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PathAnalysis

This is the official implementations of On adaptive learning paths using hidden Markov models(JKDAS, 2023.02). For technical details, please refer to:

On adaptive learning paths using hidden Markov models [DOI]
Hyunhee Choi, Yunji Lee, Hayun Lee
img

(1) Setup

This code has been tested with R 4.2.2, Python 3.8, hmmlearn 0.2.8, CUDA 11.2 and cuDNN 8.0 on Ubuntu 20.04.

  • Clone the repository
git clone https://github.com/LAIVDATA-EdTech-R-D/LearningPath-using-HMM.git
  • Setup dependencies
conda create -n PathAnalysis python=3.8
conda activcate PathAnalysis
deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/
sudo apt-get update
sudo apt-get install r-base
cd PathAnalysis

(2) AI-Hub dataset

AI-Hub(Mathematics learner's ability level measurement data) mathmatics dataset can be found here. We extracting the learner's log related with Addition & Subtraction. Data Format is:

Learner no. \ KC KC01 KC02 ... KC07
1 1 0 ... 1
2 1 0 ... 1
... ... ... ... ...
183 1 1 ... 1

(0: Incorrect, 1: Correct)

(3) Analysis

  • KC Relation Selection using LASSO & RF
Rscript Elasso_RF.R
  • Hidden Markov Model
    • HMM with KC Relation Selection
    python FeatureSelectionHMM.py
    • HMM without KC Relation Selection
    python OnlyHMM.py
  • Structural Equation Modeling
    • SEM
    Rscript PLSPM_SEM.R
    • SEM for every possible paths
    Rscript PLSPM_SEM_Every.R

(4) Result(Graph)

KC prerequisite graph(KC Map) Learning Path
graph concept