- Math Scores: Students whose parents have a master’s degree tend to have the highest average math scores. Those with parents who attended “some high school” or “high school” have the lowest average scores.
- Reading Scores: A similar trend is observed in reading scores. Students with parents holding master’s degrees outperform others. The scores generally increase with the increasing level of parental education.
- Writing Scores: This trend persists in writing scores as well. Students with parents having higher educational qualifications tend to score better on average
3. Modifying the Blueprint
Raw data, while rich, often isn’t ready for sophisticated algorithms. Our dataset underwent a transformation phase where categorical variables metamorphosed into a machine-friendly format through one-hot encoding. This encoding process converted categories into binary columns, laying down the foundation for the modeling phase.
4. Modeling: Crafting the Predictive Lens
With our data prepped, we ventured into the realm of predictive modeling. The goal? Predict math scores. The tool of choice? Linear regression.
Linear regression, a staple in the world of predictive analytics, helped us establish relationships between our predictors (gender, race, parental education, etc.) and our target, the math score. Post-training, our model was tested, revealing a Mean Squared Error (MSE) of approximately 24.84. This metric, representing the average squared difference between actual and predicted scores, indicated a decent model fit.
5. Assessment: Measuring the Crystal Ball’s Clarity
No model is complete without a rigorous assessment. We juxtaposed our model’s predictions against actual scores, visually inspecting the alignment. The results were promising. A majority of predictions snugly fit close to the actual values, testifying to the model’s efficacy.
Conclusion: The Power of SEMMA
Our odyssey through the SEMMA methodology, applied to student performance, reaffirmed the power of structured data analysis. From raw data to tangible insights, SEMMA ensured each stage was methodically addressed, yielding a model that shed light on the determinants of academic success.
In the dynamic world of education, such insights can drive policy changes, tailor educational interventions, and most importantly, help educators understand the kaleidoscope of factors influencing student performance.