scholarly journals Flow With an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow During Artificial Tutoring

2021 ◽  
Author(s):  
Hyeon-Ah Kang ◽  
Adam Sales ◽  
Tiffany A. Whittaker
Keyword(s):  
Latent Variable ◽  
Latent Class ◽  
Intelligent Tutoring System ◽  
Latent Class Model ◽  
Model Fitting ◽  
Latent Variable Modeling ◽  
Flow State ◽  
Modeling Framework ◽  
Modeling Approach ◽  
Comprehensive Survey

Increasing use of intelligent tutoring system (ITS) in education calls for analytic methods that can unravel students' learning behaviors. In this study we suggest a latent variable modeling approach to tracking flow during artificial tutoring. Flow is a mental state a student achieves when immersed in deep learning. Modeling latent flow helps identify when and how students flow during tutoring. The result of the model can also inform the functioning of ITS and provide instrumental information for designing interventions. Three latent variable models are considered to draw discrete inference on the flow state: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. For each of the models, we suggest practical model-fitting strategies, addressing the assumptions and estimation constraints. Using example data from Cognitive Tutor Algebra I, we show that each model provides unique and meaningful information about student's learning process. Through comprehensive survey of the models, we evaluate merits and drawbacks of each modeling framework and illuminate areas that need future development.

Modelling Museum Visitors’ Perception and Experience of Authenticity: Examining Heterogeneity with a Finite Mixture Model

2021 ◽  
Vol 56 ◽  
pp. 0-0
Author(s):  
Claudia Bauer-Krösbacher ◽  
Josef Mazanec
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Latent Class ◽  
Structural Model ◽  
Unobserved Heterogeneity ◽  
Model Fitting ◽  
Equation Model ◽  
Finite Mixture ◽  
Museum Visitors ◽  
Specific Strength

Purpose. In this study, the authors explore the role of museum visitors’ perceptions and experiences of authenticity. They introduce several variants of authenticity experience and analyse how they are intertwined and feed visitor satisfaction. Method. The authors apply a multi-step model fitting and validation procedure including inferred causation methods and finite mixture modelling to verify whether the visitors’ perceptions of authenticity are subject to unobserved heterogeneity. They elaborate an Authenticity Model that demonstrates out-of-sample validity and generalisability by being exposed to new data for another cultural attraction in another city. Then, they address the heterogeneity hypothesis and evaluate it for the case study with the larger sample. Findings. In both application cases, the Sisi museum in Vienna and the Guinness Storehouse in Dublin, the empirical results support the assumed cause-effect sequence, translating high quality information display—from traditional and multimedia sources—into Perceived Authenticity and its experiential consequences such as Depth and Satisfaction. Accounting for unobserved heterogeneity detects three latent classes with segment-specific strength of relationships within the structural model. Research and conclusions limitations. The combined latent-class, structural-equation model needs validation with another sample that would have to be larger than the available Guinness database. Future studies will have to complement the purely data-driven search for heterogeneity with theory-guided reasoning about potential causes of diversity in the strength of the structural relationships. Practical implications. Cultural heritage sites are among the attractions most typical of city tourism. History tends to materialise in the artefacts accumulated by the population among the urban agglomerations, and museums are the natural places for preserving exhibits of cultural value. Authenticity must be considered an important quality assessment criterion for many visitors, whereby, the distinction between object authenticity and existential authenticity is crucial. Originality. In addition to making substantive contributions to authenticity theory, the authors also extend previous research in terms of methodological effort. Authenticity research, so far, has neither exploited inferred causation methods nor combined latent variable modelling with detecting unobserved heterogeneity. Type of paper: Research article.

Cluster Analysis with General Latent Class Model

2011 ◽  
pp. 225-230
Author(s):  
Dingxi Qiu ◽  
Edward C. Malthouse
Keyword(s):  
Cluster Analysis ◽  
Statistical Models ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Teaching Style ◽  
Teaching Styles ◽  
Binary Responses ◽  
Cluster Membership ◽  
Wide Range

Cluster analysis is a set of statistical models and algorithms that attempt to find “natural groupings” of sampling units (e.g., customers, survey respondents, plant or animal species) based on measurements. The observable measurements are sometimes called manifest variables and cluster membership is called a latent variable. It is assumed that each sampling unit comes from one of K clusters or classes, but the cluster identifier cannot be observed directly and can only be inferred from the manifest variables. See Bartholomew and Knott (1999) and Everitt, Landau and Leese (2001) for a broader survey of existing methods for cluster analysis. Many applications in science, engineering, social science, and industry require grouping observations into “types.” Identifying typologies is challenging, especially when the responses (manifest variables) are categorical. The classical approach to cluster analysis on those data is to apply the latent class analysis (LCA) methodology, where the manifest variables are assumed to be independent conditional on the cluster identity. For example, Aitkin, Anderson and Hinde (1981) classified 468 teachers into clusters according to their binary responses to 38 teaching style questions. This basic assumption in classical LCA is often violated and seems to have been made out of convenience rather than it being reasonable for a wide range of situations. For example, in the teaching styles study two questions are “Do you usually allow your pupils to move around the classroom?” and “Do you usually allow your pupils to talk to one another?” These questions are mostly likely correlated even within a class.

scholarly journals Examining Population Differences in Within-Person Variability in Longitudinal Designs Using Latent Variable Modeling: An Application to the Study of Cognitive Functioning of Older Adults

2018 ◽  
Vol 79 (3) ◽  
pp. 598-609 ◽  
Author(s):  
N. Maritza Dowling ◽  
Tenko Raykov ◽  
George A. Marcoulides
Keyword(s):  
Repeated Measures ◽  
Latent Variable ◽  
Cognitive Test ◽  
Latent Variable Modeling ◽  
Population Differences ◽  
Modeling Framework ◽  
Interval Estimates ◽  
Modeling Methodology ◽  
Multiple Dimensions ◽  
Verbal Episodic Memory

Longitudinal studies have steadily grown in popularity across the educational and behavioral sciences, particularly with the increased availability of technological devices that allow the easy collection of repeated measures on multiple dimensions of substantive relevance. This article discusses a procedure that can be used to evaluate population differences in within-person (intraindividual) variability in such longitudinal investigations. The method is based on an application of the latent variable modeling methodology within a two-level modeling framework. The approach is used to obtain point and interval estimates of the differences in within-person variance and in the strength of correlative effects of repeated measures between normal and very mildly demented persons in a longitudinal study of a diagnostic cognitive test assessing verbal episodic memory.

scholarly journals A Finite Mixture Model for Genotype and Environment Interactions: Detecting Latent Population Heterogeneity

2006 ◽  
Vol 9 (3) ◽  
pp. 412-423 ◽  
Author(s):  
Nathan A. Gillespie ◽  
Michael C. Neale
Keyword(s):  
Mixture Model ◽  
Variance Components ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Trait ◽  
Model Fitting ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Population Heterogeneity ◽  
The Impact

AbstractApproaches such as DeFries-Fulker extremes regression (LaBuda et al., 1986) are commonly used in genetically informative studies to assess whether familial resemblance varies as a function of the scores of pairs of twins. While useful for detecting such effects, formal modeling of differences in variance components as a function of pairs' trait scores is rarely attempted. We therefore present a finite mixture model which specifies that the population consists of latent groups which may differ in (i) their means, and (ii) the relative impact of genetic and environmental factors on within-group variation and covariation. This model may be considered as a special case of a factor mixture model, which combines the features of a latent class model with those of a latent trait model. Various models for the class membership of twin pairs may be employed, including additive genetic, common environment, specific environment or major locus (QTL) factors. Simulation results based on variance components derived from Turkheimer and colleagues (2003), illustrate the impact of factors such as the difference in group means and variance components on the feasibility of correctly estimating the parameters of the mixture model. Model-fitting analyses estimated group heritability as .49, which is significantly greater than heritability for the rest of the population in early childhood. These results suggest that factor mixture modeling is sufficiently robust for detecting heterogeneous populations even when group mean differences are modest.

scholarly journals The Q-Matrix Anchored Mixture Rasch Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Ming-Chi Tseng ◽  
Wen-Chung Wang
Keyword(s):  
Rasch Model ◽  
Latent Variable ◽  
Latent Class ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Latent Classes ◽  
Modeling Framework ◽  
Class Invariant ◽  
Common Scale ◽  
Q Matrix

Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds. Under this modeling framework, students can be characterized by both their location on a continuous latent variable and by their latent class membership according to Students’ responses. It is important to identify anchor items for constructing a common scale between latent classes beforehand under the mixture IRT framework. Then, all model parameters across latent classes can be estimated on the common scale. In the study, we proposed Q-matrix anchored mixture Rasch model (QAMRM), including a Q-matrix and the traditional mixture Rasch model. The Q-matrix in QAMRM can use class invariant items to place all model parameter estimates from different latent classes on a common scale regardless of the ability distribution. A simulation study was conducted, and it was found that the estimated parameters of the QAMRM recovered fairly well. A real dataset from the Certificate of Proficiency in English was analyzed with the QAMRM, LCDM. It was found the QAMRM outperformed the LCDM in terms of model fit indices.

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