Welcome to bspam Shiny App!!
This is the interactive dashboard for bspam R package. bspam stands for `binomial log-normal speed-accuracy modeling.' Use of this app does not require any knowledge of R. All tasks can be completed interactively by following the directions provided under the menus/tabs.
bspam package has functions to fit the speed-accuracy psychometric model for count outcome data (Potgieter, Kamata & Kara, 2017; Kara, Kamata, Potgieter & Nese, 2020), where the accuracy is modeled by a binomial count latent variable model. For example, the use of this modeling technique allows model-based calibration and scoring for oral reading fluency (ORF) assessment data collected from reading passages.
bspam Shiny App has three main tabs: Data Preparation, Model Fitting, and Score Estimation.
Data Preparation
Data preparation allows users to prepare their data for the analyses. For demonstration purposes, data preparation tab provides access to several datasets available in the bspam package. Users can upload their datasets in various formats, including `rds` (R data serialization) and `csv (comma-separated values)`. Once the data are loaded to the app, the next step is assigning the relevant columns to required type of variables for fitting the model. This is done by using the dropdown selection menus. Users can explore their raw and prepared dataets under the relevant view tabs. A descriptive summary of the prepared dataset is also provided under the Summary Statistics tab.
Model Fitting
This page has the options for performing model fitting, namely, estimation of task parameters. Users can select the desired options for the estimation. All non-mandatory options are pre-selected as the default options as in the relevant bspam function.
Score Estimation
This page has the options for performing score estimation, namely, estimation of person parameters and model-based scores (in the scale of number of successful tasks per minute). Users can select the desired options for score estimation. All non-mandatory options are pre-selected as the default options as in the relevant bspam function.
References
Potgieter, C. J., Kamata, A., & Kara, Y. (2017). An EM algorithm for estimating an oral reading speed and accuracy model. https://arxiv.org/abs/1705.10446
Kara, Y., Kamata, A., Potgieter, C., & Nese, J. F. (2020). Estimating model-based oral reading fluency: A Bayesian approach. Educational and Psychological Measurement, 80(5), 847-869. https://doi.org/10.1177/0013164419900208
About the bspam Shiny App & bspam R package
This Shiny app and the bspam R package have been developed as part of the project entitled 'Developing Computational Tools for Model-Based Oral Reading Fluency Assessments', funded by Institute of Education Sciences, U.S. Department of Education through Grant R305D200038 to Southern Methodist University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. Please click here for detailed information about the funding.
Citation
bspam Shiny App:
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bspam R package:
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Resources
For a more detailed information about the bspam package, please see the GitHub page and package website .
The source code for the Shiny app is available on GitHub .
Copyright Statement
Copyright (C) 2022-2023 The ORF Project Team
The bspam package is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
The bspam package is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this package. If not, see http://www.gnu.org/licenses/. .