More Like this

1. From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances

   https://doi.org/10.1177/1094428119887434  

   Lester, Houston F.; Cullen-Lester, Kristin L.; Walters, Ryan W. (January 2019, Organizational Research Methods)

   null (Ed.)

   Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research. 

   more » « less
2. Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior

   https://doi.org/10.1093/biomtc/ujaf053  

   Zeng, Zijian; Li, Meng; Vannucci, Marina (May 2025, Biometrics)

   ABSTRACT Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a 3-dimensional array with elements that can be grouped according to 2 directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a full Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates. 

   more » « less
3. Characterising Individual-Level Collaborative Learning Behaviours Using Ordered Network Analysis and Wearable Sensors

   Yan, L; Swiecki, Z; Gašević, D; Zhao; L; Li, X; Martinez-Maldonado; R; Tan; Y; et al (October 2023, Springer, Cham)

   Irgens, G; Knight, S (Ed.)

   Wearable positioning sensors are enabling unprecedented opportunities to model students’ procedural and social behaviours during collaborative learning tasks in physical learning spaces. Emerging work in this area has mainly focused on modelling group-level interactions from low-level x-y positioning data. Yet, little work has utilised such data to automatically identify individual-level differences among students working in co-located groups in terms of procedural and social aspects such as task prioritisation and collaboration dynamics, respectively. To address this gap, this study characterised key differences among 124 students’ procedural and social behaviours according to their perceived stress, collaboration, and task satisfaction during a complex group task using wearable positioning sensors and ordered networked analysis. The results revealed that students who demonstrated more collaborative behaviours were associated with lower stress and higher collaboration satisfaction. Interestingly, students who worked individually on the primary and secondary learning tasks reported lower and higher task satisfaction, respectively. These findings can deepen our understanding of students’ individual-level behaviours and experiences while learning in groups. 

   more » « less
4. Multimodal Modeling of Coordination and Coregulation Patterns in Speech Rate during Triadic Collaborative Problem Solving

   https://doi.org/10.1145/3242969.3242989  

   Stewart, Angela E.B.; Keirn, Zachary A.; D'Mello, Sidney K. (January 2018, The 21st ACM International Conference on Multimodal Interaction)

   We model coordination and coregulation patterns in 33 triads engaged in collaboratively solving a challenging computer programming task for approximately 20 minutes. Our goal is to prospectively model speech rate (words/sec) – an important signal of turn taking and active participation – of one teammate (A or B or C) from time lagged nonverbal signals (speech rate and acoustic-prosodic features) of the other two (i.e., A + B → C; A + C → B; B + C → A) and task-related context features. We trained feed-forward neural networks (FFNNs) and long short- term memory recurrent neural networks (LSTMs) using group- level nested cross-validation. LSTMs outperformed FFNNs and a chance baseline and could predict speech rate up to 6s into the future. A multimodal combination of speech rate, acoustic- prosodic, and task context features outperformed unimodal and bimodal signals. The extent to which the models could predict an individual’s speech rate was positively related to that individual’s scores on a subsequent posttest, suggesting a link between coordination/coregulation and collaborative learning outcomes. We discuss applications of the models for real-time systems that monitor the collaborative process and intervene to promote positive collaborative outcomes. 

   more » « less
5. Modeling Player Engagement with Bayesian Hierarchical Models

   Sawyer, R.; Rowe, J.; Azevedo, R.; Lester, J. (January 2018, Proceedings of the Fourteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts. 

   more » « less