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For getting the semantic similarity between the student's descriptive answer and sample answer, the created model used more than 5000 pairs of sentences that are related to primary education level in Sri Lanka. To get a good result we have used the cosine similarity algorithm. The model used 70 % of dataset for the training process and 30% of the dataset for testing process. The model nearly achieved 86% of accuracy and the loss is 36% as shown in the “Fig. 2.”

Figure 2 For getting the semantic similarity between the student's descriptive answer and sample answer, the created model used more than 5000 pairs of sentences that are related to primary education level in Sri Lanka. To get a good result we have used the cosine similarity algorithm. The model used 70 % of dataset for the training process and 30% of the dataset for testing process. The model nearly achieved 86% of accuracy and the loss is 36% as shown in the “Fig. 2.”

Related Figures (5)

sLE I. GENERATING SCORES FOR DESCRIPTIVE ANSWERS As an example, if the submitted descriptive answering question contains 5 marks and the percentage of similarity is 60%. Then the actual score for the student answer is given as 3.75 out of 5 (5 * 0.75 = 3.75).
sLE I. GENERATING SCORES FOR DESCRIPTIVE ANSWERS As an example, if the submitted descriptive answering question contains 5 marks and the percentage of similarity is 60%. Then the actual score for the student answer is given as 3.75 out of 5 (5 * 0.75 = 3.75).
the percentage of the progress that student has achieved over time as shown in the “Fig. 1.b.” Fig. 1. ULof the app
the percentage of the progress that student has achieved over time as shown in the “Fig. 1.b.” Fig. 1. ULof the app
The dataset has created using collection of speech commands gathered from the students age between 8 — 10 years. The dataset has audio files in 5 different emotions as happy, sad, surprise, drowsiness and neutral in the form of .wav format. The feature extraction in speech emotion detection model has been done using MEL Spectrogram Frequency (mel), chroma, mfcc, contrast and tonnetz and model training was done using MLP classifier. The trained accuracy we have obtained was 78% and the accuracy can be further improved by scaling up the dataset collection. We plot he speech signal form “Fig. 4” and spectrogram “Fig. 5” to get the visual representation of the voice commands based on particular emotion. Fig. 4. Feature extracted speech signal form of a voice command with ‘happy' emotion
The dataset has created using collection of speech commands gathered from the students age between 8 — 10 years. The dataset has audio files in 5 different emotions as happy, sad, surprise, drowsiness and neutral in the form of .wav format. The feature extraction in speech emotion detection model has been done using MEL Spectrogram Frequency (mel), chroma, mfcc, contrast and tonnetz and model training was done using MLP classifier. The trained accuracy we have obtained was 78% and the accuracy can be further improved by scaling up the dataset collection. We plot he speech signal form “Fig. 4” and spectrogram “Fig. 5” to get the visual representation of the voice commands based on particular emotion. Fig. 4. Feature extracted speech signal form of a voice command with ‘happy' emotion
More than 1500 of facial expression images of the students in age group of 8 — 10 years old are gathered to create the dataset collection. The model has been trained using CNN in order to obtain a higher accuracy. The accuracy gained by training the CNN model with 80 epochs is 99% and loss is 2% as shown in the “Fig. 3.” The accuracy can be improved by feeding more training data to the neural network. By using the trained model, we are detecting 5 main emotions of students as happy, sad, surprise, drowsiness and neutral. Fig. 3. Loss and Accuracy graph of Face emotion detection model
More than 1500 of facial expression images of the students in age group of 8 — 10 years old are gathered to create the dataset collection. The model has been trained using CNN in order to obtain a higher accuracy. The accuracy gained by training the CNN model with 80 epochs is 99% and loss is 2% as shown in the “Fig. 3.” The accuracy can be improved by feeding more training data to the neural network. By using the trained model, we are detecting 5 main emotions of students as happy, sad, surprise, drowsiness and neutral. Fig. 3. Loss and Accuracy graph of Face emotion detection model
Fig. 5. Spectrogram of the voice command with 'happy' emotion
Fig. 5. Spectrogram of the voice command with 'happy' emotion
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