In order to eliminate discrepancies between the literature on the use of digital media for education and its effects on students’ academic achievement in higher education institutions, this article aims to develop a model that would identify essential aspects that are predicted to continue to play a large role in TTF and CT for learning, which could be used to improve academic performance in higher education. The purpose of this study was to investigate the characteristics and aspects of digital media and the relationship between their use in the TTF and CT theories to determine how they affect research students’ satisfaction and AP in HE institutions. Data for the TTF and CT theories were collected using a questionnaire survey. A questionnaire survey was the primary method of data collection. A total of 1330 students who were acquainted with digital media participated in the data collection survey from Universiti Kebangsaan Malaysia. To assess the findings, quantitative structural equation modeling was used. Technological, task, and social characteristics were found to have a substantial association with TTF for using digital media sites for academic purposes, which had a favorable impact on satisfaction and educational achievement. Similar to this, a significant relationship between online communication, reasons for communicating, communication self-efficacy, and attitude toward using features with TC was found to exist for utilizing digital media sites for educational purposes that positively impacted satisfaction as well as academic performance. The study concludes that TTF and CT concepts for using digital media enhance students’ active learning and give them the ability to effectively exchange knowledge, data, and discussions. In order to further their educational objectives, we advise students to make use of digital media platforms. Additionally, educators in higher education institutions need to be convinced to use digital media platforms in their lessons.

Since the beginning of the COVID-19 pandemic Universities around the world are taking rapid actions to ensure students learning continuity and secure the well-being of their students. This study aims at exploring the student’s e-learning experience in Jordanian Universities as well as e-learning readiness during the pandemic. While each university is unique, we hope our assessment can provide some insights into how well the student’s e-learning experience was during the pandemic. A structural online questionnaire was distributed, followed by descriptive analysis. Students from remote and disadvantaged areas primarily faced enormous challenges such as technological accessibility, poor internet connectivity, and harsh study environments. This study also highlights the role of electronic commerce in transforming distance learning. Further investments and contingency plans are needed to develop a resilient education system that supports electronic and distance learning throughout Jordan.

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.