Employer Perceptions of Student Ability during Industrial Training as Assessed by the Rasch Model Academic research paper on "Economics and business"

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Social and Behavioral Sciences

Procedia - Social and Behavioral Sciences 102 (2013) 134 - 140

6th International Forum on Engineering Education (IFEE 2012)

Employer Perceptions of Student Ability during Industrial Training as assessed by the Rasch Model

S.A. Osman*,S.I. Naam, M.Z. Omar, N. Jamaluddin, N.T. Kofli, A. Ayob & S. Johar

aFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia,43600, UKM Bangi, Selangor, MALAYSIA

Abstract

This paper presents the Rasch Measurement Model to determine employer perceptions of student ability during industrial training. A questionnaire survey was completed by 280 students from four departments (JKAS, JKMB, JKKP, and JKEES) in the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM). The survey consists of 20 questions designed by the faculty that described attributes based on the Program Outcomes (POs) that need to be answered by the employers at the end of an industrial training session. Employers were required to answer the question using a Likert scale of 1 to 5 (1 = Not Satisfactory, 5 = Most Satisfactory). Overall, most of the employers were satisfied with the students' ability to interact (Item Q12) because they gave good marks to most of the students, but most of them were dissatisfied with the students' leadership ability (Item Q4). The performance of the engineering students in the industrial training program was better than the expected performance; only 4 students were located below the Meanitem (poor students), and the rest of the students (N = 276) were above the Meanitem (top students). This result proved that the Rasch Measurement Model can precisely describe the performance of each student during the training program, allowing the students' performance for each attribute to be determined. This result can also be used by the faculty to better prepare the students before the industrial training program.

© 2013TheAuthors. PublishedbyElsevierLtd.

Selectionand/orpeer-review under responsibilityofProfessorDr Mohd. Zaidi Omar,AssociateProfessor DrRuhizanMohammadYasin, DrRoszilah Hamid,DrNorngainy Mohd.Tawil,AssociateProfessorDr WanKamal Mujani, Associate Professor Dr Effandi Zakaria.

Keywords: Rasch Model; industrial training; survey; employer 1. Introduction

Currently, most companies would like to hire well-trained graduates with excellent qualifications and excellent skills. In addition to the conventional methods of learning, industrial training programs are also important for providing students with the knowledge and experience needed to work as an engineer. Students can also learn new skills that are sometimes not taught in the university, such as communication skills among peers and technical writing. Having an industrial training program in the university curriculum would definitely benefit

Corresponding Author name. Tel.: +0-603-8911-8362 E-mail address: saminah@eng.ukm.my

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.

Selection and/or peer-review under responsibility of Professor Dr Mohd. Zaidi Omar, Associate Professor Dr Ruhizan Mohammad Yasin, Dr Roszilah Hamid, Dr Norngainy Mohd. Tawil, Associate Professor Dr Wan Kamal Mujani, Associate Professor Dr Effandi Zakaria. doi: 10.1016/j.sbspro.2013.10.724

the students and simultaneously give them an advantage when looking for future jobs, as discussed by Osman et al. (2009).

For students in the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), the industrial training course is compulsory for all students before they can graduate from the university. This is also a requirement of the Board of Engineers Malaysia through the Malaysian Engineering Accreditation Council (EAC) on the university program (Omar et al. 2009). Students are only allowed to go for training if they have completed at least six full-time academic semesters. The training was carried out at various companies throughout Malaysia, from the government to the private sector, for twelve weeks. By the end of the session, employers will have to evaluate the skills obtained by the student based on their performance during the industrial training. According to Omar et al. (2009), this evaluation can be used as a tool in measuring student ability for specific attributes from the employers' point of view.

In this study, the students' performance during the industrial training program was measured through questionnaires completed by four engineering departments: Civil and Structure Engineering (JKAS), Electrical and Electronic System Engineering (JKEES); Chemical and Process Engineering (JKKP) and Mechanical and Materials Engineering (JKMB). Based on the employer responses, the questionnaire results were then analyzed using the Rasch Measurement Model. According to Abd. Aziz et al. (2008), the Rasch Model is different from other conventional methods because in the Rasch Model, a more reliable and repeatable measurement instrument is produced rather than establishing a 'best fit line'. As stated in Saidfudin et al. (2010), the Rasch Measurement Model is an alternative measurement method that focuses on constructing a measurement instrument rather than correcting the data to fit the measurement model with errors. From the Rasch Model, the results from the employer responses were converted to a logit scale to obtain unidimensionality on a linear interval scale for better precision in measuring the students' performance for each attribute. The output obtained from this analysis can be used to determine the questionnaire's construct validity and identify unexpected patterns in the items and in student performance.

2. Methodology

A set of question containing 20 attributes, shown in Table 1, was distributed to the employer and needed to be answered at the end of the industrial training session. The attributes were designed based on the program outcomes, as outlined by the faculty, as in Omar et al. (2009). The evaluation for each question was carried out using a Likert Scale of 1 to 5 (1=Not Satisfactory, 5=Most Satisfactory) for each attribute. A total of 280 students in the third year program from four departments were involved in this study: JKAS = 62 students (S001 to S062), JKEES =59 students (S063 to S121), JKKP = 50 students (S122 to S171) and JKMB = 109 students (S172 to S280).

Table 1 Attributes Measured During Training

Question No. Attributes Question No. Attributes

Q1 Adequate background knowledge Q11 Ability to extract information from various sources

Q2 Ability to apply knowledge Q12 Ability to interact

Q3 Ability to function as a team player Q13 Listening skills

Q4 Ability to function as a leader Q14 Negotiation skills

Q5 Ability to carry out instructions Q15 Multicultural and multiracial awareness

Q6 Possess good work ethics and be Q16 Nonverbal Skill

professional

Q7 Social, cultural, humanity responsibility Q17 Ability to express ideas (verbal)

Q8 Awareness on related global and environmental issues Q18 Ability to express ideas (written)

Q9 Disciplined and motivated Q19 Comprehension

Q10 Recognize the need for lifelong learning Q20 Punctual and Independent

Each question sheet containing responses from an employer was evaluated and tabulated in Excel*prn format for further analysis in the Rasch software Winstep. The raw scores were transformed to logit values, and the outputs obtained from the analysis were analyzed and are discussed in this paper.

3. Discussion and Findings

Figure 1 shows the Person-Item Distribution Map (PIDM) from the analysis of the Rasch Measurement Model in Winsteps. On the right side of the PIDM, it shows the 'Person' spread, which refers to the engineering students, whereas the left side of the PIDM shows the 'Item' spread, which refers to the 20 attributes evaluated by the employers. There are 280 persons and 20 items measured in this analysis in which person and item are plotted on the same logit scale. Compared with traditional histogram tabulation, the PIDM allows the person and the item to be mapped together and give a better view of the exact performance of all students during the industrial training program (Abd. Aziz et al. 2008).

TABLE 16.3 INTERNSHIP (EMPLOYER) ZOUOJ2WS.TXT ]ul 12 2:12 201i

INPUT: 230 Persons 20 Items MEASURED: 2 30 Persons 20 Items 5 CATS 3.63.2

ems - MAP - Persons <Higtl> | Jpffllmf.

S053 5175

S067 5132

S072 5137

5093 5213

5109 5229

Sill 52 60

S132 52 66

PERSON MAX = 10. 17

5095 5026 SO 17 505 3 5059 SOIS 5050 5236 5020 S001 5116 5043 SOOS S160

ITEM MEAN = 0.00

T| Ql I

Q20 Q3 5+ Q9 I

Q10 QU Q7 I

[M 5023 t- 5015 S17 8 5253 1 5033 I S003 5113 5273 * S016 510S 5164 [ 5014 I S031 S002 5142 SO 4 4 S02 4

S006 SOOS

019 Q2 M+T SOlO

515 6 5162 5133 52 63

5090 5105 512 4 5151 5271 52 30

SO 91 S200 5264

SO 6 4 5137 5190 SI 93 S207 S226 S23 4

5075 5083 5135 5140 5179 527 4 5275

5115 5120 5130 SIES S177 5197 522 3 5235 5251

S070 S037 S092 SO 99 S13S 5157 S192 5196 5213

523 3 5259 5262 5263

5047 5055 5033 5119 5159 5202 52 06 5246

S009 5035 5046 5065 S076 S077 5096 5104 5107

S144 SI 45 5166 S204 S2 41 S2 44 52 5 6 5261 5273

5073 5102 5133 5191 5209 5220

5021 5023 5030 S034 5063 5069 5071 5079 5034

5212 S2 4S 525 4 S257 52 69 S272

S03 6 503 9 5094 5110 S155 SI 35 5139 5216

5031 5114 5121 5139 5141 5143 5153 5172 517 4

S201 S203 5215 S222 S224 S237 S2 42 S24E 5252

5255 5267 5279

5037 5093 5097 5152 5176 5211 525 3

502 2 SO 40 5051 SO 66 S074 5036 5039 5103 5117

S143 S170 5130 S133 S214 S223 52 2 5 5231 52 47

5019 5025 5027 S041 SO 45 5057 S060 5062 5101

5113 5123 512 6 SI29 S131 S13 4 513 6 S149 5161

5173 5199 5233

5043 5122 5163 5195 S19S 5232 52 40

5032 503 3 S078 5125 S277

S004 S007 SOU SO 42 SO 4 9 505 4 5112 5127 512 3

5171 5131 5136 5194 5203 5210 5219 5221 5270

505 6 5035 5167 5134 5217 5250 5265

S061 5150 515 4 S2 49

5013 5052 5063 5032 5100 5165 5230 5239

5Q3Q_ 5106

Q15 Q3 Q5 Q6 [ Q14 Q17 Q1S I Q13 Q16 S+

52 OS S163 5227

! [s2 43

< 1 >!»> I <POQr>

Figure 1. Person-Item Distribution Map (PIDM)

Overall, the performance of the engineering students in the industrial training program is above the expected performance; the Person mean value, Meanperson, is 4.43, which is higher than the threshold value, Meanltem = 0 (Osman et al. 2012). Only 4 students are located below the Meanitem, while the rest of the students (N = 276) are above the Meanitem.

According to Figure 1, the easiest item would be Q12, which addresses the ability to interact, while the most difficult item is Q4, which addresses the ability to function as a leader. The figure also shows that if the location of an item in the PIDM is higher than the Meanitem, the item is considered more difficult compared with the item at the bottom of the Meanitem. This is why the Meanitem is set to zero and acts as the threshold value on the logit scale (Osman et al. 2012). Even so, more than half of the students (N = 229) are located above the mean for Q4, which indicate that the performance of these students is excellent. The ability of these students exceeds the difficulty level of the skills measured. Most of the employers were satisfied with the ability possessed by these students and gave them good mark. However, there was one student (S243) located below mean on the easiest item, Q12.

As stated in Osman et al. (2012), the location of the separation between the item and the person shows the ability of the students for each attribute. If the separation is large, the ability of the student to obtain a high mark on each item is high. For example, the distances between the top students, i.e., S012, S053, S067, S072,

S098......S276 (marked with the red box) and the easiest item, Q4, are large, which indicate that employers gave

the highest mark to these students for the respective item. The person and item distribution in the PIDM is not well spread because there is a blank area at the top of the item section. This blank area needs to be corrected so that the item's difficulty and a person's ability are correlated.

The summary statistics for the person and item are shown in Figure 2 below. From the figure, the value of the Cronbach-a is 0.96, with high percentage of valid responses of 99.7%. The value is quite high and above the required level of 0.6 for a 95% confidence interval: p=0.05 (Abd. Aziz et al. 2008). The Person Reliability and Item Reliability values are also excellent, 0.93 and 0.98, respectively. The person separation, G, is good (3.68), which means that the student performance level can be separated into 3 different levels: excellent, good and poor students. The item separation is also large, 6.55, and according to Saidfudin et al. (2010), the value shows a very good differentiation for item difficulty in separating the students into different difficulty levels.

TABLE 3.1 INTERNSHIP (EMPLOYER) input: 2so Persons 20 Items measured: z0u00 4ws.TXT Jul 13 2 2so Persons 20 Items 5 CATS 37 2012 3.68.2

summary of 2 69 MEASURED (non-extreme> Persons

raw SCORE COUNT MEASURE MODEL ERROR I NF IT MNSQ Z STD OUTFIT MNSQ ZSTÜ

MEAN s. d . MAX. MIN. S3 . o 9 . 4 99 . O 45 . O 19. 9 . 4 2D. O 15 . O 4 . 19 2-13 5 . 90 —2 - 82 . 5 1 . 11 1. D5 . 34 - 99 —.1 - 5 2 1.5 3-71 4.9 -17 —3-3 . 97 . 57 3, 67 . 14 -- 2 1-5 4 . 9 —3- 4

real rmse .56 ad 3 . sd 2 .06 separation 3.68 |Person RELIABILITY . 93

model S. E . RMSE -52 adj.sd OF Person MEAN = .13 2 .07 SE PARATION

MAXIMUM EXTREME SCORE: valid responses: ^99 . 756 Persons

person raw score-to—measure correlation = .97 (approxlgiati due "Co missing data) cronbach alpha (kr-20) Person raw score reliability =Q9y(approximate due to missing data)

SUMMARY OF 20 MEASURED (NON EXTREME) Items

raw SCORE COUNT MEASURE MODEL error INF3T mnsq 2std OUTFIT mnsq zstd

MEAN s. d . MAX. MIN. 1171.8 52 . 2 1270.O 105 3.O 279. 1 1. 3 2 SO. O 275 . O - oo - 91 1. 93 -1. 90 . 13 . Ol . 15 - 13 1-OO .O - 11 1.3 1.19 2.O -75 —3-D . 97 . 13 1. 16 . 68 — - 2 1-1 1. 6 —2 - 6

real RMSE - 14 ad 3 . sd . 90 se paration 6.55 litem RELIABILITY . 98 |

model s. e . RMSE - 13 OF Item MEAN ad 3 . sd = .21 .90 SE PARATION 6.69 Item HELTAS5LITV . 50

UME AN=.OOO USCAL E=1.OOO Item raw score-to-measure correlation = — .99 (approximate due to 5363 DATA POINTS- LOG—LIKELIHOOD CHI-SQUARE: 6G5Q.43 with SD72 d. missing data) -f. p=.oooo

Figure 2. Summary Statistics

Before proceeding to the person-item map analysis, it is vital to determine whether the questionnaire used as the instrument of measurement is measuring what it is supposed to be measuring. Thus, the construct validity of the questionnaires can be determined based on an Item Measure analysis, as shown in Figure 3. The Item Measure lists the detailed measurement logit for each item that can be used to identify any misfit data by

checking three control parameters: the Point Measure Correlation (PMC), Outfit Mean Square (MNSQ) and z-standard value, ZSTD. The item is considered acceptable and infit if the Point Measure, x, for that item is within the range 0.4<x<0.85. The same is true for the Outfit Mean Square (MNSQ), y- and z-standard value (ZSTD), z, in which the item measure must be within the range of 0.5<y<1.5 and -2<z<2, respectively. The item is misfit when all three control parameters are not in the range, as mentioned earlier. Because only the z-standard value (ZSTD) for item Q14 is out of range, this item cannot be considered misfit. The item measures for the other items are also within the range for all three control parameters. Therefore, all the items are acceptable and need no review (Osman et al. 2011).

TABLE 13.1 INTERNSHIP (EMPLOYER) ZOU0321II5.TXT lul 12 2:12 2012

INPUT: 2 bo Persons 20 It ens MEASURED: 2 80 Persons 20 Items 5 CATS 3.6b.2

person: real Sep.: 3.EB rel.: .93 ... item: real Sep.: 6.55 rel.: .93 It en statistics: measure order

ENTRY TOTAL MCOEL INFIT OUTFIT PT-MEASURE EXACT MATCH

NUMBER SCORE COUNT MEASURE S.E. MNSQ ZSTD MNSQ ZSTD CORR. EXP. OBS» EXPSi DISPLACE item

4 1053 278 1. 93 . 13 1.09 1. 1 1. 14 1. 5 .72 .76 ES.5 69.2 .00 Q4

1 1092 2 80 1. 43 . 13 1.04 . 4 l.OG 7 . 66 .74 67.7 69. 8 .00 Qi

20 1097 275 1. 06 . 13 1.00 .0 1.07 7 .73 .74 72.3 70.0 .00 Q20

3 1123 2 80 34 . 13 1. 15 1.7 1. IE 1. E .72 .73 E5. 1 70.6 .00 qs

9 1133 2 80 76 . 13 1.01 . 1 1.00 0 .72 .73 72.5 70. 8 .00 Q9

10 1149 2 80 49 . 13 1. 12 1. 4 1.08 9 .72 .73 69. 1 71.6 .00 Q10

11 1154 2 80 40 . 13 1.01 . 1 1.02 2 .70 .72 70.6 71. 8 .00 qn

7 1152 279 36 . 13 . 92 -. 9 . SS -1. 1 .74 .72 73.5 72.0 .00 Q7

2 1175 2 80 04 . 13 . 93 -. 8 . 91 B .73 .72 74.0 72.6 .00 Q2

19 11E3 277 04 . 13 1.19 2.0 1. 12 1. 0 . 66 .71 E9.5 72. 9 .00 Q19

5 1189 2 80 22 . 13 1. IE 1. 8 1.09 B . 69 .71 ES. 8 73.1 .00 QS

6 1187 279 24 . 14 1.04 .5 1.02 2 . 69 .71 72.0 73.2 .00 Q6

15 1195 2 80 33 . 14 . 91 -1.0 . 3E -1. 2 .73 .71 75. 8 73.3 .00 Q15

3 1201 2 80 44 . 14 . 83 -1. 4 . SI -1. 7 .75 .70 75.5 73.6 .00 Q3

17 1202 279 52 . 14 1.00 .0 . 95 4 .74 .70 75.7 73. 9 .00 Q17

IS 1201 273 53 . 14 . 83 -1. 9 .BE -1. .73 .70 7 8.7 74. 1 -.01 Q18

14 1221 2 80 B1 . 14 .75 -3.0 . 68 | -2 6 .73 . E9 79.6 74.5 -.01 Q14

16 1231 279 -1. 03 . 14 1.05 . G . 96 .71 . S3 75.0 75.0 -.01 Q1E

13 1239 2 80 -1. 17 . 14 . 90 -1.1 . 31 -1. Z .70 . E3 75.5 75. 1 -.01 Q13

12 1270 279 -1. 90 . 15 . 97 -.3 . BE 6 . 67 . 64 SO.6 76.6 -.01 Q12

MEAN 1171. 9 279. 1 00 . 13 1.00 .0 . 97 _ 2 73.0 72.7

S.D. 52.2 1.3 91 .01 . 11 1.3 . 13 1. 1 4. 1 1. 9

Figure 3 Item Measure

Figure 4 shows the total measure given by the employers for each student. Although the Rasch analysis was carried out for all 280 students, only those being discussed are presented and shown in the figure. The students are sorted randomly from the excellent (highest score) to the poor students (lowest score). There are 19 students that received a high score from the employers, and these students are located at the top of the table. The employers were very satisfied with the performance of these students and gave them high marks. In contrast, the lowest mark was given to student S243.

There are also 22 students that have been identified as misfit according to the 3 control parameters mentioned in the Item Measure. For example, student S035 is one of the misfit students because all 3 control parameters are out of range and marked with a blue box in the table. By referring to the Scalogram pattern shown in Table 5, the pattern of respond for this student does not match the ideal model (Osman et al. 2012). The ideal pattern of respond involves the highest score for the easy item and the lowest score for the difficult item on the right. However, this student did not receive the highest mark for an easy item but received a good score for the difficult item, which means that he/she did not perform well on an easy task but did perform well on a difficult task.

table 17 .1 internship (employer) ZOU032WS.TXT Jul j." ^VvVv^'-iv j." ii- 2012

input: zsom^OSwWl iteas measured: 280 Persons zo Items 5 cats 3. 68. z

Person: real sep. : ___ It er: REAL SEP : 6. 55 rel.: .98

Person statistics: measure order

| ENTRY total modelj IMLJ, outfit |pt-heasike i exact hatch|

count heaslke s.e. |nnsq zstd|hnsq zstd|ccfr. exp. Person|

■ I iz 100 20 10.17 1. 85 1 maximum measurel^.00 . 00 loo. 0 100.0| S01Z

1 £7 100 20 10.17 1.85| MAXIMUM MEASURE!^00 . 00 100 . 0 100.01 S067

72 100 20 10.17 1. 85 | MAXIMUM MEASURE]^ 00 .00 lOO. 0 100.01 S072

98 100 20 10.17 1.85 | MAXIMUM MEASURE]^ 00 . 00 lOO. 0 100. 0 S098

111 100 20 10.17 1.85 | MAXIMUM MEASUPEl . OO .00 100. 0 100.01 Sill

146 100 20 10.17 1.851 MAXIMUM MEASURE!^00 . 00 lOO. 0 100.0| S146

147 100 20 10.17 1.85 | MAXIMUM MEASURE]^ 00 .00 ICO. 0 100.01 S147

Z18 100 20 10.17 1.85 | MAXIMUM measure]^00 . 00 lOO. 0 100. 0 S218

zz9 100 20 10.17 1.85 | MAXIMUM MEASUREL^OO . 00 lOO. 0 100.01 SZZ9

2 ce 100 20 10.17 1. 85 | MAXIMUM MEASURE^00 .00 lOO. 0 100.01 S266

Z76 100 20 10.17 1.85 | MAXIMUM MEASURE! . 00 . 00 lOO. 0 100.01 S276

1 53 99 20 8.90 1. 05 .91 ■ Z | .37 -.11^36 .19 95 . 0 S053 |

. . . CS109 - SIS9)

1 3 89 20 5 . 42 jvl 7? -1 il ch -1 il fill _#r>l If - n ci 1 i <;nna_I

35 89 20 5 .42 . 4711. 64 2.4 1.82 Z.7j -.41 . 40 S035

I 46 89 ZO 5 . 42 .47|Z.Z4 4.^12.54 4.JLtU .40 50. u 65. TT so4G |

. . . (S076 - S163)

| 227 56 20 -1. 39 . 3911. 40 1.111.5 3 1.3lwTv3Z .46 45 . n 67. 0 I

243 45 20 -Z.8Z . 34 1.48 1.6|1.50 1.61^38 . 52 45 . 0 51. 0 1 S243

1 mean 83.7 19.9 4.43 .sq .99 -.1| .97 —.21

| s.d. 9.8 .4 Z.39 .281 .52 1. 5 | . 57 1. 5 | 15 . z 6.8 |

Figure 4 Person Measure

TABLE 22.1 INTERNSHIP (EMPLOYERS Z0U032WS.TXT Jul JUS_2012

INPUT: 2 30 Persons_ItHE MEASURED: 23O Persons 2t> I "terms 5 CATS 3.63.2

GLTTHAN SCALOGRAM OF RESPONSES:

Person | lté»

1111111 i,_J, 2

|2 364873S65 9271D98014 1

. . . (S109 - S15 9)

-a. j55555454454i.44454U.4 S009

1111111 Jt_J, 2

I2 364873S65 9271D98014

Figure 5 Scalogram

4. Conclusion

In conclusion, the overall performance of the student shows that most of the employers were satisfied with the skills that the students have because they gave good marks to most of them. Most of the employers are satisfied with the students' ability to interact but most of them were dissatisfied with students' leadership ability, as shown in the PIDM. Therefore, a new approach or task must be introduced to improve this ability. From the Rasch analysis, student achievement can be plotted against the questionnaire, and the suitability of the questionnaire can be identified simultaneously with the students' ability. These results can also be used as a guideline for lecturers when planning suitable methods to prepare students before they undergo their industrial training.

Acknowledgements

The authors wish to express gratitude toward Universiti Kebangsaan Malaysia (UKM) and the Centre for Engineering Education Research for funding this research under University Research Grant OUP-2012-126 and PTS-2011-002.

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