achieved "allocative efficiency." Therefore, IT in Kenya can be considered to be internally efficient if their technical, economic, and allocative objectives are being achieved. The three educational systems of primary, secondary, and tertiary levels in Kenya are increasingly faced with resource scarcity and increasing unit costs (Republic of Kenya, 1997). To solve these problems, there is need to improve internal operational efficiency of these institutions. This study focused on analysis of economic efficiency in the utilization of technical education trainers in IT.

Education and training constitutes an investment in human capital that is expected to yield a stream of future returns in the form of income and earnings for the individual and society and economic growth through enhanced productivity (Harbison & Myers 1964; Psacharopoulos, 1995; Shultz, 1963). The theory of human capital postulates that individuals are motivated to spend on themselves in diverse ways by purchasing education, not for its own sake, but for the sake of future pecuniary and non-pecuniary returns. Both direct and indirect costs are incurred when individuals and governments spend on education (Shultz 1961, 1962). From the theory of human capital, expenditures made by individuals and governments on education and training are investments that will provide returns in the future. Becker (1993) shows that investing in education and training increases individuals' lifetime earnings. Thus, education and training are processes of human capital formation. In fact, Shultz (1962) and Mincer (1962) note that all activities aimed at improving quality of human life such as spending on health, job search, training, and migration are part of human capital.

Training, the main focus of this paper, influences variation in wages and earnings. Mincer (1962) observes: "the training process is usually the end of a more general and preparatory stage, and the beginning of a more specialized and often prolonged process of acquisition of occupational skill, after entry into the labor force" (p. 50). The cost of training incurred by the training institutions and the trainees, including expenditures on foregone earnings, are considered investments (Aliaga, 2001). Proponents of the human capital theory argue that there exists a relationship between education and human capital accumulation (Nafukho, 2000, World Bank, 1994). However, the critics of the human capital paradigm argue against education as having no direct relationship with occupation, productivity, or commensurate income (Senanu, 1996). For instance, the fallacy of this relationship can be seen in the fact that it is not necessarily the most trained or educated who earn the highest incomes in most developing countries, including Kenya.

The issue of the appropriate linkage between education and the world of work will always be on the policy agenda of education systems in Sub-Sahara Africa. It will remain a matter of concern to all parties involved, be they sponsors or beneficiaries. Its salience is climaxed in conditions of economic stagnation when there are high levels of youth unemployment and a tendency to regard schools as a source of the unemployment problem. However, the real problem lies in job creation and not education (Nafukho, 1999). To address this issue, funds should be directed away from education and invested in job creation, since education is only known to directly create teaching jobs for trainers (Simmons, 1980).

Grubb (1996) documented the external efficiency of general education relative to vocational training in the U.S. Grubb found out that people with general education earned 7% more than those with vocational qualifications and that they were more likely to become professionals. From such evidence, students following career training can only ignore general education in their curriculum at their own disadvantage. The job market is such that people with general education tend to be highly regarded (Badway, 1998).

In the 1960s and early 1970s, it was generally believed that vocational education could contribute directly to economic growth by decreasing unemployment, increasing participation in the labor force, and inducing socio-economic equity (Fisher, 1993). This thesis did not go unchallenged by other scholars who dubbed such thinking as the "vocational school fallacy," arguing that vocational education was unable to fit students to specific occupations or even reduce mismatches between education and the job market in developing countries (Watson, 1988; Blaug, 1970; Foster, 1965). It has been argued that vocational schools fail because they seek educational solutions to issues that are basically not educational (Lillis & Hogan, 1983). The challenge facing education systems in developing countries is to produce the type of education and training that is more responsive to a wide range of local conditions facing the young people. The Kenyan government's efforts to vocationalize the educational system as a way of addressing the unemployment problem have not been very successful. The vocational school fallacy is a reality in Kenya. Graduates from IT equipped with technical and vocational skills cannot find employment in either formal or informal sectors (Nafukho, 1998).

Description of the Study

The primary purpose of this study was to determine the internal efficiency of the programs offered by IT in Kenya. To achieve this, the trainers' qualifications and utilization (determined by work load), as an indication of the levels of internal efficiency in IT in Kenya, were examined.

The specific objectives that guided the study included: (a) to find out the trainers' selected personal and academic characteristics such as gender, age, highest level of academic qualification, and years of teaching experience; (b) to establish the reasons for career change among trainers in the IT; (c) to determine the trainers' perceptions regarding the adequacy or inadequacy of training materials and equipment in IT studied; and (d) to find out the trainers' weekly workloads and to determine the trainers staff equivalent and actual staffing levels.

Methodology

The target population for this study consisted of 17 Institutes of Technology (IT) in Kenya. The researchers found the IT suitable for this study because they were established through the efforts of local communities throughout the country to provide post-secondary technical training opportunities (Odada & Odhiambo, 1989).

A sample size of seven (7) institutions was selected. This sample size was arrived at after considering institutional size (enrollment) as an indicator of how the population was distributed. Other factors considered in an effort to select a fair sample included cost of the study, heterogeneity of the frame population, number of traits to be measured, and size of the acceptable sampling error margin (5%). Stratified sampling was used in the first stage of IT sampling. Institutions were stratified into three levels based on institutional size deviation from the population mean (Mp).

Two strata participated in the second stage of IT sampling: institutes with enrollments less than 317 and institutes with enrollments above 413. Simple random sampling was used to select three and four IT from the two strata, respectively. The sample allocation of three from one stratum and four from another was computed from Neyman's formula for optimal sample allocation (Rossi, Wright, & Anderson, 1983). The institutions selected and their enrollment at the time of data collection included Mathenge (167), Kaimosi (205), Kimathi (401), Ramogi Institute of Advanced Technology-RIAT (541), Kiambu (558), Murang'a (693) and Rift Valley Institute of Science and Technology-RVIST (1204). Trainers and heads of departments from the selected IT were key respondents in this study. A total of 130 trainers and 37 heads of departments were selected for the study. Data were collected from both primary and secondary sources using pre-designed instruments and structured formal interviews. To collect primary data, questionnaires, evaluation, and observational forms were used. The questionnaires used both closed-ended and open-ended items. The open-ended items had an advantage of capturing a wide range of opinion and experiences. For the closed-ended items, dichotomous and polychotomous response categories were provided.

Giving the questionnaires to research experts at Moi and Egerton universities insured the validity of the questionnaires. A pilot study was also conducted before actual data collection. Department heads and trainers from the IT not included in the sample completed the questionnaires. Information obtained from the pilot study assisted in the revision of the research instruments.

Findings

A total of 108 trainers and 35 heads of departments responded to the questionnaires, an overall response rate of 86%. Regarding the gender of the trainers, Kiambu, Kimathi, Murang'a, and Ramogi Institute (RIAT) had no female instructors in electrical instillation, masonry, plumbing, mechanical engineering, building and civil engineering, and motor vehicle mechanics. Most of the female trainers tended to concentrate in traditionally female-oriented courses such as food and beverage productions, clothing and textiles, and bakery technology.

Ninety-eight (68.5%) male and 45 (31.5%) female trainers and department heads responded to the questionnaire. Table 2 presents the distribution of sampled trainers by age. The mean age of 35 years and a modal class of 36 to 40 suggested that trainers in IT were middle aged. The percentage of trainers below 25 years and above 45 years was 3% in each case. The small percentage (3%) of respondents above 45 years suggested that the probability of trainers changing their career as teachers in IT increased with age.

Table 2
Age Distribution of Trainers
Age in years n %
21 - 25 4 2.8
26 - 30 25 17.5
31 - 35 47 32.9
36 - 40 51 35.7
41 - 45 11 7.7
Above 45 5 3.5
Total 143 100.00

Note. Mean = 35, Modal class = 36-40, SD = 5.3


Data in Table 3 show that the majority (62%) of the trainers in these IT had a diploma in vocational education and technical training. Trained trainers with a bachelor's degree constituted 17% of the sample, while untrained trainers constituted 3%. This suggests that graduate trainers in the field of vocational and technical education were scarce in the labor market, or that IT were not attractive to first-degree holders. Trainers with a diploma other than education constituted 18% of the trainer respondents. This group was made up of technical education program associate degree holders. A majority (80%) of both trained and untrained IT trainers were associate degree holders. Given that they handled post secondary training, the qualification level of trainers was found inadequate.

Table 3
Trainers' Highest Level of Academic Qualification
Level n %
Trained graduate 24 16.80
Untrained graduate 4 2.80
Diploma in Education 88 61.50
Diploma other than in education 26 18.20
Others 1 0.70
Total 143 100.00

Note. A diploma in education is equivalent to an AS degree in the U.S. system.


Regarding experience, 78% of the trainers had been teaching in IT for 10 years. This is presented in Table 4. This finding supports the earlier thesis that with advancing age IT trainers changed their career from teaching. The modal range of years of experience was 6 to 10, with 9% of the trainers having above 15 years of teaching experience. This shows that in the IT studied, the probability of losing the most experienced trainers was higher than likelihood of retaining them.

Table 4
Trainers' Years of Teaching Experience
Experience in Years n (%)
1 - 5 54 37.80
6 - 10 57 39.90
11 - 15 19 13.30
Above 15 13 9.10
Total 143 100.00

Note. Mean = 7.7, modal class = 6 - 10, SD = 4.66


Data in Table 5 show the reasons given by trainers for changing careers from teaching in the IT to other jobs. The majority (72%) of trainers who left the institutes did so due to higher pay elsewhere. Respondents described this as availability of "greener pastures." There was a very low percentage (1.4 %) of trainers who went for further studies. This suggests that either IT trainers do not qualify for further training, avenues for further training are limited, or trainers' employers lacked adequate training and development policies.

Table 5
Reasons for Trainers' Career Change from Teaching in IT
Reason Count %
Better pay elsewhere 103 72.00
Further studies 2 1.40
Retirement 1 0.70
Transfer 7 4.90
Don't know 30 21.00
Total 143 100.00

The perception of respondents regarding the adequacy or inadequacy of their teaching materials and equipment was sought. Table 6 presents the responses on closed-ended items that measured the adequacy or inadequacy of the materials and equipment used for instructional purposes. A majority (56%) of the trainers reported that the basic teaching materials (BTM) were adequate, while another 44% felt the materials were inadequate for instructional purposes. BTM included items such as chemicals, stationery, textbooks, cloth, and chalk. Adequate BTM improves internal efficiency of the training institutions by enabling the institutions to achieve their internally set objectives such as producing a graduate with the required technical knowledge and skills. When the BTM is inadequate, the internal efficiency of the institutions is compromised. Basic teaching equipment (BTE) was looked at in terms of ability to produce quality work and the number available to trainees for instructional purposes. The BTE included things such as welding machines, motor machines, computers, typewriters, and projectors. Regarding quality, 52% of the respondents perceived available BTE to be of high quality while 48% thought they were obsolete and therefore produced low quality work.

Table 6
Perception of Trainers Regarding Adequacy of Teaching Materials and Equipment
Item Adequate Inadequate
  n % n 5
Basic Teaching Materials 80 55.9 63 44.1
Basic Teaching Equipment        
Quality 74 51.7 69 48.3
Quantity 53 37.1 90 62.9
Up to date 37 25.9 106 74.1

A spot check in the field showed that courses in cookery and clothing had better quality equipment. On quantity, most respondents (63%) felt that the equipment was not available for all students. Several students shared equipment. The majority of the trainers (74 %) perceived the BTE as being obsolete and not modern enough for instructional purposes. This indicated that IT in Kenya responded slowly to technological changes. This could also be explained by lack of financial resources for these institutions to purchase new equipment.

Trainers in IT are required by their employer, the Teachers Service Commission (TSC), to teach a minimum of 12 hours and a maximum of 18 hours per week, taking into account extra responsibilities. In this study, trainers averaged 15 hours of instruction per week. As shown in Table 7, 63% of those who responded to the questionnaire taught for not more than 15 hours per week, while 34% had a teaching load of between 16 to 20 hours per week. Only 3% indicated that their workload was above 20 hours. When compared to the employer's maximum weekly requirement of 18 hours, it was found that trainers in this study worked on average three hours less than expected even after taking into account hours needed for extra responsibilities. This, according to the TSC, is an underutilization of teacher resources in the IT, and thus reflects a lower internal efficiency. However, workload must be redefined to include time spent on preparation, grading trainees' work, and advising. Thus, current measures of staff loading are based on contact hours that do not accurately reflect the amount of work being done.

Table 7
Weekly Work Load for Trainers
Hours per week n %
6 - 10 35 24.5
11 - 15 55 38.5
16 - 20 48 33.5
21 - 25 4 2.8
26 - 30 1 .7
Total 143 100.0

Note. Mean = 14, modal class = 11 - 15, SD = 4.2


Manpower utilization is an important indicator of internal efficiency in the operations of IT. To further determine the extent of utilization of trainers, a more detailed analysis of staff loading based on a Full Time Staff Equivalent (FTSE) calculation was necessary. FTSE here means the equivalent of teaching staff required to teach a particular course on a full-time basis. It is a function of student numbers, student contact hours in a subject, and trainer-pupil-ratio in a subject (Aduol, 1998; Nassiuma, 1998). This is shown symbolically as:

  FTSE = H/2 (S/RK + 1/T) -----------------(1.0)
Where: H = Total contact hours per year per subject (both theory and practical subjects)
  S = Number of students per subject
  K = A constant representing the available number of student contact and consulting hours per year (i.e. 1440 hours)
  T = Expected working hours per staff per year, (in this case, 540) both theory and practical, per subject
  R = Student trainer ratio. The principals provided it as 1:15 for science-oriented courses and 1:25 for languages and business oriented courses.

The FTSE model indicates need for trainers by subject for each department. Table 8 summarizes the ranking of sampled IT by efficiency in utilization of trainers in the IT, studied through an index measuring ideal staffing levels.

Table 8
Full Time Staff Equivalent and Actual Staffing Levels
Institute Department FTSE Actual Difference
Mathenge Motor Vehicle Mechanics 6.959 10 +3.041
  Food and Beverage 8.547 9 +0.453
  Garment Making 5.277 8 +2.723
  Sub-total 20.783 27 +6.217
Kaimosi Accounts 3.426 7 +3.574
  Secretarial Studies 5.829 2 -3.829
  Food Technology 7.770 8 +0.230
  Sub-total 17.025 17 -0.025
Kimathi Building Construction 4.123 5 +0.877
  Electrical Installation 5.945 6 +0.055
  Plumbing 2.435 3 +0.565
  Sub-total 12.503 14 +1.497
RIAT Elec. Installation & Electronics 13.647 17 +3.353
  Building and Civil Engineering 6.569 22 +15.431
  Automotive Engineering 7.250 7 -0.250
  Business Studies 4.828 19 +14.172
  Clothing and Textiles 5.801 18 +12.199
  Catering 15.458 14 -1.458
  Sub-total 53.553 97 +43.447
Kiambu Bakery Technology 7.562 7 -0.562
Murang'a Applied Science 4.630 10 +5.370
  Mechanical Engineering 12.487 11 -1.487
  Building and Civil Engineering 23.851 18 -5.851
&mbsp; Institutional Management 13.678 19 +5.322
  Electrical Installation 11.290 10 -1.290
  Sub-total 65.936, 68 +2.064
RVIST Agriculture 23.408 28 +4.592
  Applied Technology 15.002 30 +14.998
  Electrical and Electronics 13.362 25 +11.638
  Computer Studies 5.769 15 +9.231
  Sub-total 57.541 98 +40.459
GRAND TOTAL 234.903 328 +93.097

Note. FTSE does not consider the trainers' time spent on preparation, development of course material, grading student work and advising.


The FTSE analysis showed that Kaimosi Friends College, Kiambu Institute of Science and Technology, and Kimathi Technical Institute had staff levels close to the optimum. RIAT and RVIST appeared to be the least efficient in the utilization of trainer resources. At the departmental level, building and civil engineering courses at Murang'a and secretarial studies at Kaimosi were the least staffed while building and civil engineering (RIAT) and applied technology (RVIST) were the most overstaffed, according to the TSC. Overall, 93 trainers "overstaffed" the seven IT studied. To make the FTSE realistic and to accurately determine the utilization of trainer resources in IT in Kenya, different measures that reflect the additional work trainers do would be necessary.

Conclusion and Recommendations

For IT in Kenya, and other countries as well, to be internally efficient, studies of this nature are necessary. It is the human resources in these training institutions that are instrumental in insuring both internal and external efficiency of the institutions. To insure optimal utilization of trainer resources, the definition of workload must also be revisited.

A clear policy directed at determining the utilization of trainers in IT in Kenya is inevitable. The researchers recommend revising the calculation of Full Time Staff Equivalent (FTSE) and Actual Staffing Levels to reflect time spent on preparation, course and material development, grading students' work, and advising. This should, in addition, be combined with appropriate financial rewards in order to retain experienced trainers in these institutions. The IT principals interviewed felt that the salary paid to trainers in technical institutes should be incremental and competitive, considering that trainers take a long time to train before qualifying as trainers. Deliberate efforts in increasing the salaries for trainers in IT should be made by the government. This will insure a steady supply of qualified and experienced trainers in the IT. In addition, it will reduce the high rate of turnover among trainers.

For IT in Kenya to achieve the internally set objective of producing qualified graduates with the relevant technical knowledge and skills, the researchers recommend the following:

  1. Recruitment of qualified trainers with a minimum of a bachelors degree and professional training in education;
  2. Increase in the salary level of trainers as a way of reducing turnover;
  3. Acquisition of modern equipment for instructional purposes; and
  4. Redefinition of workload to include all the work that the trainers do to insure efficient and effective training.

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Ngware is Senior Lecturer and Head of the Department of Educational Administration and Planning at Egerton University in Njoro, Kenya (mngware@yahoo.com). Nafukho is Assistant Professor in the Department of Rehabilitation, Human Resources & Communication Disorders at University of Arkansas in Fayetteville, Arkansas (nafukho@uark.edu).


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