Item analysis of multiple choice questions: A quality assurance test for an assessment tool

Kumar, Dharmendra; Jaipurkar, Raksha; Shekhar, Atul; Sikri, Gaurav; Srinivas, Venkat

doi:10.1016/j.mjafi.2020.11.007

Cited by 47 publications

(54 citation statements)

References 20 publications

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“…The difficulty index that has been calculated is interpreted based on three criteria (Johari et al, 2011;Kumar et al, 2021;Wibawa, 2019). An item with a difficulty index ranging from 0.00 to 0.29 is classified as high difficulty.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…The difficulty index in a classical test theory is the proportion of who answered correctly (Mardapi, 2015). The formula that is employed to calculate the difficulty index is presented in Formula (1) (Johari et al, 2011;Kumar et al, 2021;Marie & Edannur, 2015;Nitko, 1996). In addition, the criteria of the item difficulty index are described in Table 1.…”

Section: Methodsmentioning

confidence: 99%

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Analysis of the difficulty index of item bank according to cognitive aspects during the Covid-19 pandemic

Fauzie

Pada²,

Supriatno³

2021

Jurnal Penelitian dan Evaluasi Pendidikan

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The Covid-19 pandemic is a major challenge for the education system. The face-to-face learning process shifted to online learning, including the school exams. In Aceh province, the school exams have changed from paper-based and computer-based. This research aims to analyze the difficulty index of an item bank based on cognitive aspects of Bloom’s Taxonomy. The study samples included 850 students. The data were the item bank of a final semester exam consisting of 200 multiple-choice items, answer keys, and students’ answer sheets. The empirical analysis of the item bank using classical test theory (CTT) found that 141 out of 200 items are valid based on content validity and computing data set using the Aiken’s V formula. Item tests have reliability of 0.983. The reliability is calculated using the Kuder-Richardson 21 formula. If the reliability coefficient is r11 ≥ 0.70, then the item is declared reliable. In addition, 62 out of 141 (43.97%) items from the item bank are classified with a moderate difficulty index, and 79 items (56.03%) are categorized with a high difficulty index. The cognitive aspects found in the items are remembering, understanding, applying, and analyzing. Students mostly found items with the cognitive aspects of remembering and understanding are difficult to solve.

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Section: Findings and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis of the difficulty index of item bank according to cognitive aspects during the Covid-19 pandemic

Fauzie

Pada²,

Supriatno³

2021

Jurnal Penelitian dan Evaluasi Pendidikan

View full text Add to dashboard Cite

show abstract

“…The reliability and content validity of the test were also considered. The reliability, calculated by Kuder-Richardson Formula 20 (KR-20), was at acceptable level with the coefficient value of 0.76 (Kumar, Jaipurkar, Shekhar, Sikri, & Srinivas, 2021). Meanwhile, the index of Item-Objective Congruence (IOC) for content validity was employed to verify the teachers in this research (Ismail & Zubairi, 2022).…”

Section: Measurement Toolsmentioning

confidence: 95%

A comparison of three stem approaches to the teaching and learning of science topics: Students’ knowledge and scientific creativity

Diep

Thuy

Lai

et al. 2023

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The main objective of this study was to compare the effectiveness of three STEM approaches in implementing STEM activities in lower secondary school students based on knowledge and scientific creativity. A pre-posttest experimental design was adapted. Six classes with almost similar learning conditions were randomly selected and each class was assigned as a group, in which two groups followed the Stand–alone engineering design approach, two adopted the 5E-EDP approach, and two classes used the Jigsaw learning approach. The data were collected from the tests before and after the experiments and were statistically analyzed. The findings revealed that these three approaches enhanced the students’ gains in both knowledge and scientific creativity. Despite the overall progress, there existed a statistical difference among these models in their effects on the students’ knowledge and scientific creativity. While 5E-EDP and Jigsaw learning approach were proven to make substantial progress in these factors, the Stand-alone engineering design scored less significance. The study also showed that there was a statistical difference between the Stand-alone engineering design approach and Jigsaw learning approach. The respective second approach formed a foundation for better performance of the students’ knowledge and scientific creativity compared to the first one. Each approachhas its own value in enhancing students’ knowledge and scientific creativity. The result of this study could be a hint for STEM educators to apply appropriate methods in similar contexts to boost achievements.

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“…Additionally, it checks the exam invariance that is conclusive for building exam banks with well-calibrated exam questions. Item standardization can be classified as follows [5][6][7][8]:…”

Section: Methods Of Item Analysismentioning

confidence: 99%

“…(b) Compromise: The well-known one is Hofstee, which can be used in a lowresource setting. The designers decide the MPL after consensus.All of the above techniques should be executed before conducting the exam[5][6][7][8].…”

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confidence: 99%

Item Analysis of Multiple-choice Questions (MCQs): Assessment Tool For Quality Assurance Measures

Elgadal

Mariod²

2021

SJMS

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Background: Integration of assessment with education is vital and ought to be performed regularly to enhance learning. There are many assessment methods like Multiple-choice Questions, Objective Structured Clinical Examination, Objective Structured Practical Examination, etc. The selection of the appropriate method is based on the curricula blueprint and the target competencies. Although MCQs has the capacity to test students’ higher cognition, critical appraising, problem-solving, data interpretation, and testing curricular contents in a short time, there are constraints in its analysis. The authors aim to accentuate some consequential points about psychometric analysis displaying its roles, assessing its validity and reliability in discriminating the examinee’s performance, and impart some guide to the faculty members when constructing their exam questions bank. Methods: Databases such as Google Scholar and PubMed were searched for freely accessible English articles published since 2010. Synonyms and keywords were used in the search. First, the abstracts of the articles were viewed and read to select suitable match, then full articles were perused and summarized. Finally, recapitulation of the relevant data was done to the best of the authors’ knowledge. Results: The searched articles showed the capacity of MCQs item analysis in assessing questions’ validity, reliability, its capacity in discriminating against the examinee’s performance and correct technical flaws for question bank construction. Conclusion: Item analysis is a statistical tool used to assess students’ performance on a test, identify underperformed items, and determine the root causes of this underperformance for improvement to ensure effective and accurate students’ competency judgment. Keywords: assessment, difficulty index, discrimination index, distractors, MCQ item analysis

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Item analysis of multiple choice questions: A quality assurance test for an assessment tool

Cited by 47 publications

References 20 publications

Analysis of the difficulty index of item bank according to cognitive aspects during the Covid-19 pandemic

Analysis of the difficulty index of item bank according to cognitive aspects during the Covid-19 pandemic

A comparison of three stem approaches to the teaching and learning of science topics: Students’ knowledge and scientific creativity

Item Analysis of Multiple-choice Questions (MCQs): Assessment Tool For Quality Assurance Measures

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