Students’ Tendencies in Choosing Technical and Vocational Education and Training
(TVET): Analysis of the Influential Factors using Analytic Hierarchy Process
Chia Ming Hong1*, Chee Keong Ch’ng2, Teh Raihana Nazirah Roslan3
1*,2Department of Decision Science, School of Quantitative Sciences, Universiti Utara Malaysia,
06010 UUM Sintok, Kedah, Malaysia.
3Department of Mathematics, School of Quantitative Sciences, Universiti Utara Malaysia,
06010 UUM Sintok, Kedah, Malaysia.
carmenhongcm@gmail.com1,chee@uum.edu.my2, raihana@uum.edu.my3
Article History: Received: 10 November 2020; Revised: 12 January 2021; Accepted: 27January 2021; Published online: 05April 2021
Abstract:In the era of Industry Revolution (IR) 4.0, business and industry are being transformed by a new wave of digital technology. In order to boost the economy’s prosperity in Malaysia, skilled workforce or well-trained manpower is vital in accomplishing the goal.However, it requires mainstreaming Technical and Vocational Education Training (TVET)in education system by providingcomprehensive training, effective research consultancy, holistic courses, collaboration, student placement and program attachment opportunity. Coherent from this issue, the government can produce more skill workers that can handle the rapid changing world of work. In Malaysia, there are more than 1000 TVET institutions, where 506 are considered as public institutions. However, itstill receives less attention by the students after secondary education. The identified potential factors are TVET instructors, current policy in Malaysia, social perception, employers’ perception, parents, facility, education cost and student themselves. Therefore, this study aims to rank these factors according to the levels of importance using Analytic Hierarchy Process (AHP) method. AHP is a method used to rank criteria by assigning the weight for each criterion. In this study, primary data is collected using questionnaires from 32 TVET instructors of Institut Kemahiran Belia Negara (IKBN) in northern region of Malaysia. The result of AHP shows that the variable of parents is the most influential factor with the weight of 18.81%, followed by the variable of facilities (18.56%). Conversely, the least influential factor is the variable of social perception with the weight of 7.21%. Hence, the government should implement appropriate strategies to attract more secondary school students to enroll in TVET programs. Due to the growth of skilled workers, our country is expected to transform the landscape of the manufacturing industry over the next decade. Hence, developingthe country’s productivity and curbing youth unemployment.
Keywords:Industry Revolution (IR) 4.0, Technical and Vocational Education and Training, Multi-Criteria Decision Making,Analytic Hierarchy Process
1. Introduction
The current world is undergoing a technological transformation as a new era of growth. In order to be competitive, the nations are focusing on high-tech technologies that can enhance economic development (Perpustakaan Negara Malaysia, 2018). Therefore, the implementation of policies related to Industry Revolution (IR) 4.0 can assist Malaysia to have a strong competitiveness fromthe dimensions of manufacturing and technologies. IR 4.0 aims to boost industrial development in terms of productivity, efficiency and convergent of technology. One of the best ways to succeed in IR 4.0 is through Technical and Vocational Education and Training (TVET) which acts as a catalyst for IR 4.0 (H. Alias and N. Hamid, 2018). This implies that TVET has a close relationship with the IR 4.0. TVET is an education process that provides students with different kinds of knowledge such as general knowledge, technical skills and theories related to technologies (UNESCO, 2003). In simple words, it is an education that mainly focusing on the industrial skills and practical skills. TVET would produce students with sufficient knowledge and skills in managing and monitoringthe new high technology system or equipment(Sulaiman, Norlisa, and Mohd Salleh, Kahirol, 2016). In conjunction with the high skills learned,TVET provisions contribute to enormous possibilities for industrialization and employment generation whilst boosting in national socio-economic development(Taherdoost, Hamed, 2017). However, majority of the Malaysia school leavers do not choose TVET as their first preference due to some potential factors;namely lack of English proficiency among TVET instructors, current national policy, negative social perception, negative employers’ perception, parents’ encouragement, poor facility in technical institution, high education cost and students’ personality and interest.Although these factors had been recognized, it seems that the previous strategies implemented by the government were not successful enough in increasing students' enrollment into TVET (Affero, Ismail, and Hassan, Razali, 2013). Therefore, this study aims to rank the factors affecting students’ choice in choosing TVET to assist the government in planning appropriate strategies for dealing with this issue.
2. Literature Review
As mentioned earlier, there are a few identified factors which hinder students’ interests from prioritizing TVET courses after completing secondary school. The first factor is TVET instructors, where some of them have less English proficiency. This means that they did not use English to conduct the lecture (Mohamad, Mimi Mohaffyza, et.al., 2009). Also, Information and Communication Technology (ICT) skillwas also an issue found in technical education. In creating the interactive environment in the classroom, teachers would use ICT skills to produce the teaching materials for students (Ismail, K, et.al., 2017). The lack of the ICT skills among teachersmight induce the students to feel bored during lecture and lost their interest into TVET. The next influential factor is current policy implemented in Malaysia(Khirotdin, Rd Khairina, et.al., 2019). Recently, there are two accreditation bodies in Malaysia which are Department for Skill Development (DSD) and Malaysian Qualification Agency (MQA). The existence of two different accreditation bodies led to different recognitions and raise confusion issues among students.Mohd Amin, Junainah, (2016) Stated that TVET graduates gotconfused to further their study due to those accreditations. The next factor is social perception. It is a common thinking in the public that TVET coursesare designed for those who are not excellent in academic. Due to this, the negative perception made the students in TVET perceive the inferiority(Affero, Ismail, and Hassan, Razali, 2013; Amedorme, Sherry K, et. al., 2013).
Consequently, some employers in the workplace did not recognize the qualification of TVET graduates. This could lead to unemployment issues among the TVET graduates (Sherry K, et. al., 2013).Cheong, Kee Cheok, (2016)Discussed that the employers felt that TVET students were not intelligent enough in their academic. The studentsbelieved that the skills they learnt in the class were different with what they applied in their work (Mou, Leping, 2018). Besides employers, parents also played a big role in influencing their children to enroll in TVET (Hussin, et al., 2017). Most parents encouraged their children to take academic courses instead of technical courses, iftechnical jobsdo not offer a good salary(Law, Chong Seng, 2018). The next influential factor is poor facilities in the technical institutions.Sherry K, et. al., (2013) Stated the TVET students were surrounded by a poor environment for training in the existing TVET institutions. This is due to some classrooms having no air-conditionand space. Moreover, there is also the problem of lack of equipment to train the TVET students.
The next influential factor is education cost in technical institutions (Blinov, 2019; Ismail, Affero,et al 2014). For some students whowere raised in poor families, education cost was always an obstacle for them. Despite the allocation of RM5.9 billion for TVET in Budget 2020, the National TVET Movement’s vice chairman stated that the allocation was insufficient. It was very common that the operation costs in the technical institution was high(The Star Online. October 12, 2019). The last factor is students’ personality and interest. This includes their demographic background, interest, personality, skill, and others. Interest was found to be an important factor for students to enroll in TVET (Affero, Ismail, and Hassan, Razali, 2013).Therefore, the vocational talent in each student is necessary to be identified. In this context, it refers to the student who had the vocationalaptitude, and should join vocational field (Bahtiar, et al., 2015).
Even though most of the factors for not choosing TVET as the first choice have been discovered in the literature,the level of importance for each factor has not yet been identified. In dealing with multi-criteria problems, Multi-Criteria Decision Making (MCDM) plays an important role because it can clearly show out the weightage of the preferences and the alternatives. It can deal with problems in our daily life. Although there are a few types of MCDM, they share the common characteristics such as multiple criteria to form the hierarchy, conflict among criteria, and assessment which might not conclusive.Examples of MCDM such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), and Simple Additive Weighting are written below.
i. Analytic Hierarchy Process (AHP)
AHP is developed by (SaatySaaty, Thomas L, 1980). AHP is a simple and flexible method in dealing with multi-criteria problems. It can divide the problems into smaller measures so it can be easily interpreted by decision maker. AHP will assign a weight for the criteria. A more important criteria will have a heavier weight. Therefore, the user can make the best decision based on the priority. AHP allows small inconsistency of the data (Ozdemir, Mujgan Sagir, 2005). According to (SaatySaaty, Thomas L, 1980), a model can be accepted if the consistency ratio is less than 0.1. Thus, this AHP model can be accepted as it fulfils the rule. Therefore, it acts as an effective method to analyze the decision (Brunelli, M. 2015; Taherdoost, Hamed, 2017). Previously, AHP is widely used in education field when the researchers want to investigate the factors that affect students in choosing schools. Myint, Kyi Kyi, (2019) had applied AHP in helping parents to decide which school is the best for their children. The researcher had studied the criteria such as school profile, quality of education, infrastructure, and others. Besides that, some researchers also discussed about the variables that influenced the
behavior of private university students in Bangladesh. AHP is used to analyze the most important factor among promotional activities, quality of teachers, service provided, infrastructureof campus and co-curricular activities (Anam, Shahriyar, et al., 2015).
ii. Analytic Network Process (ANP)
ANP is the generalization of AHP. It is developed by (Saaty, Thomas L, 1996). It forms a network structure based on the problem. It is a method in solving complex decision problems by simplifying the complicated problems (Gu, Wei, Saaty, 2018). Previously, ANP is applied in the studies related to technical and vocational field. Gu, Wei, Saaty, et al., (2018) Had applied ANP in determining the enterprise partner that the student will work with after they graduated. So, the researchers evaluated the criteria such as prospects of the industry, core technique, business scale, goodwill, and attitude of the manager in the enterprises. Also, the researchers applied ANP in evaluating the teachers’ performance in vocational colleges based on the criteria such as morality, ability, attendance and achievement, professional construction, teacher in charge and work bonus (Hu, Fang, Zhu, LiLong, and Liu, AiRong, 2012).
iii. Simple Additive Weighting (SAW)
SAW is also known as Weighted Linear Combination (WLC) or scoring methods (Ali Reza Afshari, M. Mojahed, and Rosnah Mohd, 2010). The calculation depends on the weighted average. An evaluation score is calculated by multiplying the alternative and weights. Researchers Putra, Dede Wira Trise, et al., (2018) stated that SAW’s capacity is more accurate during making decision. Previously, SAW is also applied in vocational field. In order to select the best teacher, the researchers applied SAW by analyzing the variables such as experiences, highest education, dedication and etc (Sahara, Riad, et al., 2018). Besides that, SAW was also applied in determining the workplace for vocational school students. The students’ behavior was analyzed and the system will make a recommendation to students to choose their preferred company (Santoso, Aij, et al., 2018).
Based on the three examples of MCDM described above, ANP is not chosen as the technique in this study due to the long time taken to completedata acquisition. ANP is more complicated compared to AHP (Yellepeddi, et al., 2006). Moreover, ANP can also cause functional disorganization if there are many variables involved(Gu, Wei, Saaty et al., 2018). For the SAW, the values for the variables are required to be positive. Sometimes, the result obtained from SAW method is not logic (Kraujalienė, Lidija, 2019). Therefore, this study applies AHP to sort out the most important factor affecting students’ choice in selecting academic courses or technical courses.AHP is the most suitable method here, due to its flexibility and simplicity(Saaty, Thomas L. 1980). Besides that, it can be easily interpreted by decision maker. Moreover,it allows small data inconsistency.
3. Methodology
In this study, primary data were collected by distribution of questionnaires. 32 TVET instructors were selected as the samples from Institut Kemahiran Belia Negara (IKBN) Kuala Perlis. The questionnaire consisted of six questions which compared each influential factor to each other. The respondents were required to fill the questionnaire based on their perceptions towards the factors mentioned.
AHP was carried out to rank the level of importance according to the instructors’ perception. There were three important steps in generating the AHP model, namelydevelop hierarchy structure,complete the judgement matrix, andconsistency testing. The detailed steps in conducting AHP are discussed below.
i. Development of Hierarchical Structure
The criteriawerelinkedwith thehierarchy layer. Generally, the structure was divided into three types which are top layer, middle layer, and bottom layer. The top layer only consisted of one element which was the target of the problems. Middle layer was the intermediate criteria used to fulfill the targets. The bottom hierarchy was the alternatives used to solve the problems. The lines connecting them showed the relationship between each other. In this study, there were two levels shown in Figure 1 because it is aimed to find out the level of importance of the factors affecting students’ choice in choosing TVET.In this study, the criteria considered to affect the students’ choice in choosing TVET were: TVET instructors, national policy, social perception, parents, facility, education cost and students’ interest. This is presented in Figure 1.
(1)
(2) Figure 1. AHP hierarchy in factors affecting students’ choice
ii. Construction of Matrix
After the hierarchy was structured, we determined the importance for each criterion. Pairwise comparison was used to identify, to which extend how one element was more important than another one byusing a 9-point scale as shown in Table 1 (Saaty, Thomas L. 1980).
Table 1. Comparison between pairs of criteria. Level of
importance
Definition Explanations
1 Equal importance Both of the criteria have equal importance to the goal. 3 Moderate importance One criterion has slight importance compared to another one.
5 Strong importance One criterion has strong importance compared to another
one.
7 Very strong importance One criterion has very strong importance compared to another one.
9 Extreme importance One criterion has extreme importance compared to another
one.
2, 4, 6, 8 Intermediate values They are used to compromise between two judgments. iii. Consistency Measure
In pairwise comparison, the ranking for the criteria was obtained. In order to see whether a model was accepted or rejected, we examined its consistency measure.Consistency index was calculated by using the formula below. 𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦𝑖𝑛𝑑𝑒𝑥 =𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑜𝑓𝑐𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦𝑚𝑒𝑎𝑠𝑢𝑟𝑒 − 𝑛 𝑛 − 1 , Where 𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑜𝑓𝑐𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦𝑚𝑒𝑎𝑠𝑢𝑟𝑒 = 𝑟𝑜𝑤𝑎𝑣𝑒𝑟𝑎𝑔𝑒x𝑟𝑜𝑤𝑜𝑓𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑐𝑜𝑚𝑝𝑎𝑟𝑖𝑠𝑜𝑛𝑚𝑎𝑡𝑟𝑖𝑥, 𝑛 = 𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠.
After that, the consistency ratio was calculated by using the formula, 𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦𝑟𝑎𝑡𝑖𝑜 =𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦𝑖𝑛𝑑𝑒𝑥
𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑛𝑑𝑒𝑥 ,
Wherethe random index is given in the Table 2 below. Table 2. Random index
A consistency ratio which is less than 0.1 implies that the model can be accepted (Saaty, 1980). 4. Result
A geometric mean of all the matrix was calculated to find out the average number of all the matrix. All the criteria were compared with each other and the values represented the importance. Comparison of any criteria with its own would maintain the value “1.00”. The matrix below shows the complete comparison matrix.
Factors affecting students' choice
Instructor National policy
Social
perception Parents Facility Education cost
Students' interest
Matrix size 1 2 3 4 5 6 7 8 9 10
Table 3.Sum of Column
The complete comparison matrix was organized in a table. Table 3 shows the sum of each column.
Table 4 shows all entries which were divided by sum of each column, whereas Table 5 shows the average of each row.
Table 4. Division of entries
Table 5. The average for each row
Factors Student Public Instructors Employers Parents Facility Cost Policy TOTAL AVERAGE
Student 0.0976 0.1472 0.0869 0.0396 0.1272 0.0906 0.1100 0.0927 0.7917 0.0990 Public 0.0478 0.0721 0.0813 0.0483 0.1165 0.1036 0.0560 0.0512 0.5767 0.0721 Instructors 0.0886 0.0699 0.0789 0.0978 0.1150 0.0603 0.0518 0.1136 0.6758 0.0845 Employer 0.1479 0.0895 0.0488 0.0600 0.0770 0.0649 0.0582 0.0460 0.5922 0.0740 Parents 0.1662 0.1338 0.1486 0.1686 0.2166 0.2939 0.2231 0.1539 1.5046 0.1881 Facility 0.1920 0.1239 0.2330 0.1649 0.1313 0.1781 0.2632 0.1981 1.4846 0.1856 Cost 0.1429 0.2075 0.2454 0.1662 0.0602 0.1090 0.1612 0.2335 1.3259 0.1657 Policy 0.1169 0.1561 0.0770 0.2548 0.1562 0.0997 0.0766 0.1110 1.0483 0.1310
The relative weight for each criterion was obtained from the average of the row, and then changed into percentage. The weight for each factor is displayed in Table 6 below.
Factors Student Public Instructors Employers Parents Facility Cost Policy
Student 1.0000 2.0424 1.1022 0.6599 0.5872 0.5085 0.6827 0.8351 Public 0.4896 1.0000 1.0314 0.8053 0.5380 0.5815 0.3472 0.4614 Instructors 0.9074 0.9697 1.0000 1.6307 0.5309 0.3385 0.3215 1.0241 Employer 1.5152 1.2414 0.6190 1.0000 0.3555 0.3644 0.3609 0.4143 Parents 1.7027 1.8571 1.8837 2.8125 1.0000 1.6500 1.3840 1.3868 Facility 1.9667 1.7200 2.9545 2.7500 0.6061 1.0000 1.6332 1.7855 Cost 1.4643 2.8800 3.1111 2.7714 0.2778 0.6122 1.0000 2.1044 Policy 1.1975 2.1667 0.9765 4.2500 0.7212 0.5600 0.4752 1.0000 SUM OF COLUMN 10.2434 13.8772 12.6785 16.6799 4.6166 5.6151 6.2046 9.0116
Factors Student Public Instructors Employers Parents Facility Cost Policy
Student 0.0976 0.1472 0.0869 0.0396 0.1272 0.0906 0.1100 0.0927 Public 0.0478 0.0721 0.0813 0.0483 0.1165 0.1036 0.0560 0.0512 Instructors 0.0886 0.0699 0.0789 0.0978 0.1150 0.0603 0.0518 0.1136 Employer 0.1479 0.0895 0.0488 0.0600 0.0770 0.0649 0.0582 0.0460 Parents 0.1662 0.1338 0.1486 0.1686 0.2166 0.2939 0.2231 0.1539 Facility 0.1920 0.1239 0.2330 0.1649 0.1313 0.1781 0.2632 0.1981 Cost 0.1429 0.2075 0.2454 0.1662 0.0602 0.1090 0.1612 0.2335 Policy 0.1169 0.1561 0.0770 0.2548 0.1562 0.0997 0.0766 0.1110
Table 6. Weights for each factor
From the result shown in Table 6,we found that parents had the heaviest weight of 18.81%, followed by technical institution’s facilitywhich was 18.56%, education cost which was 16.57%, government’s policy which was 13.10%, TVET instructors which was 8.45%, employers’ perceptionwhich was 7.40%, and the least is social perception which was7.21%.
Finally, Table 7 shows the consistency measures for each factor.Consistency measureswere used to calculate consistency index.
Table 7.Consistency measures for each factor
Consistency index was calculated based on the formula (2). Therefore,the consistency index obtained was equal to 0.0587 and consistency ratio was equal to 0.0416. Since the consistency was less than 0.1, therefore the model was accepted.
5. Conclusion and Recommendation
This paper lists and compares the influential factors of students’ tendencies in choosing TVET after completing secondary school by using AHP. Our results revealed that parents play the most important role in determining their children to enroll in TVET because this variable has the heaviest weight among others, while the least important variable is social perception. Therefore, many appropriate strategies need to befocusing on parents, in order to increase the number of students choosing TVET institutions. First, the government should always provide useful information to parents to abolish negative perception towards TVET.In addition, the government can supplycertain incentives to the families whose children are enrolled in technical courses. Other than the strategies stated above, the government should often organize campaigns to raise the awareness among parents so that they will realize the benefits of technical courses. This may help in raising positive perception of parents towards TVET, and immediately will be supportive towards children’s decision in joining technical courses in the long run.
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