<b>PerFit</b>: An <i>R</i> Package for Person-Fit Analysis in IRT

Tendeiro, Jorge N.; Meijer, Rob R.; Niessen, A. Susan M.

doi:10.18637/jss.v074.i05

Cited by 72 publications

(61 citation statements)

References 51 publications

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“…1. We used the cutoff function in the R package PerFit (Tendeiro, Meijer, & Niessen, 2016) to simulate 50,000 test takers. The cutoff function takes in (0, 1) response data from J test takers across I items as input and fits a Rasch model to it resulting in estimated difficulty item parameters for each item and predicted ability parameters for each test taker.…”

Section: Irt-based and Rt Methodsmentioning

confidence: 99%

Use of Data Mining Methods to Detect Test Fraud

Man

Harring

Sinharay

2019

J Educational Measurement

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Data mining methods have drawn considerable attention across diverse scientific fields. However, few applications could be found in the areas of psychological and educational measurement, and particularly pertinent to this article, in test security research. In this study, various data mining methods for detecting cheating behaviors on large-scale assessments are explored as an alternative to the traditional methods including person-fit statistics and similarity analysis. A common data set from the Handbook of Quantitative Methods for Detecting Cheating on Tests (Cizek & Wollack) was used for comparing the performance of the different methods. The results indicated that the use of data mining methods may combine multiple sources of information about test takers' performance, which may lead to higher detection rate over traditional item response and response time methods. Several recommendations, all based on our findings, are provided to practitioners.

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Section: Irt-based and Rt Methodsmentioning

confidence: 99%

Use of Data Mining Methods to Detect Test Fraud

Man

Harring

Sinharay

2019

J Educational Measurement

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“…Guttman Error (Gnormed): Guttman error is defined as the number of item pairs in which a learner answers an easier item incorrectly and a more difficult item correctly, normalized by the total number of pairs [16]. We used a non-parametric model implemented in R's Per Fit [26]. It is computed on all possible pairs.…”

Section: Methodsmentioning

confidence: 99%

Assessment that matters: Balancing reliability and learner-centered pedagogy in MOOC assessment

Alexandron¹,

Wiltrout²,

Berg³

et al. 2019

Preprint

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Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior – the amount of cheating and learners’ engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an ‘analytically-verified’ MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention.

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“…Los PAR se identificaron a través de cuatro índices no paramétricos basados en la Teoría de Respuesta al Ítem: C (Sato, 1975), U3 (van der Flier, 1980), MCI (Harnisch & Linn, 1981) y H T (Sijtsma, 1986;Sijtsma & Mejer, 1992), al ser índices que funcionan adecuadamente en situaciones diversas (Karabatsos, 2003;Tendeiro & Meijer, 2014). Para el cálculo de los índices se empleó el paquete PerFit de R (Tendeiro, 2015;, tratando como errores las respuestas en blanco.…”

Section: Análisis De Datosunclassified