Building a Robust Metric to Predict Star Faculty

Comprehensive Strategies for Identifying Top 10% Faculty Using Academic Analytics Data

Key Takeaways

Non-ML Approaches Provide Transparency: Descriptive analytics and rule-based methods offer clear, actionable insights without the complexity of machine learning.
Enhanced Feature Engineering is Crucial for ML: Transforming raw data into meaningful features such as rolling averages and growth rates can significantly improve predictive models.
Hybrid Solutions Balance Simplicity and Accuracy: Combining descriptive methods with advanced ML techniques ensures both interpretability and predictive power for executive decision-making.

Introduction

Identifying "star faculty," defined as those in the top 10% of the Scholarly Research Index (SRI), is a critical task for academic institutions aiming to recognize and support their most impactful scholars. Utilizing Academic Analytics (AA) data, institutions can develop metrics to predict which faculty members are likely to achieve star status. This comprehensive guide explores both non-machine learning (ML) and ML-based approaches, offering strategies to provide executives with reliable predictions despite initial challenges with model performance.

Non-Machine Learning Approaches: Descriptive Analytics

1. Defining Key Metrics

Start by identifying the most relevant metrics from AA data that correlate with star faculty status. Key indicators typically include:

Research Output: Number of publications, citations, and the h-index.
Grant Funding: Total funding received and the number of grants secured.
Collaboration Metrics: Co-authorship networks and interdisciplinary collaborations.
Teaching Metrics: Student evaluations and course load.
Awards and Honors: Prestigious awards and fellowships received.

2. Calculating Percentiles

For each identified metric, calculate the percentile rank of faculty members. This helps in determining which metrics are most strongly associated with the top 10% SRI.

3. Creating a Composite Score

Combine the percentile ranks of key metrics into a single composite score. Assign weights to each metric based on its importance. For example:

Research output: 40%
Grants: 30%
Awards: 20%
Teaching: 10%

Sum the weighted percentiles to create a final score for each faculty member.

4. Threshold-Based Classification

Set a threshold for the composite score that corresponds to the top 10% SRI. Faculty members scoring above this threshold are classified as "star faculty."

5. Visualizing Insights

Create dashboards or reports to visualize the data. Key visualizations include:

Percentage of faculty with specific metrics who achieved star status.
Trends over time, such as rolling averages of key metrics.
Comparisons across departments or institutions.

Advantages of Non-ML Approaches

Simple and easily interpretable for executives.
No need for complex ML models or high AUC scores.
Focuses on actionable insights rather than predictions.

Machine Learning-Based Approaches: Predictive Analytics

1. Feature Engineering

Transform raw AA data into meaningful features to enhance the predictive power of ML models:

Rolling Averages: Calculate rolling averages of publications, citations, and grants over a specified period (e.g., 3-year rolling averages).
Growth Rates: Compute year-over-year growth rates for key metrics.
Collaboration Networks: Quantify collaboration strength through network analysis metrics like centrality measures.
Time Series Features: Extract trends, seasonality, and volatility from time-series data.

2. Feature Selection

Identify the most predictive features using:

Statistical methods such as correlation analysis and mutual information.
ML techniques like SHAP values and feature importance from tree-based models.

3. Model Selection

Choose appropriate ML models based on the need for interpretability and predictive accuracy:

Interpretable Models: Logistic regression or decision trees.
Advanced Models: Ensemble methods like Random Forest, Gradient Boosting (e.g., XGBoost, LightGBM), or deep learning models if interpretability is less critical.

4. Addressing Class Imbalance

Since star faculty are a small subset, use techniques to handle class imbalance:

Oversampling methods such as SMOTE.
Undersampling the majority class.
Applying class weighting to give more importance to the minority class.

5. Model Evaluation

Evaluate model performance using metrics like:

Precision, Recall, F1-Score.
AUC-ROC to assess the trade-off between true positive rate and false positive rate.

Focus on precision if false positives are particularly costly.

6. Deployment and Monitoring

Deploy the model as a dashboard or API accessible to executives. Continuously monitor performance and retrain the model with updated data to maintain accuracy.

Advantages of ML-Based Approaches

Provides predictive power for identifying future star faculty.
Can uncover complex, non-linear relationships in the data.

Hybrid Solutions: Combining Descriptive and Predictive Analytics

To balance simplicity and accuracy, implement both descriptive and ML-based approaches in parallel. This hybrid solution leverages the strengths of each method:

Establish a Baseline: Use descriptive analytics to create a rule-based prediction model based on key metrics.
Develop an Advanced Model: Simultaneously build an ML model with enhanced feature engineering.
Compare and Integrate: Compare the predictions from both models and present them alongside confidence intervals to executives.

This approach ensures that executives have clear, actionable insights while also benefiting from the nuanced predictions of advanced models.

Key Considerations for Hybrid Solutions

Maintain interpretability to ensure executives understand the basis of predictions.
Use clear confidence metrics to communicate the reliability of predictions.
Account for department or field-specific variations by normalizing metrics accordingly.
Incorporate career stage indicators, such as years since PhD or tenure status, to refine predictions.

Implementing the Recommended Approach

Step 1: Data Preparation

Ensure that AA data is clean and comprehensive. This involves handling missing values, normalizing data across different departments, and transforming metrics into suitable formats for analysis.

Step 2: Feature Engineering and Selection

Create derived metrics such as rolling averages and growth rates. Use statistical and ML techniques to select the most predictive features for the ML model.

Step 3: Building Descriptive Models

Develop rule-based models using key metrics to classify faculty members. These models provide a transparent baseline that executives can easily understand.

Step 4: Developing ML Models

Train ML models using the engineered features. Address class imbalance and evaluate model performance using appropriate metrics. Ensure that models are interpretable by incorporating techniques like SHAP values.

Step 5: Deployment and Integration

Deploy both descriptive and ML models via dashboards or APIs. Provide executives with tools to access and interpret the predictions effectively.

Step 6: Continuous Monitoring and Refinement

Continuously monitor the performance of both models. Retrain ML models with new data and adjust descriptive rules as necessary to maintain accuracy and relevance.

Conclusion

Building a metric to predict star faculty requires a balanced approach that leverages both descriptive analytics and machine learning. While non-ML methods offer transparency and simplicity, enhanced feature engineering can significantly improve the predictive power of ML models. A hybrid solution that combines the strengths of both approaches ensures that executives receive accurate, actionable insights without sacrificing interpretability. By following the structured steps outlined above, institutions can develop a robust framework for identifying and supporting their top-performing faculty members.