Performance Analysis of STEM Education in High School
Implementation plan:
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Step 1: Initially, we collect and pre-process the studentsurvey Dataset.
Step 2: Then, we implement the differential evaluation algorithm (DEA) for the feature selection process.
Step 3: Then, we implement random forest and artificial neutral network (RF-ANN) to predict student academic performance.
Step 4: Next, we implement Kirkpatrick’s 4 levels Training Evaluation Model to overcome issues in online teaching.
Step 5: Next, we implement multimedia network interpretation teaching using machine learning (MNIT-ML) to generate personalized and interactive learning experiences for students.
Step 6: Next, we implement a quality assurance (QA) framework personalized to STEM fields for designing and delivering engaging online courses.
Step 7: Next, we implement Utilize fuzzy logic to monitor student performance
Step 8: Finally, we plot graph for the following metrics:
8.1: Number of Epochs vs. prediction rate (%)
8.2: Number of Epochs vs. F1-score(%)
8.3: Number of Epochs vs. Precision(%)
8.4: Number of Epochs vs. Recall (%)
Software Requirement:
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1. Development Tool : Python – 3.11.4
2. Development OS : Windows-11 (64-bit)
Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) This project is only based on simulations. Not a real time project.
4) If the above plan satisfies your requirement please confirm with us.
