Education Learning Analytics Modeling

Education Learning Analytics Modeling

Goal

Turn education platform/log/behavior data into interpretable learning analytics models and paper-ready results.

Use After

Use after:

• education-learning-analytics-design
• education-sampling-data-management
• education-quantitative-data-cleaning
• education-descriptive-statistics
• education-advanced-quantitative-modeling

Do not expose the skill name to users. Present it as "学习行为数据建模" or "学习分析建模".

Inputs

• Cleaned log or learning behavior dataset
• Data dictionary
• Unit of analysis: student, session, event, item, assignment, time window
• Outcome: achievement, completion, engagement, dropout risk, mastery, writing improvement
• Time window and prediction target
• Privacy/anonymization constraints

Workflow

1. Define modeling objective:
   • descriptive behavior analysis
   • clustering/learner profiles
   • prediction/early warning
   • knowledge tracing/mastery estimation
   • sequence/pathway analysis
   • intervention/effect analysis
2. Define unit and time window:
   • event-level
   • session-level
   • student-week
   • student-course
   • item/knowledge component
3. Check data leakage:
   • features must occur before prediction target
   • no future outcome information in predictors
   • separate training/test by student or time where appropriate
4. Engineer features:
   • frequency/count
   • duration/time-on-task
   • recency
   • regularity
   • completion
   • revision behavior
   • help-seeking
   • AI prompt/feedback usage
   • assessment history
   • knowledge component performance
5. Choose model:
   • descriptive dashboards
   • k-means/hierarchical clustering/GMM/HDBSCAN
   • logistic/linear regression
   • random forest/gradient boosting
   • sequence models
   • Bayesian knowledge tracing/deep knowledge tracing when appropriate
6. Split/evaluate:
   • train/test split
   • cross-validation
   • time-based validation
   • student-level split
7. Evaluate metrics:
   • classification: accuracy, precision, recall, F1, AUC
   • regression: MAE, RMSE, R2
   • clustering: silhouette, stability, interpretability
   • knowledge tracing: AUC, calibration, mastery curve
8. Interpret model:
   • feature importance
   • SHAP/permutation importance
   • profile descriptions
   • educational meaning
9. Produce figures/tables and result narrative.

Tool Calls

Python

pip install pandas numpy scikit-learn statsmodels seaborn matplotlib shap xgboost lightgbm umap-learn hdbscan lifelines

Feature aggregation:

features = (
    logs.groupby("student_id")
    .agg(
        n_sessions=("session_id", "nunique"),
        n_events=("event_id", "count"),
        total_time=("duration_sec", "sum"),
        avg_score=("score", "mean"),
        n_ai_feedback=("ai_feedback_used", "sum")
    )
    .reset_index()
)

Prediction:

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, stratify=y, random_state=42)
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
pred = model.predict(X_test)
proba = model.predict_proba(X_test)[:, 1]
print(classification_report(y_test, pred))
print(roc_auc_score(y_test, proba))

Clustering:

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

X_scaled = StandardScaler().fit_transform(X)
km = KMeans(n_clusters=3, random_state=42).fit(X_scaled)
silhouette_score(X_scaled, km.labels_)

SHAP:

import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

R

install.packages(c("tidyverse", "tidymodels", "caret", "cluster", "factoextra", "TraMineR", "lme4"))

Feature aggregation:

features <- logs |>
  group_by(student_id) |>
  summarise(
    n_sessions = n_distinct(session_id),
    n_events = n(),
    total_time = sum(duration_sec, na.rm = TRUE),
    avg_score = mean(score, na.rm = TRUE),
    n_ai_feedback = sum(ai_feedback_used, na.rm = TRUE)
  )

Sequence analysis:

install.packages("TraMineR")
library(TraMineR)

Knowledge Tracing Tools

pyBKT: https://github.com/CAHLR/pyBKT
pyKT: https://github.com/pykt-team/pykt-toolkit
EduKTM: https://github.com/bigdata-ustc/EduKTM

Install examples:

pip install pyBKT

Use knowledge tracing only when data include repeated item/skill/knowledge-component interactions.

Output Format

1. Modeling Objective

2. Feature Table

3. Model Plan

4. Results Table

5. Learner Profile Table

6. Knowledge Tracing Table

7. Reporting Template

基于学习平台日志，本研究构建了 [特征数量] 个学习行为特征，涵盖学习频率、学习持续时间、任务完成、AI 反馈使用和测验表现等维度。模型以 [结果变量] 为预测目标，采用 [模型] 进行分析。结果显示，模型在测试集上的 [指标] 为 [数值]，其中 [重要特征] 对预测贡献较大。该结果表明 [教育解释]。

Education-Specific Feature Ideas

Quality Rules

• Prediction is not explanation unless interpreted carefully.
• Avoid leakage from future behavior or final outcomes.
• Split data at the student level when repeated events exist.
• Report class imbalance and baseline models.
• Prefer interpretable models for educational decision-making.
• Do not label students in stigmatizing ways; use supportive profile names.
• Preserve privacy for logs, prompts, writing samples, and account identifiers.

User-Facing Closure

End by asking for the modeling objective:

学习行为数据可以进入建模阶段。你更想先做哪类分析：A. 学习者画像/聚类，B. 成绩或风险预测，C. 知识掌握追踪，D. AI 使用行为与写作改进关系？如果你不确定，我会根据数据字段推荐。