Student Engagement Prediction in Online Session

The individuals who make up the globe constantly advance into the future and improve both their personal lives and the conditions in which they live. One ‘s education is the basis of one ‘s knowledge. Humans' education has a significant impact on their behavior and IQ. Through the use of diverse pedagogical techniques, instructors always play a part in changing students' ways of thinking and developing their social and cognitive abilities. However, getting students to participate in an online class is still difficult. In this study, we created an intelligent predictive system that aids instructors in anticipating students' levels of interest based on the information they learn in an online session and in motivating them through regular feedback. The level of students' engagement is divided into three tiers based on their online session activities (Not engaged, passively engaged, and actively engaged). The given data was subjected to the application of Decision Trees (DT), Random Forest Classifiers (RF), Logistic Regression (LR), and Long Short-Term Memory Networks are among the numerous machine learning approaches (LSTM). According to performance measurements, LSTM is the most accurate machine learning algorithm. The instructors can get in touch with the students and inspire them by improving their teaching approaches based on the results the system produces

Detection of Face Recognition of Interviewee Using Transform Technique and Machinle Learning Algorithm

A crucial task in any firm is the hiring of new personnel. Virtual interviews have replaced face-to-face interviews as the norm since the Pandemic. Knowing the sincerity of the interviewee while applying to the company becomes a significant task in such a situation. The practice of manually comparing a candidate's face from many interview rounds to the actual candidate joining the organization is being used by interviewers. I want to automate this human process, using machine learning techniques to aid the interviewee's sincerity be established. Machine learning techniques will be used in this procedure to find and identify faces in pictures taken during the first round of interviews. Then compare it later to the real face that was photographed at the time of joining. If all of the visuals line up, it establishes the interviewee's sincerity. And if they don't match, management can take the necessary steps offline. This project will be conceived up and explored from the standpoint of how and whether Python may be used to implement

Machine Learning based Employee Attrition Predicting

Now a day’s variety of reasons for job resignations due to this, we have to take different types of measurements for prediction of job seekers. They have different reasons for not doing jobs well and fell like pressure. Many employees suddenly come to an end of their service without any reason. Techniques of machine learning have full-grown in fame in the middle of researchers in current years. It is accomplished of propose answer to a broad range of problems. Help of machine learning, you may produce prediction concerning staff abrasion. So machine learning model we will be using TCS employee attrition a genuine time dataset to train our model. The aim of this study is to at hand a comparison of different machine learning algorithms for predict which employees are probable to go away their society. We propose two methods to crack the dataset into train and test data: the 75 percent train 25 percent test split and the K Fold methods. Three techniques are three methods that we employ to train our model for correctness comparison, and we will compare the exactness of the models generate using these three Boosting Algorithms

Flipping pathology: our experience at an Australian medical school

Flipped classroom and active learning pedagogies are gaining popularity in institutions across the world. There are very few studies about their use in Australian medical schools, in particular the pre-clinical sciences. In this paper, we share our experience of adopting the flipped classroom approach in teaching pathology to second year medical students at the University of Queensland, School of Medicine. The overall response from our students was positive but we are also aware of the limitations and shortcomings. Our findings, particularly student perceptions, can guide other investigators in flipping their course