Merged Tree-CAT: A fast method for building precise computerized adaptive tests based on decision trees

Rodríguez-Cuadrado, Javier; Delgado-Gómez, David; Laria, Juan C.; Rodríguez-Cuadrado, Sara

doi:10.1016/j.eswa.2019.113066

Cited by 9 publications

(10 citation statements)

References 18 publications

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“…A way to overcome this in future work could be through constructing trees with nonbinary splits, for example, by treating all variables as categorical and using chi-square automatic interaction detection (CHAID) [ 20 , 21 ]. This technique could have a higher risk of overfitting, although this might be mitigated through split rules and by merging similar branches [ 6 ]. Future work is needed to test whether CHAID could create more efficient, more accurate, adaptive assessments by incorporating non-PROM data at no extra burden to the assessment taker.…”

Section: Discussionmentioning

confidence: 99%

“…Recursive partitioning is a form of machine learning that involves iteratively splitting labeled data sets into subgroups to minimize the within-subgroup variance of an outcome, such as a PROM score [5]. Recent studies have explored the use of personalized health assessments based on decision trees constructed with similar techniques [6][7][8]. These trees split respondents into subgroups based on their responses to individual items.…”

Section: Recursive Partitioningmentioning

confidence: 99%

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Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

et al. 2021

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Background Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate. Objective We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models. Methods We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson’s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error. Results Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data. Conclusions When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study.

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Section: Discussionmentioning

confidence: 99%

Section: Recursive Partitioningmentioning

confidence: 99%

Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

et al. 2021

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“…Over the years, in different projects, various tools have been applied in the development of the phases that make up the CATs, for example: threeparameter logistic model for item calibration (Lee et al, 2018); maximum likelihood estimation for the evaluator's skill estimation (Albano et al, 2019); and root mean square differences as an evaluation criterion (Stafford et al, 2019), among others. Specifically, for the item selection stage, work has been done to solve the problems presented by Fisher's Maximum Information, using other selection strategies, for example, Bayesian networks (Tokusada and Hirose, 2016), Greedy algorithm (Bengs, Brefeld and Krohne, 2018), Kullback-Leibler Information (Chen et al, 2017), Minimum Expected Subsequent Variance (Rodríguez-Cuadrado et al, 2020), to mention a few which, while they have achieved favorable results, most have only been in studies of simulation and not in real application.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

Ortiz¹,

Rodríguez-Mazahua²,

Mejía³

et al. 2020

REVISTA PERSPECT. EMPRESARIAL

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One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.

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“…As a rapidly growing area of e-assessment, E-testing involves the delivery of examinations and assessments on screen, using either local systems or web-based systems. In general, e-testing provides automatic assemblies of uniform test forms, for which each form comprises a different set of items but which still has equivalent measurement accuracy [1][2][3][4][5][6][7][8][9][10]. Uniform test forms are assembled for which all forms have equivalent qualities for equal evaluation of examinees who have taken different test forms.…”

Section: Introductionmentioning

confidence: 99%

Deep Item Response Theory as a Novel Test Theory Based on Deep Learning

2021

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Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees’ ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees’ abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees’ abilities from a distribution. Experiment results demonstrate that Deep-IRT estimates examinees’ abilities more accurately than the traditional IRT does. Moreover, Deep-IRT can express actual examinees’ ability distributions flexibly, not merely following the standard normal distribution assumed for traditional IRT. Furthermore, the results show that Deep-IRT more accurately predicts examinee responses to unknown items from the examinee’s own past response histories than IRT does.

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Merged Tree-CAT: A fast method for building precise computerized adaptive tests based on decision trees

Cited by 9 publications

References 18 publications

Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study

Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

Deep Item Response Theory as a Novel Test Theory Based on Deep Learning

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