LOGANTree: Tree-Based Models for the Analysis of Log Files from
Computer-Based Assessments
Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, <https://www.oecd.org/pisa/>) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) <doi:10.1007/978-3-319-19977-1_13> Boehmke, B., & Greenwell, B. M. (2019) <doi:10.1201/9780367816377> .
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
ROCR, caret, caretEnsemble, dplyr, ggplot2, rpart.plot, tibble, gbm, stats |
| Published: |
2022-06-22 |
| Author: |
Denise Reis Costa [aut, ths],
Qi Qin [aut, cre] |
| Maintainer: |
Qi Qin <logantreeqq at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
LOGANTree results |
Documentation:
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