Scholarly article on topic 'Evaluating the Soft Skills Performed by Applicants of Malaysian Engineers'

Evaluating the Soft Skills Performed by Applicants of Malaysian Engineers Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Azami Zaharim, Ibrahim Ahmad, Yuzainee Md Yusoff, Mohd Zaidi Omar, Hassan Basri

Abstract The paper proposed a formula to calculate the soft skills performed by engineering graduates. The equation derived from a finding of survey on employer's preference for employability skills. The input collected from a sample of 301 engineering industries employers from twelve natures of business located in Kelang Valley. The survey addressed a number of questions related to the level of requirement of engineering employability skills according to their industry's needs. The findings in terms of skills development derived differences weight among the skills required by the industries. This study focuses on soft skills valued by employers that slightly higher than technical skills for engineering professionals. The paper has a suggestion for employers and undergraduates on measuring the soft skills performed by young graduates. Furthermore, employers facing evaluation quality assessment might find this approach provides useful evidence for the assessment process to the job interviewed candidate.

Academic research paper on topic "Evaluating the Soft Skills Performed by Applicants of Malaysian Engineers"

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Procedia - Social and Behavioral Sciences 60 (2012) 522 - 528

UKM Teaching and Learning Congress 2011

Evaluating the Soft Skills Performed by Applicants of Malaysian

Engineers

Azami Zaharima*, Ibrahim Ahmadb, Yuzainee Md Yusoffb'c, Mohd Zaidi Omarc,

Hassan Basric

aHead Centre for Engineering Education Research, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia b College of Engineering, Universiti Tenaga Nasional (UNITEN), Malaysia c Centre for Engineering Education Research, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

Abstract

The paper proposed a formula to calculate the soft skills performed by engineering graduates. The equation derived from a finding of survey on employers' preference for employability skills. The input collected from a sample of 301 engineering industries employers from twelve natures of business located in Kelang Valley. The survey addressed a number of questions related to the level of requirement of engineering employability skills according to their industry's needs. The findings in terms of skills development derived differences weight among the skills required by the industries. This study focuses on soft skills valued by employers that slightly higher than technical skills for engineering professionals. The paper has a suggestion for employers and undergraduates on measuring the soft skills performed by young graduates. Furthermore, employers facing evaluation quality assessment might find this approach provides useful evidence for the assessment process to the job interviewed candidate.

© 2011Published byElsevierLtd. Selection and/orpeer reviewedunderresponsibilityofthe UKMTeachingand LearningCongress 2011

Keywords: Engineering graduates; employers; soft skills; employability skills; measurement

1. Introduction

Today, every new graduate requires employability skills to succeed. Leaders in government are calling for new graduates to demonstrate mastery of employability skills such as communication skills, teamwork, problem solving and decision making skills. They wanted the newly graduates be able to 'know-how' to solve real-world problems. Consequently, higher education provider need to ensure that all graduates are qualified to succeed in work and life in this new era of the global economy (Zaharim et al. 2010). Higher education provider, employers and government need to have a common understanding of set of skills should be owned by engineering graduates. As a result, several studies and projects had been conducted to find this set of employability skills and presented a few numbers of

* Corresponding author. Tel.: +6-03-8921-6466; fax: +6-03-8925-2546. E-mail address: azami.zaharim@gmail.com.

1877-0428 © 2011 Published by Elsevier Ltd. Selection and/or peer reviewed under responsibility of the UKM Teaching and Learning Congress 2011 doi:10.1016/j.sbspro.2012.09.417

frameworks related to employability skills (DEST 2002; Zaharim et al. 2009; Zaharim et al. 2010). In EDUCON2010, Zaharim et al. (2010) proposed a framework of engineering employability skills for Malaysian namely Malaysian Engineering Employability Skills (MEES). The framework shows an integrated of technical and nontechnical skills that comply with the requirement of accreditation body and employers needs. However, standing in the way of integrating such skills is about measurement. The measuring skills are difficult, and different definitions and methods have been used (Borghans et al. 2001). Measuring a student's knowledge is discrete facts but measuring a student's skills and ability to apply knowledge is ambiguous situations. Therefore, this study intent to develop a formulation to measure skills based on the criteria presented in MEES (Zaharim et al. 2010) and the finding in a study conducted by Yuzainee et al. (2011).

2. Literature Review

The employability skills refer to the required skills to acquire, keep and doing well on a job (Robinson 2000). Skill is an ability to perform a specific task (DEST, 2006) and employability is about having the capability to gain initial employment, maintain employment and obtain new employment if required (Hillage, 1998). Liz Reisner explained that there is a way to measure some of these skills (Elena, 2009). He said that "it might be possible to assess decision-making skills by analyzing the middle school participants' selection of high-quality college preparatory high schools". A report by Elena Silva, a Senior Policy Analyst, revealed that the skills "can be measured accurately and in a common and comparable way" (Elena, 2009). Studies on employability skills differed with regards to direct or indirect measurement depend on occupational title, qualification and level of education, years of work experience and numbers of training (Ashton & Green, 1996). Measuring the scores of the employability skills is subjective and depends on the perception of evaluators. The employability scores are determined by the particular combination of soft skills, and by the personal knowledge of the individuals.

3. Methodology

The data used in this study is a part of the data collected from engineering industries in the Kelang Valley, Malaysia for identifying employability skills of engineering graduates in Malaysia. The survey focuses on technical and soft (nontechnical) skills in an engineering discipline (Yuzainee et al. 2010). In this paper, the focus is on data that were obtained from questionnaires regarding the level of requirement of nontechnical/ soft skills. The responses were collected from 301 out of a random sample of 500 potential employers of engineering graduates around Kelang valley, Malaysia in September 2009 to January 2010. The soft skills selected for statistical testing in this study are the soft skills required in the relevant literature (DEST 2002; Lee 2003; Hassan et al. 2006; Zaharim et al. 2009; Zaharim et al. 2010). There were fifty attributes used to examine the required employability skills as valued by employers when hiring fresh engineering graduates. These fifty attributes grouped into ten skills that are communication skills (EES1), teamwork (EES2), lifelong learning (EES3), professionalism (EES4), problem solving and decision-making skills (EES5), competent in the application and practice (EES6), knowledge of science and engineering principles (EES7), knowledge of contemporary issues (EES8), engineering system approach (EES9) and competent in specific engineering discipline (EES 10) (Zaharim et al. 2010). Based on previous study, the tangible skills such as EES1, EES2, EES3, EES4 and EES5 are defined as soft skills and the other are technical skills (DEST 2002; Lee 2003; Hassan et al. 2006; Zaharim et al. 2009).

The respondents were high-rank officers in the companies to assure accurate results. They were divided in six levels of position in the organisation, and twelve types of industry's nature of business as presented in Table 1. Data collected were through face-to-face interviews, telephone interviews, email and snow-ball sampling. About 337 out of 500 engineering's employers responded and only 301 usable responses were analyzed. The data collected was analyzed using basic statistical method to present profile of respondents involved in this study.

Table 1. Profile of respondents based on position in company and nature of business.

Nature Chairman Chief Officer Director Manager Senior Engineer Others Total %

N1 0 0 0 2 3 1 6 2.0

N2 0 0 0 3 1 2 6 2.0

N3 0 1 0 7 10 1 19 6.3

N4 0 0 1 4 8 0 13 4.3

N5 2 1 4 18 15 2 42 14.0

N6 0 0 0 2 1 0 3 1.0

N7 0 0 1 9 12 0 22 7.3

N8 0 0 1 9 11 2 23 7.6

N9 0 2 1 25 21 6 55 18.3

N10 0 0 0 16 14 0 30 10.0

N11 0 1 1 20 21 1 44 14.6

N12 0 1 6 13 15 3 38 12.6

Total 2 6 15 128 132 18 301

% 0.7 2.0 5.0 42.5 43.9 6.0 100

N 1-Healthcare and Social; N2-Leisure and Entertainment; N3-Education; N4-Commerce,Trade and Finance; N5-Communications and IT; N6-Defence and Security; N7-Transport; N8-Agriculture and Food; N9-Engineered Materials; N10-Energy and Natural Resources; N11-Built Environment; N12-Consulting.

The questionnaire requires respondents to assess quantitatively on the level of requirement of each identified skill that should be owned by engineering graduates. Each skill was measured using a five-point Likert scale representing different level of requirement of skills. The responses "1" indicates "Extremely Not Required", "2" indicates "Not Required", "3" indicates "Slightly Required", "4" indicates "Required" and "5" indicates "Extremely Required". The weight, Normalised Skill Weight (NSW), index and level of requirement of skills were analyzed using the multi-attribute value technique (MAVT) adapted from Fishburn (1967) dan Keeney and Raiffa (1976). The result has been presented in EDUCON2011 as shown in Appendix. This paper is an extension of the study presented in EDUCON2011 (Yuzainee et al. 2011) with the intention to proposed a new mathematical formula to measure the soft skills identified in the study presented in EDUC0N2010 (Zaharim et al. 2010).

4. Data Analysis and Computation of Results

The NSW of five soft skills (coded EES1-EES5) calculated using the weight of skills obtained from a report presented by Yuzainee et al. (2011). The NSW determined by Equation 1, adopted from Fishburn (1967) dan Keeney and Raiffa (1976). To evaluate the score for engineering job applicant, Equation 2 was derived using NSW.

NSWn = —^— x 100 t Xn

n = l (1)

NSW - Normalised Skill Weight Xn - Weight of skill i - Number of skills (i = 5) n - nth term

Employability Score = ^(Vn * Sn

Vn - Value of Normalised Skill Weight

Sn - Score of skill obtain by applicant (Mark/50)

i - Number of skills (i = 5) n - nth term

For this study, the calculation using Equation 2 illustrated as following:

Employability Score = (20.5) S1 + (20.4) S2 + (19.4) S3 + (19.9) S4 + (19.8) S5

Table 2 shows an example of employability score obtained by three applicants using Equation 2 compared to percentage and average score.

Table 2. Example of score for three applicants

Skills Code Weight NSW Full marks Candidate 1 Candidate 2 Candidate 3

Communication skills EES1(S1) 0.1048 20.52 50 20.52 30 12.31 25 10.26 45 18.47

Teamwork EES2(S2) 0.1043 20.44 50 20.44 35 14.31 30 12.26 40 16.35

Lifelong Learning EES3(S3) 0.0988 19.35 50 19.35 45 17.42 35 13.55 35 13.55

Professionalism EES4(S4) 0.1014 19.86 50 19.86 40 15.89 40 15.89 30 11.92

Problem solving and decision making skills EES5(S5) 0.1012 19.82 50 19.82 25 9.91 45 17.84 25 9.91

0.5104 100 250 100 175 69.84 175 69.80 175 70.20

Percentage / Average 100 50 70 35 70 35 70 35

5. Results and Discussion

The evaluators of job interview judge the candidate according to a different level of preference (1-50 points). According to Ryan& Hughes (1997) and agreed by Vick & Scott (1998) that the level of preference should be realistic and informative to make it competitive choices. In addition, the range of levels of preference should provide enough variation. The positive level of preference should be used because it does not seem reasonable for candidates with negative levels of preferences. Table 2 shows the example of employability score for five soft skills owned by three candidates. Total mark, percentage, and mean score are equal for these three candidates though they have different abilities. Equation 2, the equation for employability score gives different value for these three candidates based on the coefficients of NSW assigned to each skill. The coefficient shows that communication skills (20.5) are the skill with the strongest effect on the candidates, and it considered being most influential and required skills for the candidates. Based on the coefficients of NSW, teamwork skills ranked as second, and professionalism as third required skills, while problem solving and decision-making skills are relative the least important for the candidate of engineer professionals.

The example illustrated in Table 2 shows that Candidate 3 is the first choice followed by Candidate 1 as a second choice to succeed in a job interview. Candidate 3 shows better competencies in communication skills and teamwork compared to the other two candidates. This gives him better opportunity to succeed in the interview.

6. Conclusion

Previous studies on employability skills confirmed the significant of technical and nontechnical skills (DEST 2002; Lee 2003; DEST 2006; Hassan et al. 2006; Zaharim et al. 2009; Zaharim et al. 2010; Yuzainee et al. 2011). Soft skills are close related to skills required in various industries including engineering sector. Well-performed skills are the selling values/attributes that employers looking for in the engineering entry level jobs.

This study contributes to the discussion on the measurement of soft skills during the job interview in engineering sector. The finding shows that employers in the engineering firm seem to be more interested in graduates who can communicate well and work in group effectively. They believed lifelong learning is less required than the other four skills since the skills can be obtained through job training. These results can be expected as employer spend most of their time dealing with employees, for which excellent communication skills are essential. Engineering graduates need to realise that having a good degree is no longer sets them apart from other candidates in today's job hunting. Graduates must be able to market themselves by performing good soft skills as well technical skills.

Acknowledgement

We would like to thank UKM for providing the research grant (UKM-GUP-NBT-08-26-097 and UKM-OUP-NBT-28-131/2011).

References

Ashton, D. N., & Green, F. (1996). Education, training and the global economy. Cheltenham: EdwardElgar.

Borghans, L., Green, F., & Mayhew, K.. (2001). Skills Measurement and Economic Analysis: An Introduction. Oxford Economic Papers, 53(3), 375-384.

DEST. (2002). Employability skills for Australian industry: literature review and framework development. Employability skills for the future, a report by the Australian Chamber of Commerce and Industry and the Business Council of Australia for the Department of Education, Science and Training, Canberra.

DEST. (2006). "Employability skills from framework to practice, an introductory guide for trainers and assessors", a report by the Australian Chamber of Commerce and Industry and the Business Council of Australia for the Department of Education, Science and Training, Canberra.

Elena Silva. (2009). Measuring Skills for 21st-Century Learning. Phi Delta Kappan, 90 (09), 630-634. Fishburn, P.C., (1967). Methods of estimating additive utilities. Management Science, 13(7)

Hassan Basri, Mohd Zaidi Omar, Zainai Mohamed, Abang Abdullah Abang Ali, Badrulhisham Abdul Aziz, Abdul Hamid Hamidon, Zaidi Mohd Ripin, Nik Abdullah Nik Mohamad dan Azmi Hassan. (2006). The Future of Engineering Education in Malaysia, Ministry of Higher Education, Malaysia. ISBN 983-2982-1318, Book. September 2006. Hillage, J. and Pollard, E., (1998). Employability: Developing a Framework for Policy Analysis, Research Report No. RR85, Department for

Education and Employment (DfEE), London. Keeney, R. L. and Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. Wiley, New York. Lee Fui Tong. (2003). "Identifying essential learning skills in students' engineering education, Monash University Malaysia. http://surveys.

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Appendix: Level of requirement of each employability skills

Skills Criteria No. Skills and Criteria Mean Weight Index Rank

EES1 Communication skills 4.25 0.1048 1.0000 [1]

1.1 Speak in clear sentences 4.39 0.2063 1.0000 1

1.2 Give clear direction 4.26 0.2002 0.9704 4

1.3 Listen and ask question 4.27 0.2006 0.9726 3

1.4 Present ideas confidently and effectively 4.37 0.2053 0.9954 2

1.5 Understand and speak English and other languages 3.99 0.1876 0.9096 5

EES2 Teamwork 4.24 0.1043 0.9961 [2]

2.1 Function effectively as an individual 4.25 0.2005 0.9659 3

2.2 Understand the role in a group 4.40 0.2076 1.0000 1

2.3 Function effectively in a group as a team member 4.36 0.2058 0.9917 2

2.4 Accept and provide feedback in constructive and considerate 4.20 0.1981 0.9545 4

manner. (Forming, storming, performing, adjourning)

2.5 Work in a group with the capacity to be a leader. 3.98 0.1880 0.9060 5

EES3 Lifelong Learning 4.01 0.0988 0.9431 [7]

3.1 Recognize the need to undertake lifelong learning 4.06 0.2024 1.0000 1

3.2 Possess and acquire the capacity to undertake lifelong learning 4.01 0.1999 0.9877 3

3.3 Engage in lifelong learning 4.03 0.2008 0.9918 2

3.4 Set their personal learning targets. 3.98 0.1983 0.9795 5

3.5 Plan in achieving their learning goal(s) 3.98 0.1986 0.9811 4

EES4 Professionalism 4.11 0.1013 0.9672 [3]

4.1 Understand the social responsibilities. (human factors and social 4.07 0.1980 0.9599 4

issues)

4.2 Understand the cultural and global responsibilities. (Awareness 4.02 0.1953 0.9466 5

on cultural and nature surrounding)

4.3 Understand the environmental responsibilities. (Aware of 4.08 0.1985 0.9623 3

environmental needs)

4.4 Commit to professional responsibilities. (Be professional as an 4.24 0.2063 1.0000 1

Engineer).

4.5 Commit to ethical responsibilities. (Be accountable for their 4.15 0.2019 0.9788 2

actions)

EES5 Problem solving and decision making skills 4.11 0.1011 0.9655 [4]

5.1 Undertake problem identification. (identify problem in work 4.05 0.1974 0.9658 4

place)

5.2 Implement problem solving. (use experiences to solve problem) 4.09 0.1994 0.9754 3

5.3 Apply formulation and solution. (use science, mathematics or 4.05 0.1974 0.9658 5

technology to solve problem)

5.4 Be creative, innovative and see different points of view in solving 4.20 0.2044 1.0000 1

problems.

5.5 Identify the root cause of the problems. 4.14 0.2015 0.9857 2

EES6 Competency 4.11 0.1011 0.9654 [5]

6.1 Use the necessary techniques for engineering practice. 3.99 0.1943 0.9403 5

6.2 Use the necessary skills for engineering practice. 4.04 0.1966 0.9513 4

6.3 Use the modern engineering tools and software. 4.19 0.2039 0.9866 2

6.4 Work toward quality standards and specifications. 4.24 0.2067 1.0000 1

6.5 Assemble equipment following written directions. 4.07 0.1984 0.9599 3

Skills Criteria No. Skills and Criteria Mean Weight Index Rank

EES7 Knowledge of science and engineering principles 4.05 0.0997 0.9520 [6]

7.1 Continue to acquire knowledge of sciences and engineering fundamentals. 3.93 0.1941 0.9547 5

7.2 Apply the knowledge of engineering fundamentals 4.08 0.2015 0.9911 3

7.3 Select and use proper tools and equipments for particular job/task. 4.12 0.2033 1.0000 1

7.4 Access, analyse and apply skills and knowledge of science and engineering. 4.09 0.2022 0.9943 2

7.5 Understand principles of sustainable design and development. 4.03 0.1989 0.9781 4

EES8 Knowledge of contemporary issues 3.94 0.0971 0.9273 [8]

8.1 Continue learning independently in the acquisition of new knowledge, skills and technologies. 4.02 0.2040 0.9877 3

8.2 Use information technologies. (Computers, networks and electronic) 4.07 0.2066 1.0000 1

8.3 Use communication technologies in the knowledge-based era. 3.98 0.2018 0.9771 4

8.4 Use computing technologies. 4.05 0.2052 0.9935 2

8.5 Read news paper 3.60 0.1824 0.8830 5

EES9 Engineering system approach 3.87 0.0953 0.9097 [10]

9.1 Utilize a systems approach to design operational performance 3.90 0.2018 0.9807 4

9.2 Utilize a systems approach to evaluate operational performance. 3.92 0.2024 0.9841 3

9.3 Design systematically 3.95 0.2040 0.9916 2

9.4 Analyse engineering design 3.98 0.2057 1.0000 1

9.5 Demonstrate a knowledge and understanding of engineering system for management and business practices. 3.60 0.1861 0.9045 5

EES10 Competent in specific engineering discipline 3.91 0.0964 0.9199 [9]

10.1 Continue to acquire in-depth technical competence in a specific engineering discipline. (electrical, highway, structure etc) 3.89 0.1987 0.9774 3

10.2 Apply technical skills in a specific engineering discipline effectively 3.97 0.2031 0.9992 2

10.3 Design and conduct experiments 3.88 0.1985 0.9765 4

10.4 Analyse and interpret data 3.98 0.2033 1.0000 1

10.5 Apply knowledge in multidisciplinary engineering 3.84 0.1965 0.9665 5

Total requirement of employability skills 40.6