A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated.
Version: 0.1.1 Depends: R (≥ 3.50) Imports: shiny, shinydashboard, spheredata, lavaan, semPlot, CTT, mirt, shinycssloaders, FSelectorRcpp, randomForest, caret, caTools, pROC, GA, readxl Published: 2025-05-09 DOI: 10.32614/CRAN.package.sphereML Author: Purwoko Haryadi Santoso [aut, cre], Edi Istiyono [ctb], Haryanto Haryanto [ctb] Maintainer: Purwoko Haryadi Santoso <purwokoharyadisantoso at unsulbar.ac.id> BugReports: https://github.com/santosoph/sphereML/issues License: MIT + file LICENSE URL: https://github.com/santosoph/sphereML NeedsCompilation: no CRAN checks: sphereML results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sphereML to link to this page.
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