Research ArticleAssessment of Student Performance for Course ExaminationUsing Rasch Measurement Model: A Case Study of InformationTechnology Fundamentals Course

Amir Mohamed Talib ,1 Fahad Omar Alomary,2 and Hanan Fouad Alwadi1

1Information Technology Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud IslamicUniversity, Riyadh, Saudi Arabia

2"e Executive Director of Center of Cyber Crimes Studies, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh,Saudi Arabia

Correspondence should be addressed to Amir Mohamed Talib; ganawa53@yahoo.com

Received 27 March 2017; Accepted 11 December 2017; Published 19 February 2018

Academic Editor: Gwo-Jen Hwang

Copyright © 2018 Amir Mohamed Talib et al. ,is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

,is paper describes a measurement model that is used to measure the student performance in the final examination of InformationTechnology (IT) Fundamentals (IT280) course in the Information Technology (IT) Department, College of Computer & InformationSciences (CCIS), Al-ImamMohammad Ibn Saud Islamic University (IMSIU).,e assessment model is developed based on studentsmark entries of final exam results for the second year IT students, which are compiled and tabulated for evaluation using RaschMeasurement Model, and it can be used to measure the students performance towards the final examination of the course. A studyon 150 second year students (male� 52; female� 98) was conducted to measure students knowledge and understanding for IT280course according to the three level of Bloom’s Taxonomy. ,e results concluded that students can be categorized as poor (10%),moderate (42%), good (18%), and successful (24%) to achieve Level 3 of Bloom’s Taxonomy. ,is study shows that the studentsperformance for the set of IT280 final exam questions was comparatively good. ,e result generated from this study can be used toguide us to determine the appropriate improvement of teaching method and the quality of question prepared.

1. Introduction

Tests and examinations are part of evaluation and assess-ment that are carried out to fulfill the academic requirement[1]. ,ey are systematic methods in assessing individualchange of behaviors, which are related to effective teachingand learning activities [2]. Test question is an assessmenttool to gather information on cognitive, psychomotor, andaffective achievement of a student [3]. Tests and assessmentsare implemented to fulfill their important purpose to reflectstudents’ achievement and differentiate proficient studentsfrom the amateur. ,is is useful to classify students accordingto their skill and capability.

Examination item is an important instrument to re-flect students’ achievement and differentiate proficient stu-dents from the amateur. ,e scarcity of guideline on testing

the reliability and validity of examination item needs to beaddressed to ensure a systematic method in assessing students’ability. ,is research is carried out to achieve two objectives:first, to propose a systematic procedure to measure validityand reliability of the instrument in assessing students’ability, and second, to discriminate between proficient andamateur according to their ability and determine item difficultyusing the Rasch assessment model.

Measurement has been grossly misunderstood andunnoticed in lots of situations, specifically within the field ofsocial science. Many researchers in social technology arefrustrated whilst current gadgets are not nicely tailored to themission, because they then cannot anticipate touchy, accurate,or valid findings [4]. However, modern measurement methodas practiced using item response theory with a focus on RaschMeasurement Model provides the social sciences with the

HindawiEducation Research InternationalVolume 2018, Article ID 8719012, 8 pageshttps://doi.org/10.1155/2018/8719012

kind of measurement that characterizes measurement in thenatural sciences, that is, the field of metrology.

,e basics of measurement should comprise the in-strument for use for purpose, which has specific unit of anagreed trendy amount. A device must have the best constructof linear scale, which can be set 0 and duly calibrated. A validinstrument can then be replicated for use independent of thesubject hence measurement taken thereof is therefore a reli-able data for meaningful analysis and examination to generateuseful information [5]. ,is information is of maximumimportance to be the top ingredient in a particular selectionmaking.

Many of higher education institutions in the Kingdom ofSaudi Arabia (KSA) have implemented the outcome-basededucation (OBE). It is one of the important steps designed toelevate the level of quality and excellence in institutions ofhigher education [6].

Most higher education institutions in KSA, nowadays,are towards complying with the American AccreditationBoard of Engineering and Technology, 2000 (ABET) re-quirements, which promote OBE learning process. ,e OBEapproach must constantly be monitored, assessed, andmeasured so as for the university to effectively compete andachieve exquisite overall performance. OBE appeals for theassessment of the path getting to know course learningoutcome (CLO) that has been laid out in each coursespecification. CLOs were evaluated primarily based on thestudents’ overall performance, which gives an indication intheir learning achievements [7].

Measurement of students’ performance has been de-pendent on the students’ overall performance in sporting outobligations inclusive of quizzes, assignments, mid exami-nations, projects, and final exams. Evaluation and mea-surement at the general performance output give an instanceat the fulfillment of CLO for every course andmay be used asguidance for academics in determining the ideal develop-ment of the teaching method in addition to the quality ofquestions prepared. Hence, a good measurement method isvital to be carried out in order to measure and predict thestudent’s performance in future, and this would also help usin identifying those students that likely to fail.

,e purpose of this study is to measure the students’achievement in IT Fundamentals (IT280) course in theInformation Technology (IT) Department, College ofComputer and Information Sciences (CCIS) at Al ImamMohammad Ibn Saud Islamic University (IMSIU). ,ecourse is one of the core subjects that must be completed bythe IT students before they can graduate. ,is course issupposed to be on the introductory level to offer basic skillsfor subsequent guides for the next courses. It offers a top-degree view of the concern of IT, describes how it pertains toother computing disciplines, and begins to instill an ITmindset. ,e goal is to assist students understand the nu-merous contexts wherein its miles used and the worryingsituations are inherent in the diffusion of innovative tech-nology. ,e aim is to help students apprehend the variouscontexts wherein its miles used and the demanding situa-tions are inherent inside the diffusion of revolutionarygeneration. ,e main objectives of this course are to explain

the terms and concepts of IT (personal PC, software, hard-ware, security, networking, Internet/web, and applications);acquire basic skills and be able to use themain PC applications;understand the basic concepts and terminology of IT and beable to define them; select and judge the usage of IT productsand services; explore and evaluate IT career opportunities; useInternet/web services as a resource for learning and discovery;create useful end products for themselves in ITareas of interestto explore major, career, skills, interests, and talents; andincrease the ability to learn and explore new informationtechnologies with confidence to identify issues related toinformation security. ,e measurement is based on thefinal examination questions, and the Rasch MeasurementModel will analyze the raw marks.

,eRaschMeasurementModel is a probabilisticmodel thatconsiders two aspects: (i) the difficulty of the items/questionsand (ii) the ability of respondents/students to verify the items[8]. ,e basic principle underlying the Rasch MeasurementModel is that the probability of a student successfully verifyinga particular question is governed by the difference between thequestion’s difficulty and student’s ability [8–11]. ,e logicunderlying this principle is that all students have a higherprobability of answering easier questions and a lower proba-bility of answering more difficult questions accurately [9].

2. Methodology

,e data that were obtained from the final examination ofIT280 course, which was conducted for the second-yearInformation Technology students at CCIS, IMSIU in thesecond semester of 2014/2015-2015/-2016. ,is study wasconducted for all 150 students (male� 52; female� 98) whohave registered for the course.,e final examination consistsof 35 questions which were divided into eight parts: Part A,Part B, Part C, Part D, Part E, Part F, Part G, and Part H.Students are required to answers all the questions. IT280course was chosen because it is a compulsory and corecourse for all the following courses within the ITcurriculumin the department. IT curriculum has a number of tracks inwhich IT280 course assists the students to identify whichtracks have to be chosen. Besides, IT280 course introducesthe techniques of the training program which is compulsory,and students may involve at the end of their third year.,erefore, students need to pass this course in prior to thetraining.

,e final exam consists of seven questions that arerequired to be answered, all of which were divided intoeight topics which are covered by IT280 course, as tab-ulated in Table 1.

,e course learning outcomes for the learning topics forIT280 expected for the students to achieve are tabulated inTable 2.

,e questions are entered as entry number as tabulatedin Table 3. ,e item is labeled as Question No. and Tax-onomy Bloom Level of Learning, which the students expectedto develop three levels of Bloom’s Taxonomy, namely,remembering/understanding (1), applying/analyzing (2), andevaluating/creating (3). ,us, for entry item number 1, theitem is coded as A01_1 (as shown in Table 2).

2 Education Research International

Rating from final exam results has been amassed andcompiled. As those row ratings have specific total marks forevery query, a standardization method is used. ,e formulafor the standardization is given as follows [12]:

zij �Xij−minxjmaxxj

, (1)

where i� the ith student (i� 1, 2, . . ., 59), j� the jth question(j� 1, 2, . . ., 22), zij� standardized marks for the ith studentand jth question, xij�marks for the ith student and jthquestion, min xj�minimummarks for the jth question, andmax xj�maximum marks for the jth question.

Responses from the students’ exam results were analyzedusing rating scale in which the students were rated accordingto their achievement. From (1),

zij∗10 � A. (2)

,en, A is classified corresponding to the rating scale inTable 4.

Table 2: Course learning outcomes for IT280.

Number Course learning outcomes (CLOs)

CLO-1Able to understand the concepts and terms of IT

(personal PC, software, hardware, security,networking, Internet/web, and applications)

CLO-2 Able to acquire the basic skills and be able to use themain PC applications

CLO-3 Able to understand basic concepts and terminologyof IT and be able to define them

CLO-4 Able to select and judge the usage of IT services andproducts

CLO-5 Able to explore and evaluate IT career opportunities

CLO-6 Able to use Internet/web services as a resource forlearning and discovery

CLO-7Able to create beneficial cease products for

themselves in ITareas of interest to explore principle,profession, abilities, pursuits, and capabilities

CLO-8Able to grow the capacity to learn and discover new

IT with self-assurance and be able to pick outproblems related to fact security

Table 1: Topics covered by IT280 course.

Topic Topics covered by IT280 courseTopic A IT terminologies

Topic B IT systems, IT applications, and science andtechnology

Topic C IT products and servicesTopic D Internet/web servicesTopic E IT professions and careersTopic F Hypertext Markup Language (HTML)Topic G IT ethics and responsibilitiesTopic H Concepts of information security

Table 3: Entry number coded for each exam question.

Questions Subquestions Topic Entrynumber CLOs

1

1 Topic A A01_1 CLO-12 Topic A A02_1 CLO-3 and CLO-73 Topic A A03_1 CLO-14 Topic D D04_1 CLO-65 Topic A A05_1 CLO-36 Topic G G06_1 CLO-87 Topic A A07_1 CLO-18 Topic D D08_1 CLO-69 Topic A A09_1 CLO-310 Topic A A10_1 CLO-311 Topic C C11_1 CLO-312 Topic C C12_1 CLO-713 Topic C C13_1 CLO-414 Topic A A14_1 CLO-315 Topic H H15_1 CLO-816 Topic A A16_1 CLO-317 Topic A A17_1 CLO-118 Topic H H18_1 CLO-319 Topic F F19_1 CLO-320 Topic C C20_1 CLO-3

2

11 Topic E E11_1 CLO-512 Topic D D12_1 CLO-613 Topic C C13_1 CLO-714 Topic H H14_1 CLO-815 Topic B B15_1 CLO-816 Topic C C16_1 CLO-417 Topic D D17_1 CLO-618 Topic D D18_1 CLO-419 Topic E E19_1 CLO-520 Topic C C20_1 CLO-721 Topic H H21_1 CLO-822 Topic A A22_1 CLO-323 Topic A A23_1 CLO-3

3 24 Topic A A24_1 CLO-125 Topic A A25_1 CLO-2

4

26 Topic H H26_1 CLO-827 Topic H H27_1 CLO-828 Topic H H28_1 CLO-829 Topic H H29_1 CLO-8

5 30 Topic A A30_1 CLO-3

6 31 Topic B B31_1 CLO-8

7

32 Topic C C32_1 CLO-433 Topic C C33_1 CLO-734 Topic C C34_1 CLO-835 Topic C C35_1 CLO-5

Table 4: Rating scale [12].

Marks (A) 0–1.49 1.50–3.49 3.50–6.49 6.50–8.49 8.50–10.00Ratingscale 1 2 3 4 5

,e grade rating is tabulated and analyzed using Bond and Fox steps, theRasch Measurement Model software.

Education Research International 3

3. Data Analysis and Discussion

Figure 1 demonstrates the summary statistics for 150 stu-dents who answered 35 IT280 �nal exam questions. �e �rstindicator from these �ndings is the person’s mean of +0.68(SE 0.54), showing that the students �nd this set of �nal

exam questions comparatively easy. �is means that theytend to answer all the questions correctly. �e mean square�ts (IMNSQ and OMNSQ) and the z statistics (INFIT ZSTDand OUTFIT ZSTD) are closer to their expected values +1and 0, respectively, for persons (students) and items(questions). �is con�rms satisfactory �t to the model.

Persons

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

+

+

+

+

MeanScore Count Measure Error IMNSQ ZSTD OMNSQ ZSTD

26.2 35.0 1.57 0.54 0.99 0.1 0.95 0.16.2 0.0 1.30 0.20 0.13 0.6 0.46 0.7

0.57 Adj.SD 1.17 Separation 2.04 Person Reliability 0.81SDReal RMSE

Mean 109.9 147.0 0.00 0.24 1.00 0.1 0.95 −0.119.5 0.0 0.97 0.06 0.11 1.0 0.31 1.2

0.25 Adj.SD 0.93 Separation 3.79 Item Reliability 0.93SDReal RMSE

150 Input INFIT OUTFIT150 Measured

Items 35 Input INFIT OUTFIT35 Measured

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Figure 1: Summary satisfaction.

Persons MAP OF Items<more ability> |

+||||||||+||||+|||||||||||+||+|||||||+|

A22_1

933 902 999 901964 987 908 904

91209143 909 9150

994 902 906 992S

C32_1 E19_1

9143 H28_1 H14_1 B31_1907 929 936

926 970 935 925923 911

T990 901 9138 903974 912 9113 M H15_1 A05_1 C13_1 H18_1 C12_1

939 9100 997 905 G06_1 A09_1 A01_1919 9107 9134 C11_1

929 943 965 911 A02_1 D12_1 H21_1 A03_11 977 9111 9133 S A16_1 D18_1

9843 938 922 910 E11_1 F19_1 A10_19107 9141 948 9119

952 9114 D04_1

D08_1 A14_1A07_1 C20_1 D17_1 B15_1 A23_1A17_1

962 955 917 937 990 C16_1−2 948

A24_1 C35_1 H27_1 A30_1922 9148 923 984 971 C34_1 H29_1 C33_1 H26_1

A25_1

−3 C13_1

985 906 986 9110 9140T

MEAN item

2

3

919 922 956

<more difficult>

<less ability> <easiest>

M

Figure 2: Wright map.

4 Education Research International

Besides, the person reliability (Rasch equivalence to Cron-bach’s alpha) is 0.81, while the item reliability is much higherat 0.93. �e values of person and item reliability (>1.57) doreveal that the instrument for measuring the students’learning ability is reliable, reproducible, and valid formeasurement. �e person separation can be used to calculatethe separation strata, which indicate a variety of wonderfulability degrees separated through 3 widespread errors ofmeasurement. �e formula for the strata is (4× person sep-aration index+1)/3 [13, 14]. �e strata value of 0.13 suggeststhat the 35 IT280 �nal exam questions can distinguish twodistinct levels of the students’ academic ability.

Figure 2 shows the Wright map representation for theanalysis. It signi�es that the distribution of the students is onthe left and the distribution of the questions is on the rightaccording to student and question label, respectively. �edistributions of students and questions supported the result

from the summary statistics as illustrated in Figure 1. �eWright map provides an exact overview of the student’sachievement on the �nal examination.

�e separation of the question against the student’s lo-cation on the Wright map shows the level of a student’sability. �e further the separation, the more a student is ableto respond correctly to the question. �e level of questiondi�culty is also re�ected by the spread of the question on thescale.�eMEANitem of the questions in the higher location ofWright map (as illustrated in Fig. 2) is more di�cult com-pared to the questions on the lower location.�us, MEANitemserves as threshold and it is set to zero on the logit scale.

�e most di�cult question is A22_1 located at the top ofthe item distribution, while the easiest question is C13_1located at the bottom of the item distribution. �e Wrightmap con�rms that the person mean is higher than thethreshold value, MEANitem� 0. �ese values show that the

Input: 150 Persons

Item statistics: Measure order+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––+

+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––+

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Entrynumber

ModelSE

PTMEACORR.

MATCHEXP%Count Measure

INFITMNSQ ZSTD

OUTFITMNSQ ZSTD Item

22182633462

313423282730163211I9217

29155

14359

172481

2012163

2513

547387899191959798

104105107108111112112112116117120122122124125126128129129130131132133134141145

150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150150

2.401.681.171.101.031.030.880.800.760.520.480.400.360.230.180.180.180.09−0.05−0.20−0.31−0.31−0.42−0.48−0.53−0.66−0.72−0.72−0.79−0.86−0.94−1.01−1.09−1.81−2.49

74.871.971.271.471.771.772.572.973.174.975.275.876.277.477.877.877.879.880.381.882.982.983.984.585.086.086.686.687.187.788.388.989.594.096.6

0.200.190.190.190.190.190.190.200.200.200.200.200.210.210.210.210.210.220.220.230.230.230.240.240.250.250.260.260.260.270.270.280.290.360.47

1.271.120.961.191.160.970.910.900.981.071.010.871.060.891.071.021.071.000.900.921.130.931.000.941.100.971.011.150.980.910.850.980.910.691.06

2.61.4−0.52.32.0−0.4−1.1−1.3−0.20.80.2−1.40.7−1.10.70.30.70.0−0.9−0.71.0−0.50.1−0.40.7−0.10.11.00.0−0.5−0.8−0.1−0.4−1.10.3

1.751.230.851.151.320.841.000.750.901.261.050.750.921.030.970.961.550.880.740.681.030.730.790.710.930.650.751.320.690.810.620.761.690.240.83

3.71.7−1.11.02.0−1.10.1−1.6−0.51.30.3−1.3−0.30.20.0−0.12.2−0.4−1.0−1.20.2−0.9−0.6−0.9−0.1−1.0−0.60.9−0.8−0.4−0.9−0.51.4−1.50.0

69.470.172.863.366.773.575.572.873.574.174.878.270.782.376.280.378.980.379.684.479.683.781.686.481.085.785.085.086.487.189.890.590.594.696.6

0.320.430.530.380.370.520.520.550.490.400.440.540.420.490.400.420.360.430.490.470.330.450.410.430.330.410.370.250.390.400.430.350.350.460.20

A22_lD18_1H26_1C33_lD04_lG06_1A02_1B31_1C34_1A23_lH28_lH27_lA30_lC16_1C32_1C11_1F19_l

H21_1A07_1H29_1H15_1A05_1H14_lC35_1A09_1A17_lA24_lD08.1A01_1C20_lD12_1A16_1A03_1A25_1C13_l

Totalscore

EXACTCBS%

MeanSD

109.919.5

147.00.0

0.000.97

0.240.05

1.000.11

0.11.0

−0.11.2

0.950.31

80.07.8

80.06.9

35 Items 35 ItemsMeasured: 150 Persons

Figure 3: Item statistics.

Education Research International 5

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Figure 4: Guttman scalogram.

6 Education Research International

student’s performance is above the expected performance.More than half of the students measured (N� 138, 98.1%) arefound to be above the MEANitem, while only 12 students(1.9%) are below theMEANitem.�e students with less abilityhave some di�culty in answering half of the exam questions,which are located above the MEANitem.

Student S48 can be categorized as a student with thepoorest ability since that student is located at the bottom ofthe student distribution. In contrast, there are 4 students(S85, S06, S110, and S140) with the highest ability who wereable to answer all the questions given and they are located atthe top of the student distribution. �eir abilities haveexceeded the degree of di�culties of the questions. �isreveals that these students successfully achieved all theBloom Taxonomy Level of Learning.

Figure 3 determines the Rasch item estimates for theIT280 �nal exam questions; hence, the details of the maplocations can be veri�ed more easily. �is �ndings con�rmthat the easiest question/item is C13_1 located at the bottomof the item distribution at −2.49 logits (SE 0.047), while themost di�cult question/item is A22_1 located at +2.40 logits(SE 0.20). �e analysis reveals that the easiest question(C13_1) has minimum estimated measure. �is indicatesthat all students can answer the question correctly. �e �tstatistics of the item output look good, although we need toreconsider two under �t items: D04_1 and D08_1. Coun-tercheck against the Guttman scalogram as illustrated inFigure 4 indicates that the two items D08_1 (item 8) andD04_1 (item 4) have been underrated by 20 students: student85 (S85), student 06 (S06), student 86 (S86), student 110(S110), student 140 (S140), student 33 (S33), student 02(S02), student 99 (S99), student 01 (S01), student 46 (S46),student 87 (S87), student 08 (S08), student 04 (S04), student120 (S120), student 143 (S143), student 09 (S09), student 150(S150), student 94 (S94), student 02 (S02), and student 92(S92), respectively (Figure 3). One possible reason is thatthey could have been careless in attempting their answerswhich led to such a grossly underrated work. After verifyingthat the point measure correlation as illustrated in PTMEACORR column in Figure 3 for both items is a positive value,the two mis�ts are acceptable.

�is study is also interested to measure the students’performance on question C12_1 that tests the students’ability to create useful products for themselves in ITareas ofinterest to explore major, career, skills, interests, and talents(refer to Tables 2 and 3 and Figure 2). Further analysis onitem category for question C12.1 as illustrated in Figure 5found that 9 students (6%) were not able to answer it. �is

reveals that they failed to achieve Level 3 of Bloom’s Tax-onomy. �e rest of the students can be categorized as poor(10%), moderate (42%), good (18%), and successful (24%)to achieve Level 3 of Bloom’s Taxonomy.

4. Conclusion and Future Work

�e Rasch Measurement Model presents a valid platformof measurement equivalent to fundamental courses, whichmatches the mission and vision measurement standard andcriteria. It is also quanti�able considering the fact that it islinear.�e RaschMeasurementModel has made it very usefulwith its predictive function to triumph over missing data.

�is paper described the assessment and measure-ment of students’ performance for IT Fundamentals(IT280) course in the second semester of 2014/2015-2015/-2016 for IT students in the Information Tech-nology (IT) Department, College of Computer and In-formation Sciences (CCIS), Al Imam Mohammad IbnSaud Islamic University (IMSIU) by using Rasch Mea-surement Model.

In conclusions, this study reveals that the students’performance for the set of IT280 �nal exam questions iscomparatively good. �is con�rms that most of the studentswere able to achieve Level 3 of Bloom’s Taxonomy.�e resultgenerated from this study can be used to guide us to de-termine the appropriate improvement of the teachingmethod and the quality of question prepared.

In the future, we will continue our e©orts to evaluate thestudents’ performance with other set of students andquestions in order to ensure the consistency of the students’performance result. Besides, we will further our study toassess the students’ achievement for other IT courses toprepare the evidence for the quality of measurement ofstudents’ performance. �is will help the IT department incomplying with the ABET accreditation requirement.

Conflicts of Interest

�e authors declare that they have no con�icts of interest.

References

[1] W. M. Kapambwe, “�e implementation of school basedcontinuous assessment (CA) in Zambia,” Journal of Educa-tional Research and Reviews, vol. 5, no. 3, pp. 99–107, 2010.

[2] P. Sahlberg, “Education policies for raising student learning:the Finnish approach,” Journal of Education Policy, vol. 22,no. 2, pp. 147–171, 2007.

Entrynumber

Datacode

Scorevalue

Datacount %

Averagemeasure

SEmean

OUTFITMNSQ

PTMEAcorr. Item

35 1 0.33 0.15 0.6 −0.202 0.31 0.05 0.1 −0.303 0.56 0.05 0.6 −0.284 0.82 0.08 0.0 0.115

12345

915632736

610421824 1.21 0.14 0.5 0.54

C12_1

+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––+

+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––+

+–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––+

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Figure 5: Item category for question C12_1.

Education Research International 7

[3] B. L. Boyd, K. E. Dooley, and S. Felton, “Measuring learning inthe affective domain using reflective writing about a virtualinternational agriculture experience,” Journal of AgriculturalEducation, vol. 47, no. 3, pp. 24–32, 2006.

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[7] S. A. Osman, W. H. W. Badaruzzaman, R. Hamid et al.,“Assessment on students performance using Rasch model,”Reinforced Concrete Design Course Examination. Recent Re-searches in Education, pp. 193–198, 2011.

[8] G. Rasch, Probabilistic Models for Some Intelligence and At-tainment Test, Danish Institute for Educational Research,Copenhagen, Denmark, 1960.

[9] T. V. Bond and C. M. Fox, Applying the Rasch Model: Fun-damental Measurement in the Human Sciences, LawrenceErlbaum Associates, Mahwah, NJ, USA, 2nd edition, 2007.

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[12] H. Othman, I. Asshaari, H. Bahaludin, Z. M. Nopiah, andN. A. Ismail, “Application of Rasch measurement model inreliability and quality evaluation of examination paper forengineering mathematics courses,” Procedia–Social and Be-havioral Sciences, vol. 60, pp. 163–171, 2012.

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