4. Applied the GLM on the same data, but this time, I have Standardized it and applied Step-AIC. 
Accuracy = 49.01%
5.  Random forest on the Standardized data (dropped activity column)
Accuracy = 60.71 %
When plotted the variable importance for the model, it showed that only 5 variables are more important.
Selected
Mouse movement,
time_diff, 
exercise,
mouse_click_left,
idle time. 
6.  RF_model_imp_variable s:
Variables used: mouse_movement + idle_time + time_diff + exercise + mouse_click_left
accu_val_rf_imp = 0.568
7. SVM model – standardized and without activity
Accuracy_svm_val = 0.5682113
8. Using Polynomial kernel, accuracy on validation
accu_val_svm_poly = Accuracy (pred_val_svm_poly, std_valid$grade)
#56.8245
9.Random forest with standardized and three classes, [dropped activity, student ID but session is present] (here only for students present for all sessions)
accuracy on the validation set -  0.4879224
accuracy on test = 49.75%