Opportunities, Quality Factors, and Required Changes during the Pandemic Based on Higher Education Leaders’ Perspective

Instructors' self-efficacy, perceived benefits, and challenges in transitioning to online learning

Drawing on social cognitive theory, this study investigated instructors' online teaching self-efficacy during the sudden, COVID-19-induced transition to online teaching. The pandemic has forced instructors to shift to online teaching, arming them with valuable hands-on experience in this alternative teaching mode. This study examined instructors' online teaching self-efficacy, perceived benefits, intention to implement online teaching strategies in their future teaching, and the challenges encountered during this transition. A total of 344 instructors completed the developed and validated questionnaire. The data were analyzed using multiple linear regression modeling, using the stepwise estimation technique. The findings demonstrate that affiliated universities, the quality of online learning, and previous use of learning management systems (LMS) are significant predictors of instructors' online teaching self-efficacy. Online teaching self-efficacy, along with gender, quality of online learning, and professional training are significant predictors of the perceived benefits of online learning during emergencies. Meanwhile, the quality of online learning and professional training are significant predictors of instructors' intention to implement online teaching strategies and learning technology tools. Instructors ranked remote assessment as the most challenging factor in online teaching during emergencies, and internet access or internet speed as the first and most complicated hindrance for students in this transition. This study helps in understanding instructors' online teaching self-efficacy during the sudden transition and the positive consequences of shifting to the online mode due to the COVID-19 pandemic on the higher education field. Recommendations and implications are discussed.

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

The gross domestic product (GDP) of a country is mainly dependent on its trade and external sector which improves the country's income. According to FY2021-2022, India's nominal GDP is estimated to be 3.12 trillion US dollars. Overall exports and imports have a year-over-year increase of 49.6% and 68% respectively. Machine learning techniques have the potential to improve India's Current gross value by up to 15% by the year 2035. The integration of data, Technology, and talent helps to create intelligent models that enhance artificial intelligence growth. This paper presents an optimized light gradient boosting machine (Light GBM) model using the hybrid Harris hawk optimization (H3O) algorithm for trade forecasting. The overfitting problem in the conventional Harris Hawk Optimization is overcome using the exclusive feature bundling (EFB) and the gradient-based one-side sampling (GOSS) methodologies. The H3O optimization algorithm offers fast convergence by optimizing different lightGBM parameters such as a number of training iterations, maximum depth, minimal data in the leaf, etc. To improve the performance, one step further, the residual errors of the optimized lightGBM model are corrected using the Markov Chain model. The main aim of the optimized lightGBM model is to extract the crucial input values of certain variables such as imports and exports of goods and services, service trade, and merchandise trade and predict the price movement decision. The proposed model identifies the interrelationship with the external market and future market growth along with analyzing the variation in market conditions. The prediction decision is mainly to hold, but, or sell the stocks. When evaluated using the precious metal price forecast and stock market datasets, the proposed methodology shows that the hybrid approach can enhance the prediction performance. The results show that the input parameters were efficient in predicting the economic growth regarding the Intermarket trading system (ITS) and services with higher accuracy.

Advantages and Disadvantages of E-Learning Innovations during COVID-19 Pandemic in Higher Education in Poland

The paper presents the results of the research on the advantages and disadvantages of innovative e-learning during the COVID-19 pandemic in higher education. In the first stage, an internet questionnaire was used for the study. The research was carried out in Poland on a sample of 621 students. The results allowed the researchers to determine variables that are important for the proper implementation of innovative e-learning, and the emergent perspectives for this form of education after the end of the COVID-19 pandemic. Among the most significant disadvantages, there was a lack of direct contact with colleagues, difficulties with teaching practical subjects, lack of direct contact with the teacher, and too much time spent in front of a computer/telephone or other mobile device. The most important correlations included the following: students satisfied with e-learning assess the advantages highly and the disadvantages lower; all the advantages of innovative e-learning are positively correlated with the student’s assessment of the ease of acquiring content in e-learning, and negatively correlated with the student’s assessment of the ease of acquiring content in traditional education; the easier the student assimilates innovative e-learning content, the higher the student’s evaluation of the advantages of e-learning; students who perceive e-learning content as difficult to absorb have low motivation to learn remotely; the better the student knows information technology, the more highly the student evaluates the advantages of innovative e-learning; the better resources to participate in e-learning activities the student has, the higher the student’s evaluation of the advantages of e-learning; the more often innovative e-learning solutions are used in a given university, the better the student assesses the advantages of e-learning.

Influence of COVID-19 Pandemic on Dissemination of Innovative E-Learning Tools in Higher Education in Poland

The paper presents the results of the research on the influence of the COVID-19 pandemic on the dissemination of innovative e-learning tools in higher education. Research was carried out in Poland in December 2021 on a sample of 621 students. The main issue that was the subject of the author’s analysis was the influence of the COVID-19 pandemic on the change in the use of innovative e-learning tools in university education. After conducting the research and discussing this and related research about e-learning during the pandemic, it was concluded that the percentage of students familiar with the analyzed e-learning tools has increased significantly during the pandemic. There has been a visible rise, especially in the usage of the following tools: MS Teams, Zoom, and Google Classroom. The most frequently used e-learning tools during the COVID-19 pandemic have been mainly videoconferencing tools such as MS Teams and Zoom. However, students also have used e-learning platforms and e-mails. The author’s research identified three hidden factors (categories) of the used e-learning tools. They include the following categories: popular services and applications adapted to e-learning; popular applications for synchronous meetings adapted to e-learning; and other synchronous and asynchronous e-learning methods. The familiarity with information technology, as well as an interest in innovative e-learning tools, have positive influence on the ease of acquiring content in e-learning. Having the proper resources also positively influences the absorption of e-learning content. On the basis of the achieved results, the authors prepared a model of relations between students’ interest in innovative e-learning technology and the resources they possess to participate in e-learning classes. This model enables us to assess which method—e-learning, traditional or hybrid—should be used in the given situation. The developed model can be useful for universities. They can assess the students’ interest in innovative e-learning technologies and their level of technical resources using questionnaires and on this basis divide students into groups to prepare the optimal learning way—e-learning, traditional or hybrid.

Measuring the Impact of the Pandemic on Female and Male Students’ Learning in a Society in Transition: A Must for Sustainable Education

A sustainable education amid a disruptive event (e.g., a pandemic) requires the objective assessment of learning before and during the event and, if necessary, evidence-driven solutions in response to deficiencies. The present action research study illustrates an evidence-based response of educators to the widespread concerns that learning in college students, accustomed to face-to-face courses, might have been damaged during the pandemic by the switch to the online mode. It focuses on general education (GE) courses as they usually enroll students at the beginning of their journey in higher education, and thus, a population that is likely to be particularly sensitive to unforeseen changes. Pass/fail grades in courses taught face-to-face and online synchronously by the same instructors were examined. It was hypothesized that if the switch from face-to-face to online instruction changed the students’ approaches to learning, course performance would differ between the instructional modes. Differences in female and male students’ adaptation responses were expected to be reflected in their course performance. The study found that female students performed better online than face-to-face in Arabic Culture, Natural Science, Math, and Wellness courses. Male students also performed better online in Math and Natural Science courses, whereas they exhibited better performance face-to-face in Arabic Culture, Wellness, and Professional Competency courses. It was concluded that basic indices of uneven performance can guide further analyses into the sources of female and male students’ approaches to instructional modes.