A Probabilistic Model for Diagnosing Misconceptions By The Pattern Classification Approach

1985 ◽  
Vol 10 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Kikumi K. Tatsuoka
Keyword(s):  
Artificial Intelligence ◽  
Item Response Theory ◽  
Item Response ◽  
Pattern Classification ◽  
Probabilistic Model ◽  
Probabilistic Approach ◽  
Response Patterns ◽  
Classification Approach ◽  
Response Errors

This paper introduces a probabilistic approach to the classification and diagnosis of erroneous rules of operations that result from misconceptions (“bugs”) in a procedural domain of arithmetic. The model is different from the usual deterministic strategies common in the field of artificial intelligence because variability of response errors is explicitly treated through item response theory. As a concrete example, we analyze a dataset that reflects the use of erroneous rules of operation in problems of signed-number subtraction. The same approach, however, is applicable to the classification of several different groups of response patterns caused by a variety of different underlying misconceptions, different backgrounds of knowledge, or treatment.

scholarly journals A Computational Strategy for Classification of Enem Issues Based on Item Response Theory

2020 ◽  
Vol 27 (1) ◽  
pp. 92-111
Author(s):  
Gustavo Henrique Nunes ◽  
Bruno Alberto Soares Oliveira ◽  
Ciniro Aparecido Leite Nametala
Keyword(s):  
High School ◽  
Item Response Theory ◽  
Item Response ◽  
Generation System ◽  
Response Theory ◽  
Hit Rate ◽  
System A ◽  
Intelligent Simulation ◽  
Computational Strategy

The National High School Examination (ENEM) gains each year more importance, as it gradually, replacing traditional vestibular. Many simulations are done almost randomly by teachers or systems, with questions chosen without discretion. With this methodology, if a test needs to be reapplied, it is not possible to recreate it with questions that have the same difficulty as those used in the first evaluation. In this context, the present work presents the development of an ENEM Intelligent Simulation Generation System that calculates the parameters of Item Response Theory (TRI) of questions that have already been applied in ENEM and, based on them, classifies them. in groups of difficulty, thus enabling the generation of balanced tests. For this, the K-means algorithm was used to group the questions into three difficulty groups: easy, medium and difficult. To verify the functioning of the system, a simulation with 180 questions was generated along the ENEM model. It could be seen that in 37.7% of cases this happened. This hit rate was not greater because the algorithm confounded the difficulty of issues that are in close classes. However, the system has a hit rate of 92.8% in the classification of questions that are in distant groups.

scholarly journals Entropy-Based Measures for Person Fit in Item Response Theory

2017 ◽  
Vol 41 (7) ◽  
pp. 512-529 ◽  
Author(s):  
William R. Dardick ◽  
Brandi A. Weiss
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Preliminary Evidence ◽  
Model Fit ◽  
Response Patterns ◽  
Person Fit ◽  
Response Theory ◽  
Logistic Regression Models ◽  
Irt Models

This article introduces three new variants of entropy to detect person misfit ( Ei, EMi, and EMRi), and provides preliminary evidence that these measures are worthy of further investigation. Previously, entropy has been used as a measure of approximate data–model fit to quantify how well individuals are classified into latent classes, and to quantify the quality of classification and separation between groups in logistic regression models. In the current study, entropy is explored through conceptual examples and Monte Carlo simulation comparing entropy with established measures of person fit in item response theory (IRT) such as lz, lz*, U, and W. Simulation results indicated that EMi and EMRi were successfully able to detect aberrant response patterns when comparing contaminated and uncontaminated subgroups of persons. In addition, EMi and EMRi performed similarly in showing separation between the contaminated and uncontaminated subgroups. However, EMRi may be advantageous over other measures when subtests include a small number of items. EMi and EMRi are recommended for use as approximate person-fit measures for IRT models. These measures of approximate person fit may be useful in making relative judgments about potential persons whose response patterns do not fit the theoretical model.

scholarly journals Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
M. Ravichandran ◽  
G. Kulanthaivel ◽  
T. Chellatamilan
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Real Life ◽  
Response Theory ◽  
Sentence Level ◽  
Traditional Classification ◽  
Massive Number ◽  
The Common ◽  
Document Level

Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows:(1)lexicon based sentiment polarity of tweet messages;(2)the bigram cooccurrence relationship using naïve Bayesian;(3)the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

scholarly journals Examining the Two Categorical Datas by Jmetrik, Bilog-Mg and Irtpro with Application of Mathematics Exam

2017 ◽  
Vol 13 (33) ◽  
pp. 20 ◽  
Author(s):  
Gokhan Aksu ◽  
Cigdem Reyhanlıoglu ◽  
Mehmet Taha Eser
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Significant Relationship ◽  
Logistic Model ◽  
Relational Model ◽  
Response Patterns ◽  
Response Theory ◽  
Final Exam ◽  
Data Set ◽  
Individual Ability

The aim of this study was to examination of two-category rated mathematics course final exam based on Item Response Theory data analyzed with the help of 2-Parameter Logistic Model and determination of the ability and standard errors with the help of different programs. This study involves a comparative interpretation of some descriptive statistics and analysis. Therefore, research has characterized as relational model which is one of the general survey models. For this purpose, 771 students’ final achievement test responses to a 20-point final exam, were analyzed by BILOG, IRT PRO and JMETRİK programs. Item Response Theory assumptions were analyzed with SPSS and Factor 9.3 programs. Working as a result of the analysis of data all of the IRT assumptions are met and the most appropriate model of data set has been concluded that the twoparameter logistic model. The study also found that there is a statistically significant relationship between the estimated parameters related to individual ability and error at the level of .01. Especially compared to the others there is also significant relationship between JMETRİK and IRT PRO. Different models and methods of research proposals have been made in terms of response patterns to be analyzed a gain for the same data set.

Applying Item Response Theory Modeling to Identify Social (Pragmatic) Communication Disorder

2020 ◽  
Vol 63 (6) ◽  
pp. 1916-1932 ◽  
Author(s):  
Haiying Yuan ◽  
Christine Dollaghan
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Sensitivity And Specificity ◽  
Social Communication ◽  
National Database ◽  
Communication Disorder ◽  
Social Communication Questionnaire ◽  
Autism Research ◽  
The Social ◽  
Pragmatic Communication

Purpose No diagnostic tools exist for identifying social (pragmatic) communication disorder (SPCD), a new Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition category for individuals with social communication deficits but not the repetitive, restricted behaviors and interests (RRBIs) that would qualify them for a diagnosis of autism spectrum disorder (ASD). We explored the value of items from a widely used screening measure of ASD for distinguishing SPCD from typical controls (TC; Aim 1) and from ASD (Aim 2). Method We applied item response theory (IRT) modeling to Social Communication Questionnaire–Lifetime ( Rutter, Bailey, & Lord, 2003 ) records available in the National Database for Autism Research. We defined records from putative SPCD ( n = 54), ASD ( n = 278), and TC ( n = 274) groups retrospectively, based on National Database for Autism Research classifications and Autism Diagnostic Interview–Revised responses. After assessing model assumptions, estimating model parameters, and measuring model fit, we identified items in the social communication and RRBI domains that were maximally informative in differentiating the groups. Results IRT modeling identified a set of seven social communication items that distinguished SPCD from TC with sensitivity and specificity > 80%. A set of five RRBI items was less successful in distinguishing SPCD from ASD (sensitivity and specificity < 70%). Conclusion The IRT modeling approach and the Social Communication Questionnaire–Lifetime item sets it identified may be useful in efforts to construct screening and diagnostic measures for SPCD.

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