Dataset Information

DINA Models for Multiple-Choice Items With Few Parameters: Considering Incorrect Answers.

ABSTRACT: The deterministic-input, noisy "and" gate (DINA) model can judge whether an individual examinee has mastered each skill that is needed to answer an item correctly. This information is useful for students to know their deficits and for teachers to teach effectively. The DINA model is a statistical model for binary (correct or incorrect) data. However, recently a DINA model for multiple-choice items was developed by de la Torre. The model is aimed at obtaining information about students' skills from incorrect answers. In the present study, new DINA models for multiple-choice items are developed that need many fewer parameters while still being able to express various answering probabilities without any restrictions on the form of the Q-matrix. Simulations using a Markov chain Monte Carlo method are performed to demonstrate the efficacies of the proposed models compared with the DINA model for binary data and the model of de la Torre for multiple-choice items, if appropriate starting values are set.

SUBMITTER: Ozaki K

PROVIDER: S-EPMC5978611 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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DINA Models for Multiple-Choice Items With Few Parameters: Considering Incorrect Answers.

Ozaki Koken K

Applied psychological measurement 20150327 6

The deterministic-input, noisy "and" gate (DINA) model can judge whether an individual examinee has mastered each skill that is needed to answer an item correctly. This information is useful for students to know their deficits and for teachers to teach effectively. The DINA model is a statistical model for binary (correct or incorrect) data. However, recently a DINA model for multiple-choice items was developed by de la Torre. The model is aimed at obtaining information about students' skills fr ...[more]

PMID: 29881017

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Project description:Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

Dataset Information

DINA Models for Multiple-Choice Items With Few Parameters: Considering Incorrect Answers.

Publications

DINA Models for Multiple-Choice Items With Few Parameters: Considering Incorrect Answers.

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