Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

An exploratory study to compare the effects of immersive virtual reality based training on the learning motivation of final year medical students as compared to video and text-based learning. Different modes of delivery of a training simulation of laparoscopy operation were presented to students and learning motivation corresponding to which were evaluated using the Intrinsic Motivation Inventory. The study was conducted from September 2018 to May 2019. Undergraduate medical students from 8 medical colleges and universities across Punjab, Pakistan participated in this study. A total of 87 students with a mean age of 22.5 ± 4 years were recruited for the study. Of these, 57.4% (n = 50) were males and 42.6% (n = 37) were females. Paired sampled t-test was chosen for the statistical investigation for the study. The tests were conducted by comparing means of text, video, and virtual reality learning methodologies in medical students. All executed statistical models are having significance value P=.000. Therefore, results are generalizable and can be implemented across the population. Medical student motivation was observed to be the greatest in Virtual Reality settings as compared to video-based and text-based learning settings. Both theoretical and practical studies have importance in medical studies, whereas practical hand-on-practice can enhance medical students’ professional proficiency. Virtual reality was at the top in User experience, perceived competence, usefulness, and motivation for final year medical students. It can play a signficant role in contemporary teaching and learning methodology with medical educationist and students can get benefit from this technology.

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Higher education sector has always been incorporating changes pertaining to technology in order to provide better teaching and learning environment to the stakeholders, including students and faculty members. A smart classroom is the one, which is equipped with latest tools and technologies that are based on the internet. Various web-based tools are also being used to enhance teaching and learning experience. To provide appropriate content to the students, based on their level of understanding, learning analytics could be helpful. Students would be provided with the content, based on assessments and tests, the result of which could analyze the area of improvement suggested for them. In this way, faculty can ensure that learning is taking place at each level in the classroom. This research paper highlights such environment which provides a strong base for learning analytics for enhanced learning environment in higher education.

Higher educational institutes (HEI) are adopting ubiquitous and smart equipment such as mobile devices or digital gadgets to deliver educational content in a more effective manner than the traditional approaches. In present works, a lot of smart classroom approaches have been developed, however, the student learning experience is not yet fully explored. Moreover, module historical data over time is not considered which could provide insight into the possible outcomes in the future, leading new improvements and working as an early detection method for the future results within the module. This paper proposes a framework by taking into account module historical data in order to predict module performance, particularly the module result before the commencement of classes with the goal of improving module pass percentage. Furthermore, a video streaming server along with blended learning are sequentially integrated with the designed framework to ensure correctness of teaching and learning pedagogy. Simulation results demonstrate that by considering module historical data using time series forecasting helps in improving module performance in terms of module delivery and result outcome in terms of pass percentage. Furthermore, the proposed framework provides a mechanism for faculties to adjust their teaching style according to student performance level to minimize the student failure rate.