# Explainable AI in Education: Techniques and Qualitative Assessment
## 3 Experimental Setup
### OULA Dataset
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Objectives** & **Algorithms Were Utilized** & **XAI Technique(s)** & **Evaluation** \\ \hline \multirow{4}{*}{[15]} & Measure student performance using demographic, administrative, engagement, and intra-course outcome data & LR, RF, KNN, DT, SVM, MLP & SHAP & Both SVM and RF show good performance in most of the tasks/ Equally crucial for predictions in ensuring fairness and fostering trust in their adoption. \\ \hline \multirow{4}{*}{[33]} & Forecast student performance by examining how they engage with platforms & \multirow{2}{*}{Neuro-fuzzy network} & \multirow{2}{*}{Feature selection} & \multirow{2}{*}{Feature selection} & \multirow{2}{*}{Feature selection slightly reduced the neuro-fuzzy system performance.} \\ \cline{1-1} \cline{5-5}  & Predict student performance & DT, FURIA & & \multirow{2}{*}{Decision rules, Visualization Tools} & \multirow{2}{*}{Automatically produced multimodal explanations using a combination of text and graphics.} \\ \hline \multirow{4}{*}{[31]} & Study how online behavior and background information impact STEM dropout rates separately and together & LR, SVM, MLP, GB & SHAP & Achieved 90\% accuracy using GB. \\ \hline \multirow{4}{*}{[30]} & Identify and prevent student failure and dropout & KNN, SVM, DT, MLP, RF, Voting classifier & Feature selection & Ensemble Black Box model performs the best over white-box model. \\ \hline \multirow{4}{*}{[29]} & Identify at-risk students by examining learning activities more closely on a weekly basis, with an emphasis on output interpretability & SVM, DT, RF, Proposed model & LIME & \multirow{2}{*}{LIME} & Proposed model delivered the best performance compared with the baseline models when identifying at-risk students. \\ \hline \multirow{4}{*}{[13]} & Introduce an interpretable rule-based genetic programming classifier using student data from multiple sources & RF & \multirow{2}{*}{CN2 rule inducer algorithm} & A hybrid statistical fuzzy system identified students likely to fail by analyzing their performance in initial assessments. \\ \hline \multirow{4}{*}{[34]} & Measure student performance using daily activities & SVM, DT, RF, LR & Feature selection & Achieved 90.85\% accuracy using SVM. \\ \cline{1-1} \cline{5-5}  & Determine how demographics affect academic success & DT & Decision rules & Achieved 83.14\% accuracy. \\ \hline \multirow{4}{*}{[36]} & Identify academic performance by the web pages visited & C4.5 Tree, NB & None & Achieved 63.8\% accuracy using NB. \\ \hline \multirow{4}{*}{[37]} & Determining which students are most likely to be unsuccessful & LR, SVM, RF, LR, NB & Feature selection & Achieved 63.8\% accuracy using NB. \\ \cline{1-1} \cline{5-5}  & & & & \\ \end{tabular}
\end{table} 
Table 1: A comparative review of the studies that utilized the OULAD. Initially, we chose a subset of the entire dataset from the OULAD for our work ([[https://github.com/gogoladzetedo/Open_University_Analytics](https://github.com/gogoladzetedo/Open_University_Analytics)]([https://github.com/gogoladzetedo/Open_University_Analytics](https://github.com/gogoladzetedo/Open_University_Analytics)) (accessed on 2 September 2024)). Three modules--codes AAA, BBB, and CCC--had their data taken into consideration. Since the purpose of the data analysis was to estimate the passing rate of the students, we also concentrated on the student information. As a result, Table 2 summarizes the conduct of the students, which was achieved by selecting a subset of attributes and developing aggregated attributes. The initial classes \"Pass\" and \"Distinction\" were combined in order to create the \"Pass\" class. Similarly, the original dataset's \"Fail\" and \"Withdrawn\" classes were combined to create the \"Fail\" class. In order to properly preprocess the OULAD dataset, important steps were conducted to ensure data quality and analysis effectiveness. First, missing values were addressed by removing rows or columns with too much missing information.