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Construction Education’s Simulation Study in the Fourth Industrial Revolution

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The Construction Industry in the Fourth Industrial Revolution (CIDB 2019)

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Abstract

The increasing gap between academia and industry is of concern. In engineering, universities are introducing simulation studies into their construction management (CM) curricula for context-based simulated experience for graduating candidates. Consequently, this study investigated the important CM graduate skills and attributes as a basis for CM simulation course design. The methodology adopted a longitudinal study of two cohorts of CM graduates using semi-structured online questionnaire, consisting of 30 literature-informed CM graduate skills and attributes. The average response rate was 78%. Descriptive data analysis was used to categorize the CM graduate skills and attributes into criticality zones based on mean scores (minor = 0 to 2.50; moderate = > 2.50 to 3.75; and major = > 3.75 to 5.00). Wilcoxon rank sum test proved that the two cohorts were equal. Planning and controlling, time management, communication and leadership skills were ranked highest while environmental awareness, research and statistical analysis as well as marketing and entrepreneurship skills were ranked lowest by both cohorts. While the increasing need for soft or non-technical skills is supported, implications for CM education include the need for more problem-oriented nested learning activities, creating the opportunities to test solutions much more practically, and industry-academia collaboration in the design and assessment of simulated tasks.

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Author information

Authors and Affiliations

  1. School of Construction Economics and Management, University of the Witwatersrand, Johannesburg, South Africa

    Oluwayomi Babatunde

Authors
  1. Oluwayomi Babatunde
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Corresponding author

Correspondence to Oluwayomi Babatunde .

Editor information

Editors and Affiliations

  1. Construction management and Quantity Surveying, University of Johannesburg, Johannesburg, Gauteng, South Africa

    Clinton Aigbavboa

  2. Construction management and Quantity Surveying, University of Johannesburg, Johannesburg, Gauteng, South Africa

    Wellington Thwala

Appendices

Appendix A. CM Skills and Attributes

CM skill and attribute

Code

CM skill and attribute

Code

Ability to conduct research

SKA1

Leadership capability

SKA16

Ability to exercise professional judgment

SKA2

Managerial knowledge

SKA17

Conflict and dispute resolution skills

SKA3

Marketing skills and entrepreneurship

SKA18

Know-how of surveying and leveling apparatus

SKA4

Measurement, costing and estimating

SKA19

Ability to work autonomously

SKA5

Numeracy/quantitative analytics

SKA20

Academic achievement

SKA6

Planning, scheduling and controlling

SKA21

Acceptance of responsibility

SKA7

Practical building knowledge

SKA22

Active listening and verbal communication

SKA8

Problem solving skills, creativity and innovation

SKA23

Malleability to dynamic work situation

SKA9

Supervisory skills and ability to train others

SKA24

Computer literacy

SKA10

Systems development ability

SKA25

Environmental knowledge

SKA11

Team building capability, trust and honesty

SKA26

Familiarity with construction quality management

SKA12

Time management

SKA27

Knowledge of the complex nature of the industry

SKA13

Up-to-date professional knowledge

SKA28

Financial management

SKA14

Work study

SKA29

Interpersonal skills

SKA15

Worker safety and health awareness

SKA30

Appendix B. CM Graduate Skills and Attributes: Ranking and Criticality

CM Graduate Skillsa

Cohort A

Cohort B

Mean

SD

Rank

Criticality

Mean

SD

Rank

Criticality

SKA1

3.65

0.91

28

Moderate

3.52

1.20

28

Moderate

SKA2

4.42

0.82

7

Major

4.00

1.13

19

Major

SKA3

4.42

0.75

6

Major

4.16

1.16

16

Major

SKA4

3.45

1.02

29

Moderate

3.44

1.24

29

Moderate

SKA5

4.42

0.67

5

Major

4.12

0.82

17

Major

SKA6

3.95

0.94

22

Major

3.92

0.89

20.5

Major

SKA7

4.35

0.79

9

Major

4.54

0.76

4

Major

SKA8

4.55

0.67

3

Major

4.60

0.57

2

Major

SKA9

4.21

0.83

14

Major

4.44

0.80

5

Major

SKA10

4.26

0.71

12

Major

4.04

1.08

18

Major

SKA11

3.94

1.08

24

Major

3.64

0.93

25

Moderate

SKA12

4.35

0.65

8

Major

4.32

0.84

10

Major

SKA13

3.84

0.87

27

Major

3.52

1.06

27

Moderate

SKA14

4.35

0.85

10

Major

3.64

1.20

26

Moderate

SKA15

4.15

0.85

18

Major

4.60

0.75

3

Major

SKA16

4.47

0.60

4

Major

4.40

0.57

6

Major

SKA17

4.00

0.92

20

Major

4.16

0.97

15

Major

SKA18

3.39

1.16

30

Moderate

3.20

1.33

30

Moderate

SKA19

3.85

1.01

26

Major

3.68

1.26

24

Moderate

SKA20

4.20

1.03

15

Major

3.76

1.42

23

Major

SKA21

4.60

0.73

1

Major

4.40

0.85

7

Major

SKA22

4.20

1.08

16

Major

4.24

0.76

11.5

Major

SKA23

4.30

0.71

11

Major

4.32

0.55

8

Major

SKA24

3.95

1.10

23

Major

4.32

0.68

9

Major

SKA25

4.00

0.84

19

Major

4.24

0.76

11.5

Major

SKA26

4.16

0.81

17

Major

4.16

0.83

14

Major

SKA27

4.56

0.68

2

Major

4.76

0.43

1

Major

SKA28

3.95

0.92

21

Major

3.92

1.16

22

Major

SKA29

3.90

0.99

25

Major

3.92

0.89

20.5

Major

SKA30

4.25

0.99

13

Major

4.20

0.85

13

Major

  1. aPlease refer to Appendix A for the full description of the CM graduate skills and attributes.

Appendix C. Ordered Data for Null Hypothesis

Code

Cohort A

Cohort B

Mean

SD

Rank

Mean

SD

Rank

SKA1

3.65

0.91

9

3.52

1.20

6

SKA2

4.42

0.82

51

4.00

1.13

24

SKA3

4.42

0.75

50

4.16

1.16

31

SKA4

3.45

1.02

4

3.44

1.24

3

SKA5

4.42

0.67

49

4.12

0.82

26

SKA6

3.95

0.94

20

3.92

0.89

15.5

SKA7

4.35

0.79

45

4.54

0.76

54

SKA8

4.55

0.67

55

4.60

0.57

57

SKA9

4.21

0.83

35

4.44

0.80

52

SKA10

4.26

0.71

39

4.04

1.08

25

SKA11

3.94

1.08

18

3.64

0.93

7

SKA12

4.35

0.65

44

4.32

0.84

43

SKA13

3.84

0.87

12

3.52

1.06

5

SKA14

4.35

0.85

46

3.64

1.20

8

SKA15

4.15

0.85

27

4.60

0.75

59

SKA16

4.47

0.60

53

4.40

0.57

47

SKA17

4.00

0.92

23

4.16

0.97

30

SKA18

3.39

1.16

2

3.20

1.33

1

SKA19

3.85

1.01

13

3.68

1.26

10

SKA20

4.20

1.03

33

3.76

1.42

11

SKA21

4.60

0.73

58

4.40

0.85

48

SKA22

4.20

1.08

34

4.24

0.76

36.5

SKA23

4.30

0.71

40

4.32

0.55

41

SKA24

3.95

1.10

21

4.32

0.68

42

SKA25

4.00

0.84

22

4.24

0.76

36.5

SKA26

4.16

0.81

28

4.16

0.83

28

SKA27

4.56

0.68

56

4.76

0.43

60

SKA28

3.95

0.92

19

3.92

1.16

17

SKA29

3.90

0.99

14

3.92

0.89

15.5

SKA30

4.25

0.99

38

4.20

0.85

32

Total

  

958

  

871

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Babatunde, O. (2020). Construction Education’s Simulation Study in the Fourth Industrial Revolution. In: Aigbavboa, C., Thwala, W. (eds) The Construction Industry in the Fourth Industrial Revolution. CIDB 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-26528-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-26528-1_31

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  • Online ISBN: 978-3-030-26528-1

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