Computer Adaptive Testing: Comparison of a Probabilistic Network Approach with Item Response Theory

Desmarais, Michel C.; Pu, Xiaoming

doi:10.1007/11527886_51

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Within the area of instructional technology, there has recently been a renaissance of applications aimed at enhancing educational interventions. To list but a very few examples, neural networking systems have been applied to classifying and predicting student math skills during interactive training (e.g., Ninness et al, 2005), developing student learning models and predicting knowledge (e.g., Desmarais, Meshkinfam, & Gagnon, 2006), adapting computer student assessment and prediction (e.g., Desmarais & Pu, 2005), classifying and predicting student musical skills (e.g., Ninness, et al, 2013), developing instructional systems providing concurrent assessment and tutoring (e.g., Feng, Heffernan, & Koedinger, 2009), developing online personalized learning systems (e.g., Heller, Steiner, Hockemeyer, & Albert, 2006), and analyzing online cognitive tutors (e.g., Aleven, 2013;Koedinger, Corbett, & Perfetti, 2012).…”

Section: The Rise Of Neural Networkmentioning

confidence: 99%

The Emergence of Stimulus Relations: Human and Computer Learning

Ninness¹,

Ninness

Rumph³

et al. 2017

Perspect Behav Sci

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Traditionally, investigations in the area of stimulus equivalence have employed humans as experimental participants. Recently, however, artificial neural network models (often referred to as connectionist models [CMs]) have been developed to simulate performances seen among human participants when training various types of stimulus relations. Two types of neural network models have shown particular promise in recent years. RELNET has demonstrated its capacity to approximate human acquisition of stimulus relations using simulated matching-to-sample (MTS) procedures (e.g., Lyddy & Barnes-Holmes Journal of Speech and Language Pathology and Applied Behavior Analysis, 2, 14-24, 2007). Other newly developed connectionist algorithms train stimulus relations by way of compound stimuli (e.g., Tovar & Chavez

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Section: The Rise Of Neural Networkmentioning

confidence: 99%

The Emergence of Stimulus Relations: Human and Computer Learning

Ninness¹,

Ninness

Rumph³

et al. 2017

Perspect Behav Sci

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“…Bayesian networks have been successfully applied to assess subskills and competencies in various domains, including physics problem solving (Martin & VanLehn, 1995;VanLehn & Martin, 1998), troubleshooting (Gitomer, Steinberg, & Mislevy, 1995), mixed-number subtraction (Mislevy, 1995), proportional reasoning (Beland & Mislevy, 1996), knowledge of English (Almond & Mislevy, 1999), dental hygiene (Mislevy, Steinberg, Breyer, & Almond, 2002), and student knowledge in computer-based instruction systems (e.g., Conati, Gertner, & VanLehn, 2002;Pardos, Heffernan, Anderson, & Heffernan, 2007;VanLehn & Niu, 2001). In recent years, interest in applying Bayesian networks in educational assessments seems to be growing (e.g., Desmarais & Pu, 2005;Heiner, Heffernan, & Barnes, 2007).…”

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confidence: 99%

Diagnosis of Subtraction Bugs Using Bayesian Networks

Lee

Corter

2010

Applied Psychological Measurement

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Diagnosis of misconceptions or ''bugs'' in procedural skills is difficult because of their unstable nature. This study addresses this problem by proposing and evaluating a probability-based approach to the diagnosis of bugs in children's multicolumn subtraction performance using Bayesian networks. This approach assumes a causal network relating hypothesized subtraction bugs to the observed test items. Two research questions are tested within this framework. First, it is investigated whether more reliable assessment of latent subtraction bugs can be achieved by hypothesizing and using subskill nodes in the Bayesian network as causal factors affecting bugs. Second, network performance is evaluated using two types of testing situations, one using binary data (items scored as correct or incorrect) and the other simulating a multiple-choice test format with diagnostic use of specific wrong answers. The resulting four types of Bayesian networks are evaluated for their effectiveness in bug diagnosis. All four networks show good performance, with even the simplest network (bug nodes only, binary data) giving overall bug diagnosis rates of at least 85%. Prediction is best with the most complex network (bug and subskill nodes, diagnostic use of specific wrong answers), for which the correct diagnosis rate reaches 99%. These results suggest that stable and reliable bug diagnosis can be achieved using a Bayesian network framework, but that the stability and effectiveness of diagnosis is increased when the network includes latent subskills in addition to bugs as causal factors, and when specific wrong answers are used for diagnostic purposes.

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A Tool for Adaptive E-Assessment of Project Management Competences

Bodea

Dascălu

2012

Intelligent and Adaptive Learning Systems

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This chapter proposes an e-assessment method for project management competences, using the computer adaptive testing (CAT) principle. Competences are represented using concept space graphs. The proposed model increases the tests configurability by considering several knowledge constraints when an item is selected. The proposed model is also seen as a self-directed-learning-tool, useful in the preparation process for project management certifications. The model is validated by comparison with an existing e-assessment tool, used for simulation purposes; statistic results are presented and analyzed. Although the initial level of knowledge of each user has a great impact on the final results obtained by that user, preparation with the proposed e-assessment method proved to be more efficient.

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Computer Adaptive Testing: Comparison of a Probabilistic Network Approach with Item Response Theory

Cited by 3 publications

References 7 publications

The Emergence of Stimulus Relations: Human and Computer Learning

The Emergence of Stimulus Relations: Human and Computer Learning

Diagnosis of Subtraction Bugs Using Bayesian Networks

A Tool for Adaptive E-Assessment of Project Management Competences

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