# Academic Teaching Quality Framework and Performance Evaluation Using Machine Learning
## 6 Results and Discussion  
This section presents the testing results for the machine learning algorithm used on the dataset. As mentioned in the previous section, the dataset consists of teacher evaluation records based on their academic, technical, and examination criteria. The popular machine-learning python libraries pandas [44] for data analysis and statistics, as well as Scikit-learn [45] to train and test the models are used in Python for the ML mode. All models were executed using the Google Colaboratory (Colab) machine learning tool. Google Colaboratory is particularly useful for data science and machine learning applications, as it provides access to powerful computing resources and libraries. One of the critical benefits of Colab is that it allows that we can run code in the cloud, meaning  
Figure 5: Correlation graphs—dependent and independent variable selection. (**a**) Academic criteria correlation. (**b**) Technical criteria correlation. (**c**) Examination criteria correlation. we do not need to have a powerful computer or install any software on a local machine. Furthermore, using Google Colab notebooks in the teaching and learning process can help students become familiar with programming ideas [46]. To calculate performance, multiple regression analysis is done on the QTEF data presented in Tables 2-4 in the ratio of 65:35, i.e., training (260 records) and testing (140 records) dataset. Keeping view of the least number of dataset records, the goodness of fit is determined by the coefficient of determination (R2). It is a statistic applied to statistical models whose main objective is to either predict future outcomes or test hypotheses using data from other relevant sources. Based on the percentage of the overall variation in outcomes that the model is responsible for explaining, it provides a gauge of how effectively observed results are mirrored by the model [40, 41]. R2 can be anywhere between 0.0 and 1.0. If the determination coefficient is 0, the value of the dependent variable, Y, cannot be predicted from the value of the independent variable, X. As a result, it can be concluded that there is no linear relationship between the variables, and a horizontal line provides the best fit. The median of all Y values is where the line crosses. All points fall perfectly on the horizontal path without scattering if their value is 1.0. An exact prediction of Y can be made if X is known. The R2 value for the multiple regression results in Table 7 based on highly correlated variables from Figure 2 is achieved as 0.9322, which shows that predictor variables have 93.22% accuracy. Causation is not necessarily implied by correlation. Although a significant connection would suggest causation, there may be alternative reasons. The factors may appear related, but there may be no real underlying relationship. Therefore, it could be the outcome of random chance [47]. As a result, we have replaced TC-14 with TC-15 and EC-5 to EC-3 as Scenario-1, which increases the accuracy to 95.35%. In addition, we have found that the replacement of examination criteria to EC-1 from EC-3 and technical from TC-4 to TC-7 in the training model increases the best possible accuracy of 95.77%. Additionally, the Mean Square Error has decreased from 20.12 to 13.78 and finally reduced to 12.56, measuring the squared average difference between the actual and anticipated data. The findings from the testing vs. training dataset of the machine learning model using Python code are represented as Figure 6 and graphically as Figure 7. The results demonstrate that the projected and actual figures are sufficiently close to one another by correlating them. Our MLR model produces results nearly identical to the distribution of the actual outcomes, as was anticipated by the grades distribution given in Table 5. It is because of the best-selected indicators from our QTEF model. Nevertheless, a few exceptions may be found in Figure 6 due to the use of limited dataset records.