Qualitative Analysis for Visual Attention of Students by Using the Technology of ICT

Introduction

Multimedia plays a vital role in electronic learning (E-learning). These days, eLearning is mostly used to educate the people. Electronic Learning services can be used in almost any context of learning. Using eLearning, universities started different courses through distance learning. It is a non-traditional way to transfer knowledge to the students. The multimedia technique is used to deliver the information to the students in the eLearning setup. In E-learning, students are not directly interacting with the teachers (Farhan, Aslam, Jabbar, Khalid, & Kim, 2017). The teacher records video lectures in a multimedia environment, and then the recorded lecture is delivered to the students. When students take a lecture, the teacher is not present in front of them. Computers and multimedia devices are used for the interaction between students and teachers. A video camera setup is used to record the video lecture, whereas the student is not present (Farhan, Aslam, Jabbar, & Khalid, 2018).

Machine Learning is not a new concept. Modern technology has changed it into a new level. Now prediction and adaption of learning behaviors are possible using machine learning. Authors may now use algorithms and analytics on our LMS for a more efficient analysis of our data, then offer more significant targeted e-learning resources to the audience. Machine learning plays a vital role in the future of e-learning. It can be used in LMS and HS systems. Machine learning plays a significant role in e-learning is personalization. Efficient data analysis and automation are used to achieve it (Farhan, Jabbar, Aslam, Hammoudeh, et al., 2018). Machine learning in LMS enables access to user data, and e-learning skills can be improved by using that data. It can be integrated with the HR system for a better analysis of data efficiently. It is helpful to pinpoint and improvement in areas that are based on pre-set algorithms and systematic patterns. Using such algorithms, LMS can analyze learner’s past performance and integrate it with the latest information for the prediction of consequences. In this way, e-learners can improve their learning skills and their performance. All of this based on pre-defined criteria, such as administrative ideas, goals, and objectives (Tippett, 2016).

Incorporate sector's machine learning applications are used to improve the retention of employees and maintain the satisfaction level of them. It includes a combination of LMS, and HR. Analytical Predictions and evaluations permit to identify the patterns, i.e., sharp points in course dropouts or documentation gaps. Therefore, authors can interfere before it is too overdue to preserve our pinnacle expertise and make sure that everybody complies, saving the rate of getting to vet activity applicants and hire new employs. Machine Learning empowers us to facilitate the automatic customized web-based training paths for each corporate student. For instance, information from the HR framework and LMS is utilized to make an individualized arrangement for client benefit representatives (Clark & Mayer, 2016). That depends on their past execution, present place of employment obligations, and required abilities. The system additionally examines examples and patterns to naturally alter the corporate students' coursework to meet their preparation needs. Our organization must have the capacity to advance, and everything relies on a versatile online training framework. Machine learning that coordinates with our LMS and HR frameworks enables us to revise the current online training procedure rapidly. Refresh the calculation or criteria to convey refreshed web-based preparing content on a bigger scale. For instance, naturally convey current item information online assets properties to our remote deals group, not to our IT team (Rakoczi, 2017).

Numerous associations face the challenge of information deficiency. In particular, the information they can use to make noteworthy objectives and recognize designs that obstruct worker efficiency. Machine learning gives us an abundance of Big Data that authors can use to offer focused on online training suggestions. Each worker approaches web-based preparing materials that consider their preparation needs and learning objectives. A large amount of data mining can be automated using Machine Learning. Our workers can focus on different parts of the L&D procedure. For example, modules that focused on real-world application or development of online training activities. In this way, employees enable them to gain practical knowledge and clear their gaps in knowledge that were uncovered through data analysis (Farhan, Jabbar, Aslam, Ahmad, et al., 2018).