Bayesian Student Models Based on Item to Item Knowledge Structures

Desmarais, Michel C.; Gagnon, Michel

doi:10.1007/11876663_11

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…This has been the primary focus and application of Bayesian methods in education thus far. Indeed, most studies employing Bayesian methods have used them to perform psychometric and factor analyses of novel assessment types (e.g., multi-skill itemized activities and question types) and surveys to study student comprehension, cognition, and attitudes toward learning (Desmarais and Gagnon, 2006;Pardos et al, 2008;Brassil and Couch, 2019;Martinez, 2021;Parkin and Wang, 2021;Vaziri et al, 2021;Wang et al, 2021). The insights obtained from these studies have led to the design, development, and deployment of more adaptive learning and student-focused knowledge assessment content, based on their aptitude levels, allowing educators to learn more about student comprehension and how individualized content can be tailored to students (Drigas et al, 2009).…”

Section: Application To Stem Educational Settings and ML Assessmentmentioning

confidence: 99%

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

Bertolini

Finch²,

Nehm³

2023

Front. Educ.

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IntroductionAs artificial intelligence (AI) technology becomes more widespread in the classroom environment, educators have relied on data-driven machine learning (ML) techniques and statistical frameworks to derive insights into student performance patterns. Bayesian methodologies have emerged as a more intuitive approach to frequentist methods of inference since they link prior assumptions and data together to provide a quantitative distribution of final model parameter estimates. Despite their alignment with four recent ML assessment criteria developed in the educational literature, Bayesian methodologies have received considerably less attention by academic stakeholders prompting the need to empirically discern how these techniques can be used to provide actionable insights into student performance.MethodsTo identify the factors most indicative of student retention and attrition, we apply a Bayesian framework to comparatively examine the differential impact that the amalgamation of traditional and AI-driven predictors has on student performance in an undergraduate in-person science, technology, engineering, and mathematics (STEM) course.ResultsInteraction with the course learning management system (LMS) and performance on diagnostic concept inventory (CI) assessments provided the greatest insights into final course performance. Establishing informative prior values using historical classroom data did not always appreciably enhance model fit.DiscussionWe discuss how Bayesian methodologies are a more pragmatic and interpretable way of assessing student performance and are a promising tool for use in science education research and assessment.

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Section: Application To Stem Educational Settings and ML Assessmentmentioning

confidence: 99%

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

Bertolini

Finch²,

Nehm³

2023

Front. Educ.

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“…Bayesian networks have also been used to model two different approaches to determine the probability a multi skill question has of being correct [15] and to predict future group performance in face-to-face collaborative learning [16]. It has also been used to predict end-of-year exam performance through student activity with online tutors [17] and to predict item response outcome [18]. Different types of neural network models have been used in prediction as well.…”

Section: Overview Of Literaturementioning

confidence: 99%

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

Oyerinde¹,

Chia²

2017

IJCA

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Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management..The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos, Nigeria.Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance.

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“…The system went through an initial testing with very satisfying success rate and it was also successfully tested through an actual e-learning application called Multitutor (Sevarac, 2006). Desmarais and Gagnon (2006) explored the use of two Bayesian frameworks (of one typical Bayesian network and of a constrained version under the assumption of local independence) in an attempt to predict item outcome. The two approaches were compared over their predictive power and their performance was measured using data from real tests to conduct simulations.…”

Section: Studies Classifying Students According To Their Knowledge Lementioning

confidence: 99%

“…After the choice of an item, the result of the answer, whether it is a success or a failure, is fed to the inference algorithm. Although both methods turn out to give accurate predictions, the constrained one obtains better predictive results (Desmarais and Gagnon, 2006). Wei and Blank (2006) conducted an experiment in order to study the sufficiency of atomic Bayesian networks for student models.…”

Section: Studies Classifying Students According To Their Knowledge Lementioning

confidence: 99%

Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis

Drigas

Argyri

Vrettaros

2009

IJSHC

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Abstract:Over the last decade, artificial intelligence has offered a wide range of tools that have proved to be of vital importance for educational research. Indeed, logic, classifiers and machine learning methods, probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various approaches that artificial intelligence has offered in dealing with real life problems. This paper attempts to explore the research that has been conducted on the application of the most typical and popular soft computing techniques [fuzzy logic, neural networks, Bayesian networks, genetic programming and hybrid approaches such as neurofuzzy systems and genetic programming neural networks (GPNNs)] in student modelling over the decade 1999-2009. This latest research trend is a part of every intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used so as to point out their qualities and then we describe the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

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Bayesian Student Models Based on Item to Item Knowledge Structures

Cited by 5 publications

References 16 publications

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis

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