Analyzing Students' Performance Dataset in Physics Education Research (SPHERE) using Machine Learning (ML)
Overview
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.
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK12 OK2026-04-25
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- ERROR13 OK, 1 ERROR2026-03-10
Documentation
- Help pages
- 1
- Examples that run
- 0%1 never run
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Repository
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/santosoph/sphereml on 2026-10-21.
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Code & Tests
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- RR 4.6.0 released · 2026-04-24
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- RR 4.5.0 released · 2025-04-11
- 0.1.02025-02-06
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-02-06
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.50
- Download size
- 180 KB
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