Performance Analysis of PREDICTING STUDENT SATISFACTION AND QUALITY MANAGEMENT
Implementation Plan:
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Step 1: Initially we collect the dataset for remote learning students in South East Asia.
Step 2: Next, we preprocess the data which includes the following steps. Noise filtering, Removal of stop words, Tokenization, Lemmatization, and Stemming.
Step 3: Then, we implement feature selection using the Minimum Redundancy Maximum Relevance-based Whale Optimization Algorithm (MRMR-WOA).
Step 4: Next, we perform a feature extraction process using the CONV-LSTM (Long Short-Term Memory and Convolutional Neural Networks).
Step 5: Then, we develop the prediction model using RFC-IQPSO (Random Forest classifier-based improved
quantum particle swarm optimization).
Step 6: Next, we implement Strategies for Enhancing Remote Learning in Southeast Asia.
Step 7: Finally, we plot graph for the following metrics:
7.1: True positive rate vs. False positive rate
7.2: F-measure (%) vs. Number of Epochs
7.3: Recall (%) vs. Number of Epochs
7.4: Accuracy (%) vs. Number of Epochs
7.5: Precision (%) vs. Number of Epochs
Software Requirement:
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1. Development Tool: Python – 3.11.4 or Above version
2. Operating System: Windows 10 (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) If the above plan satisfies your requirement please confirm with us.
4) We develop simulation based projects only, not in real time.
We implement an existing project Reference 5: Title:- Prediction of students’ early dropout based on their interaction logs in online learning environment
