Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field

Reichenberg, Ray E.

doi:10.1080/08957347.2018.1495217

Cited by 22 publications

(17 citation statements)

References 42 publications

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“…All variables are observed except the cognitive load variables. Therefore, the cognitive load variables were estimated based on the responses to the survey items (Figure 2) At the first step, for the proficiency variable, equal probabilities were considered to take the values for four grades when prior information regarding learner proficiencies on the course had not yet been obtained [18]. In addition, the cognitive load nodes also took equal probabilities of each level when no prior information had been obtained regarding learners' cognitive load.…”

Section: Bayesian Network Analysis For Diagnostic Information About the Cognitive Loadmentioning

confidence: 99%

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Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

Choi¹,

Kim²

2021

Sustainability

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A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

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Section: Bayesian Network Analysis For Diagnostic Information About the Cognitive Loadmentioning

confidence: 99%

“…This study investigated a learner's cognitive load in an e-learning system using learning analytics obtained by a Bayesian Network (BN) [17,18]. We estimated a personal diagnostic information regarding the cognitive load from the BN.…”

Section: Introductionmentioning

confidence: 99%

Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

Choi¹,

Kim²

2021

Sustainability

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“…As such, it is different in that there are no "high-stakes" test-taking or decision-making, but rather individual assessments provide feedback that then allows the learner to analyze "their own performance, formulate concrete learning goals and demonstrably attain them", which is opposed to the previously discussed assessment structure where the assessment is the evidence of learning or the lack thereof ( [84], p. 211). Further reading in this area should be directed to [85][86][87][88].…”

Section: Concluding Thoughts and The Future Of Educational Psychometricsmentioning

confidence: 99%

On Educational Assessment Theory: A High-Level Discussion of Adolphe Quetelet, Platonism, and Ergodicity

Bloniasz

2021

Philosophies

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Educational assessments, specifically standardized and normalized exams, owe most of their foundations to psychological test theory in psychometrics. While the theoretical assumptions of these practices are widespread and relatively uncontroversial in the testing community, there are at least two that are philosophically and mathematically suspect and have troubling implications in education. Assumption 1 is that repeated assessment measures that are calculated into an arithmetic mean are thought to represent some real stable, quantitative psychological trait or ability plus some error. Assumption 2 is that aggregated, group-level educational data collected from assessments can then be interpreted to make inferences about a given individual person over time without explicit justification. It is argued that the former assumption cannot be taken for granted; it is also argued that, while it is typically attributed to 20th century thought, the assumption in a rigorous form can be traced back at least to the 1830s via an unattractive Platonistic statistical thesis offered by one of the founders of the social sciences—Belgian mathematician Adolphe Quetelet (1796–1874). While contemporary research has moved away from using his work directly, it is demonstrated that cognitive psychology is still facing the preservation of assumption 1, which is becoming increasingly challenged by current paradigms that pitch human cognition as a dynamical, complex system. However, how to deal with assumption 1 and whether it is broadly justified is left as an open question. It is then argued that assumption 2 is only justified by assessments having ergodic properties, which is a criterion rarely met in education; specifically, some forms of normalized standardized exams are intrinsically non-ergodic and should be thought of as invalid assessments for saying much about individual students and their capability. The article closes with a call for the introduction of dynamical mathematics into educational assessment at a conceptual level (e.g., through Bayesian networks), the critical analysis of several key psychological testing assumptions, and the introduction of dynamical language into philosophical discourse. Each of these prima facie distinct areas ought to inform each other more closely in educational studies.

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“…Versions of BNs that are sometimes referred to as Dynamic Bayes Nets (DBNs) (and the process of Bayesian Knowledge Tracing (BKT) more generally) are prominent in the Intelligent Tutoring System (ITS) and Learning Analytics research communities 35 . While we do not present an example of a DBN or BKT in this paper, we note their potential applicability for training programs that may seek to explicitly model changes in skills and attributes over time 36 …”

Section: Assembling Bns To Address Challenges In Programmatic Assessmentmentioning

confidence: 99%

The potential use of Bayesian Networks to support committee decisions in programmatic assessment

Zoanetti

Pearce

2020

Medical Education

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Context The benefits of programmatic assessment are well‐established. Evidence from multiple assessment formats is accumulated and triangulated to inform progression committee decisions. Committees are consistently challenged to ensure consistency and fairness in programmatic deliberations. Traditional statistical and psychometric techniques are not well‐suited to aggregating different assessment formats accumulated over time. Some of the strengths of programmatic assessment are also vulnerabilities viewed through this lens. While emphasis is often placed on data richness and considered input of qualified experts, committees reasonably wish for practical, defensible solutions to these challenges. Methods We draw upon on existing literature regarding Bayesian Networks (BN), noting their utility and application in educational systems. We provide illustrative examples of how they could potentially be used in contexts that embed programmatic principles. We show a simple BN for a knowledge domain before presenting a full‐scale ‘proof of concept’ BN to support committee decisions. We zoom in on one ‘node’ to demonstrate the capacity of incorporating disparate evidence throughout the network. Conclusions Bayesian Networks offer an approach that is theoretically well‐supported for programmatic assessment. They can aid committees in managing evidence accumulation, help them make inferences under conditions of uncertainty, and buttress decisions by adding a layer of defensibility to the process. They are a pragmatic tool adding value to the programmatic space by applying a complementary statistical framework. We see four major benefits of BNs in programmatic assessment: BNs allow for visual capturing of evidentiary arguments by committees during decision‐making; ‘recommendations’ from probabilistic pathways can be used by committees to confirm their qualitative judgments; BNs can ensure precedents are maintained and consistency occurs over time; and the imperative to capture data richness is maintained without resorting to questionable methodological strategies such as adding qualitatively different things together. Further research into their feasibility and robustness in practice is warranted.

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Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field

Cited by 22 publications

References 42 publications

Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

On Educational Assessment Theory: A High-Level Discussion of Adolphe Quetelet, Platonism, and Ergodicity

The potential use of Bayesian Networks to support committee decisions in programmatic assessment

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