GradeForecast Home Intro Data Training Future Conclusion
EXPLORING THE SCIENCE OF
Score prediction
Research on creating a model that predicts
scores
By Dylan , Nuaym and
Joshua
GradeForecast Home Intro Data Training Future Conclusion
Intro
C O N T E N T S:
• What is it and Why?
• Components of the program
• Intro on Regression Models
• How would it Function?
GradeForecast Home Intro Data Training Future Conclusion
What is it and why?
GradeForecast is a tool that predicts
Final exam scores based on their
performance in Unit Test and Midterms
This program would aid teachers in
accurately predicting a suitable
score to assign to the student for
college applications
•User Interface: The front-end design that allows users to interact with the system.
•Outcome: The predicted final exam score based on inputs.
GradeForecast Home Intro Data Training Future Conclusion
Components of the program
• Data: Historical scores (unit tests,
midterm) and final exam results.
• Machine Learning Model: The predictive
model that learns from the data.
• User Interface: The front-end design that
allows users to interact with the system.
• Outcome: The predicted final exam score
based on inputs.
•User Interface: The front-end design that allows users to interact with the system.
•Outcome: The predicted final exam score based on inputs.
GradeForecast Home Intro Data Training Future Conclusion
What is a Regression model?
A regression model is a type of statistical method used to
predict a continuous outcome (such as final exam scores) based
on one or more input variables (like unit test and midterm
scores). In our case, the model would take the scores of the
unit tests and the midterms to predict the final exam score.
The model analyzes the relationship between the input
variables and the outcome. For example, if a student performs
well on unit tests and the midterm, the model predicts a
higher final score. Linear regression is a basic form, but
other models can be used for more complex relationships.
GradeForecast Home Intro Data Training Future Conclusion
How does it Work?
First, the user inputs their scores from the unit tests and
midterm exam. The program then processes these inputs using a
regression model, which has been trained on historical data to
identify patterns in how performance on earlier assessments
correlates with final exam scores. The model analyzes these
patterns and uses them to predict the student’s final exam
score, providing an estimate based on their past performance.
This prediction helps the teacher gauge the student's
potential final exam outcome
GradeForecast Home Intro Data Training Future Conclusion
DATA
C O N T E N T S:
• What data do we need?
• How is it Used?
• Structure of data
•Feeding data to the AI
GradeForecast Home Intro Data Training Future Conclusion
What data do we need?
01 Historical Student Score
02 Clean, Formatted Data
03 Training and Testing Data
GradeForecast Home Intro Data Training Future Conclusion
How is it Used?
The data is first cleaned and preprocessed, which includes
handling missing values and normalizing the test scores to
ensure consistency. It’s then split into a training set, where
the model learns the relationship between the unit test,
midterm scores, and the corresponding final exam outcomes. The
trained model then uses this pattern to predict final exam
scores from new input data. This allows the program to provide
accurate predictions, and the model can be refined over time
as more data becomes available, improving its performance.
GradeForecast Home Intro Data Training Future Conclusion
Structure of Data
The data is structured in a table with
three columns: Unit Test Score, Midterm
Score, and Final Exam Score. Each row
represents a student's scores, where
the unit test and midterm scores serve
as inputs, and the final exam score is
the output the model predicts. The
model learns the relationship between
these scores to make predictions for
new students.
GradeForecast Home Intro Data Training Future Conclusion
Feeding data to ai
The data is fed to the AI in the form of numerical inputs, where each
student's unit test and midterm scores are treated as features. During
training, the model processes multiple rows of this data, analyzing
the relationship between these input features and the final exam
score, which serves as the target output. Once trained, the AI uses
this learned pattern to predict final exam scores by accepting new
unit test and midterm inputs. The dataset is also divided into
Training and Testing data where Training is used to train the model
whereas testing is used to test the accuracy of the model, majority of
the dataset should be allotted to training and the rest for testing.
GradeForecast Home Intro Data Training Future Conclusion
TRAINING
C O N T E N T S:
• Collection and preprocessing of data
• How is the model trained?
• Testing the model
• Model Refinement
GradeForecast Home Intro Data Training Future Conclusion
Collection and preprocessing of data
Data collection and preprocessing are essential for training a
predictive model. The data, gathered from sources like
academic records or learning systems, includes unit test,
midterm, and final exam scores, organized in a table with rows
representing students. Preprocessing involves handling missing
values, removing outliers, and scaling features to ensure
consistency. Categorical data, if any, is converted to
numerical form. The data is then split into a training set for
model learning and a test set for evaluation, ensuring the
model can make accurate predictions on new inputs.
GradeForecast Home Intro Data Training Future Conclusion
How is the model trained?
The model is trained by feeding it unit test and midterm
scores as inputs and final exam scores as the target output.
Using a regression algorithm, it identifies patterns and
adjusts its internal parameters to minimize prediction errors,
guided by a loss function. Through iterative optimization,
such as gradient descent, the model improves its accuracy over
time. Once trained, it’s tested on unseen data to ensure it
can generalize and make reliable predictions for new inputs.
GradeForecast Home Intro Data Training Future Conclusion
TESTING THE MODEL
Testing the model involves using a test dataset with unit test
and midterm scores, while final scores are hidden. The model
predicts the final scores, and these predictions are compared
to the actual results using metrics like Mean Squared Error
(MSE) or R-squared. If the model performs well, it shows it
has learned the relationship between inputs and outputs. Poor
results may require retraining, fine-tuning, or better data
preparation. This process ensures the model can make accurate
and reliable predictions.
GradeForecast Home Intro Data Training Future Conclusion
Model Refinement
Model refinement involves improving the model’s performance when
its predictions are not accurate enough. This can include
retraining the model with more or better-quality data, adjusting
hyperparameters like the learning rate or model complexity, or
applying more advanced techniques such as regularization to prevent
overfitting. Additionally, refining the feature set by adding or
removing variables can help the model better capture relevant
patterns. After each refinement, the model is re-evaluated on the
test data to ensure its accuracy improves. This iterative process
continues until the model consistently delivers reliable and
accurate predictions.
GradeForecast Home Intro Data Training Future Conclusion
Future Improvements
C O N T E N T S:
• Enhanced Data Integration
• Model Complexity
• User Experience
• Adaptation
GradeForecast Home Intro Data Training Future Conclusion
ENHANCED DATA INTEGRATION
Enhanced data integration involves expanding the model by
incorporating additional data sources, such as attendance
records, homework scores, and participation levels. By
including these factors, the model can better understand the
broader context of student performance and make more accurate
predictions. This integration would allow the model to account
for variables that influence learning outcomes, providing a
more holistic view of each student's academic progress and
potentially improving the overall prediction of final exam
scores.
GradeForecast Home Intro Data Training Future Conclusion
MODEL COMPLEXITY
Increasing model complexity involves moving beyond basic
regression techniques to more advanced algorithms, such as
neural networks or ensemble methods. These models can capture
intricate, non-linear relationships between input features and
final scores, improving prediction accuracy. By leveraging
more sophisticated methods, the model can better handle
diverse data patterns, making it more adaptable and effective
in predicting outcomes in complex academic scenarios. This
added complexity allows for a deeper understanding of how
various factors interact to influence student performance.
GradeForecast Home Intro Data Training Future Conclusion
USER EXPERIENCE
Improving user experience focuses on creating a more intuitive
and accessible interface for educators, students, and
administrators. This can include clear visualizations, easy-
to-understand predictions, and interactive features that allow
users to input data seamlessly. A well-designed interface
enhances user engagement and makes it easier to interpret
results, helping users make informed decisions based on the
model’s predictions. By prioritizing simplicity and usability,
the tool becomes more effective and approachable for a broader
audience.
GradeForecast Home Intro Data Training Future Conclusion
ADAPTATION
Continuous learning and adaptation involve creating a feedback
loop where the model can learn from new data over time. As
real final exam scores are recorded, they can be fed back into
the model, allowing it to refine its predictions and adapt to
changes in student performance patterns. This ongoing learning
process ensures the model remains relevant and accurate,
improving its predictions as more data becomes available and
academic trends evolve. It helps the model stay up-to-date and
effective in dynamic educational environments.
GradeForecast Home Intro Data Training Future Conclusion
CONCLUSION
In conclusion, this predictive model offers a valuable tool for forecasting
final exam scores based on unit test and midterm results. By utilizing
advanced regression techniques, it can provide accurate predictions to help
students, educators, and administrators make more informed decisions. With
future improvements like enhanced data integration, increased model
complexity, better user experiences, and continuous learning, the model’s
accuracy and utility can be further expanded. As it evolves, it has the
potential to become an essential resource in educational settings, offering
deeper insights into student performance and supporting more personalized
learning pathways.
GradeForecast Home Intro Data Training Future Conclusion
THANK YOU
