Item response theory

Summary

In psychometrics, item response theory (IRT) (also known as latent trait theory, strong true score theory, or modern mental test theory) is a paradigm for the design, analysis, and scoring of tests, questionnaires, and similar instruments measuring abilities, attitudes, or other variables. It is a theory of testing based on the relationship between individuals' performances on a test item and the test takers' levels of performance on an overall measure of the ability that item was designed to measure. Several different statistical models are used to represent both item and test taker characteristics. Unlike simpler alternatives for creating scales and evaluating questionnaire responses, it does not assume that each item is equally difficult. This distinguishes IRT from, for instance, Likert scaling, in which "All items are assumed to be replications of each other or in other words items are considered to be parallel instruments". By contrast, item response theory treats the difficulty

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This study describes the development and validation process of a computational thinking (CT) test for adults. The team designed a set of items and explored a subset of those through two qualitative pilots. Then, in order to provide validity evidence based on the test content, a team of 11 subject-matter experts coded the initial pool of items using two different systems of categories based on CT components and contents. Next, the items were piloted on a sample of 289 participants, 137 experts in CT, and 152 novices. After a series of confirmatory factor analyses, a unidimensional model that represents algorithmic thinking was adopted. Further analyzing the psychometric quality of the 27 items, 20 of them with excellent reliability indices were selected for the test. Thus, this study provides a tool to evaluate adults’ CT: the Algorithmic Thinking Test for Adults (ATTA), which was developed according to psychometric standards. This article also reflects on the nature of CT as a construct.

2022

The competent Computational Thinking test (cCTt): Development and validation of an unplugged Computational Thinking test for upper primary school

Barbara Bruno, Francesco Mondada

With the increasing importance of Computational Thinking (CT) at all levels of education, it is essential to have valid and reliable assessments. Currently, there is a lack of such assessments in upper primary school. That is why we present the development and validation of the competent CT test (cCTt), an unplugged CT test targeting 7-9 year-old students. In the first phase, 37 experts evaluated the validity of the cCTt through a survey and focus group. In the second phase, the test was administered to 1519 students. We employed Classical Test Theory, Item Response Theory, and Confirmatory Factor Analysis to assess the instruments' psychometric properties. The expert evaluation indicates that the cCTt shows good face, construct, and content validity. Furthermore, the psychometric analysis of the student data demonstrates adequate reliability, difficulty, and discriminability for the target age groups. Finally, shortened variants of the test are established through Confirmatory Factor Analysis. To conclude, the proposed cCTt is a valid and reliable instrument, for use by researchers and educators alike, which expands the portfolio of validated CT assessments across compulsory education. Future assessments looking at capturing CT in a more exhaustive manner might consider combining the cCTt with other forms of assessments.

2022

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Volkan Cevher, Jonathan Mark Scarlett

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic framework for general noise models. For the special cases of no noise and symmetric noise, we find that the asymptotic number of tests required for vanishing error probability is within a factor log 2 approximate to 0.7 of the information-theoretic optimum at low-sparsity levels, and that with a small fraction of allowed incorrectly decoded items, this guarantee extends to all sublinear sparsity levels. In addition, we provide a converse bound showing that if one tries to move slightly beyond our low-sparsity achievability threshold using separate decoding of items and independent identically distributed randomized testing, the average number of items decoded incorrectly approaches that of a trivial decoder.

2018