Walter L Leite
Datos Biográficos
| ID | 6030033 |
|---|---|
| NOMBRE | Walter L Leite |
| NOMBRES | Walter L |
| APELLIDO | Leite |
| FIRMA | LEITE W L |
| AFILIACIONES | University of Florida |
| ORCID | 0000-0001-7655-5668 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 34 |
| TOTAL DE CITAS | 4 |
| TOTAL COMO AUTOR | 34 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2002 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 1 |
Understanding the heterogeneity of effects of teacher professional development
Professional development (PD) programs for in-service teachers play a critical role in providing opportunities to deepen content knowledge, refine pedagogical skills, and adapt to evolving standards and diverse classroom contexts. Although several recent meta-analyses of PD studies have shown overall positive effects, these effects can vary widely depending on study characteristics, participants, settings, and outcomes. The current study examines…
Sensitivity Analyses for Omitted Confounders in Structural Equation Models with Tabu Search Optimization
Structural equation modeling (SEM) is a popular methodological approach for representing and testing hypothesized relationships among observed and unobserved, or latent, variables. Researchers in the social and behavioral sciences commonly encounter three general situations for testing such structural equation models—strictly confirmatory, testing alternative or competing hypothesized models, and model generation. However, none of these involve t…
Analyzing interaction patterns and content dynamics in an online mathematics discussion board
Interactive digital platforms have transformed mathematics education by offering students novel opportunities for collaborative engagement with peers and instructors in constructing knowledge. Drawing on the premise that meaningful interpersonal interactions are pivotal to effective learning within discussion boards, this study focuses on the nature of interactions within an online mathematics discussion board. We collected a dataset encompassing…
Evaluating the Performance of a Regularized Differential Item Functioning Method for Testlet-Based Polytomous Items
This study investigated the effect of testlets on regularization-based differential item functioning (DIF) detection in polytomous items, focusing on the generalized partial credit model with lasso penalization (GPCMlasso) DIF method. Five factors were manipulated: sample size, magnitude of testlet effect, magnitude of DIF, number of DIF items, and type of DIF-inducing covariates. Model performance was evaluated using false-positive rate (FPR) an…
Teacher strategies to use virtual learning environments to facilitate algebra learning during school closures
After nationwide school closures due to COVID-19, virtual learning environments (VLE) have seen tremendous increase in usage. The current study identified teacher activities for orchestration using an Algebra VLE during school closures, and whether these activities were related to student achievement. In May 2020, we collected survey data on how 213 teachers were using a VLE for Algebra with 10,590 students, along with system logs and student ach…
Using fair AI to predict students’ math learning outcomes in an online platform
As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational contexts has received insufficient attention, which can increase educational inequality. This study a…
Enhancing the Detection of Social Desirability Bias Using Machine Learning
Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect…
Unreliable Continuous Treatment Indicators in Propensity Score Analysis
Propensity score analyses (PSA) of continuous treatments often operationalize the treatment as a multi-indicator composite, and its composite reliability is unreported. Latent variables or factor scores accounting for this unreliability are seldom used as alternatives to composites. This study examines the effects of the unreliability of indicators of a latent treatment in PSA using the generalized propensity score (GPS). A Monte Carlo simulation…
Pedagogical discourse markers in online algebra learning
Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification
Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published st…
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
The relationship between self-regulated student use of a virtual learning environment for algebra and student achievement
Building socially responsible conversational agents using big data to support online learning
A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention …
A Comparison of Person-Fit Indices to Detect Social Desirability Bias
Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC)…
Assessing Ability Recovery of the Sequential IRT Model With Unstructured Multiple-Attempt Data
The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of data set as a whole in the practice of educational measurement. It is critical that methods be availabl…
Semisupervised Learning Method to Adjust Biased Item Difficulty Estimates Caused by Nonignorable Missingness in a Virtual Learning Environment
In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log file data, and this is expected to negatively affect IRT item p…
Multilevel Mixture Modeling with Propensity Score Weights for Quasi-Experimental Evaluation of Virtual Learning Environments
With the growing use of virtual learning environments (VLE), innovative methods to evaluate their performance are increasingly needed. A key difficulty in evaluating VLE using system logs is the large heterogeneity of usage patterns. The current study demonstrates an approach to classify complex patterns of student-level and classroom-level usage with latent class analysis, then estimate average treatment effects (ATEs) of membership in student o…
Construct validation of an innovative observational child assessment system
Exploring student and teacher usage patterns associated with student attrition in an open educational resource-supported online learning platform
A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development
This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and rela…
Assessing Change in Latent Skills Across Time With Longitudinal Cognitive Diagnosis Modeling
Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor attribute stability of individuals and to account for respondent dependence. Models based on combining late…
The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte Carlo simulation study was performed manipulating effect sizes, cluster sizes, number of clusters, intr…
An Evaluation of Weighting Methods Based on Propensity Scores to Reduce Selection Bias in Multilevel Observational Studies
Observational studies of multilevel data to estimate treatment effects must consider both the nonrandom treatment assignment mechanism and the clustered structure of the data. We present an approach for implementation of four propensity score (PS) methods with multilevel data involving creation of weights and three types of weight scaling (normalized, cluster-normalized and effective), followed by estimation of multilevel models with the multilev…
The Consequences of Ignoring Variability in Measurement Occasions Within Data Collection Waves in Latent Growth Models
In longitudinal data collection, it is common that each wave of collection spans several months. However, researchers using latent growth models commonly ignore variability in data collection occasions within a wave. In this study, we investigated the consequences of ignoring within-wave variability in measurement occasions using a Monte Carlo simulation and an empirical study. The results of the simulation study showed that ignoring heterogeneit…
A Reliability Generalization Study of the Marlowe-Crowne Social Desirability Scale
A reliability generalization (RG) study was conducted for the Marlowe-Crowne Social Desirability Scale (MCSDS). The MCSDS is the most commonly used tool designed to assess social desirability bias (SDB). Several short forms, consisting of items from the original 33-item version, are in use by researchers investigating the potential for SDB in responses to other scales. These forms have been used to measure a wide array of populations. Using a mix…
A Reliability Generalization Study of the Marlowe-Crowne Social Desirability Scale
A reliability generalization (RG) study was conducted for the Marlowe-Crowne Social Desirability Scale (MCSDS). The MCSDS is the most commonly used tool designed to assess social desirability bias (SDB). Several short forms, consisting of items from the original 33-item version, are in use by researchers investigating the potential for SDB in responses to other scales. These forms have been used to measure a wide array of populations. Using a mix…
Validation of Scores on the Marlowe-Crowne Social Desirability Scale and the Balanced Inventory of Desirable Responding
The Marlowe-Crowne Social Desirability Scale (MCSDS), the most commonly used social desirability bias (SDB) assessment, conceptualizes SDB as an individual’s need for approval. The Balanced Inventory of Desirable Responding (BIDR) measures SDB as two separate constructs: impression management and self-deception. Scores on SDB scales are commonly used to validate other measures although insufficiently validated themselves. This study used college …
Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm
This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and traditionally used methods of item selection is also presented. It is shown that the ACO algorithm o…
The Internal Structure of Positive and Negative Affect
This study tested five confirmatory factor analytic (CFA) models of the Positive Affect Negative Affect Schedule (PANAS) to provide validity evidence based on its internal structure. A sample of 223 club sport athletes indicated their emotions during the past week. Results revealed that an orthogonal two-factor CFA model, specifying error correlations according to Zevon and Tellegen’s mood content categories, provided the best fit to our data. In…
Attempted Validation of the Scores of the Vark
The authors examined the dimensionality of the VARK learning styles inventory. The VARK measures four perceptual preferences: visual (V), aural (A), read/write (R), and kinesthetic (K). VARK questions can be viewed as testlets because respondents can select multiple items within a question. The correlations between items within testlets are a type of method effect. Four multitrait—multimethod confirmatory factor analysis models were compared to e…
Detecting Social Desirability Bias Using Factor Mixture Models
Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide biased responses, which items elicit the most socially desirable responses, and which external variables p…
Can a perceptual peer deviance measure accurately measure a peer's self-reported deviance
Literacy-related school readiness skills of English language learners in Head Start
The purpose of this study is to examine the effects of Head Start on early literacy skills relevant to school readiness of English language learners compared to their peers. The comparisons of literacy outcomes were conducted between English language learners and non-English language learners when both groups participated and were not in Head Start. A total of 47 covariates were involved in propensity score analysis, and average treatment effects…
The Consequences of Ignoring Variability in Measurement Occasions Within Data Collection Waves in Latent Growth Models
In longitudinal data collection, it is common that each wave of collection spans several months. However, researchers using latent growth models commonly ignore variability in data collection occasions within a wave. In this study, we investigated the consequences of ignoring within-wave variability in measurement occasions using a Monte Carlo simulation and an empirical study. The results of the simulation study showed that ignoring heterogeneit…
An Evaluation of Weighting Methods Based on Propensity Scores to Reduce Selection Bias in Multilevel Observational Studies
Observational studies of multilevel data to estimate treatment effects must consider both the nonrandom treatment assignment mechanism and the clustered structure of the data. We present an approach for implementation of four propensity score (PS) methods with multilevel data involving creation of weights and three types of weight scaling (normalized, cluster-normalized and effective), followed by estimation of multilevel models with the multilev…
The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte Carlo simulation study was performed manipulating effect sizes, cluster sizes, number of clusters, intr…
Assessing Change in Latent Skills Across Time With Longitudinal Cognitive Diagnosis Modeling
Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor attribute stability of individuals and to account for respondent dependence. Models based on combining late…
Exploring student and teacher usage patterns associated with student attrition in an open educational resource-supported online learning platform
A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development
This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and rela…
Multilevel Mixture Modeling with Propensity Score Weights for Quasi-Experimental Evaluation of Virtual Learning Environments
With the growing use of virtual learning environments (VLE), innovative methods to evaluate their performance are increasingly needed. A key difficulty in evaluating VLE using system logs is the large heterogeneity of usage patterns. The current study demonstrates an approach to classify complex patterns of student-level and classroom-level usage with latent class analysis, then estimate average treatment effects (ATEs) of membership in student o…
Construct validation of an innovative observational child assessment system
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
The relationship between self-regulated student use of a virtual learning environment for algebra and student achievement
Building socially responsible conversational agents using big data to support online learning
A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention …
A Comparison of Person-Fit Indices to Detect Social Desirability Bias
Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC)…
Assessing Ability Recovery of the Sequential IRT Model With Unstructured Multiple-Attempt Data
The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of data set as a whole in the practice of educational measurement. It is critical that methods be availabl…
Semisupervised Learning Method to Adjust Biased Item Difficulty Estimates Caused by Nonignorable Missingness in a Virtual Learning Environment
In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log file data, and this is expected to negatively affect IRT item p…
Pedagogical discourse markers in online algebra learning
Mathematics (23 obras) · Computer Science (22 obras) · Psychology (21 obras) · Statistics (20 obras) · Econometrics (11 obras) · Psychometric Methodologies and Testing (11 obras) · Machine learning (10 obras) · Structural equation modeling (10 obras) · Artificial Intelligence (8 obras) · Online Learning and Analytics (8 obras)