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We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

The COVID-19 pandemic has created a significant disruption in the educational systems worldwide. Some institutions opted for emergency remote education due to the need to cancel in-person activities. The aims of this paper were to evaluate the use of asynchronous methodology in health sciences education, determine whether asynchronous methodology was sacrificing overall student satisfaction, and investigate whether satisfaction improved as the program develops.

Initially, there was phase 1 that corresponded to four weeks of activities. Each professor produced a video lesson, and after each video lesson, a weekly educational activity was made available. Next, phase 2 was implemented using the same methodology, however lasting six weeks. Three questionnaires were developed, and a Likert scale was administered to verify the students’ level of satisfaction. Data were analyzed using frequency distributions, mean values, standard deviation and confidence interval. The normality of the sum data (total of the questionnaires) was tested using the Kolmogorov–Smirnov test.

Although the students pointed out that the asynchronous methodology facilitated access to the content and considered this methodology satisfactory, they expressed a reduced level of satisfaction regarding emergency remote education in general when data from the first weeks were compared to those of the previous weeks. It is clear that students became increasingly discouraged and tired over time, which motivated the institution to shift into a combination of synchronous and asynchronous methodology to improve student learning.

Teaching in the field of health care encompasses difficult competencies that sometimes are impossible to be learned remotely, so there is a need to examine and evaluate properly the remote education in this area. With careful planning, educational institutions can evaluate their experiences during the pandemic, allowing those involved to highlight strengths and identify weaknesses to better prepare for future needs to improve remote education.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

The objective of this study is to examine the drivers of retail apps satisfaction and continuance intention. An integrative theoretical framework was developed based on the IS success model, E-S-QUAL and expectancy and disconfirmation model to explain retail apps users’ satisfaction and continuance intention.

A total of 359 useable data were collected from the targeted Malaysian respondents who had experience in using retail apps services. Data were analysed using the partial least squares technique.

The results indicate that system quality and e-service quality positively influence retail apps usage satisfaction and have positive direct and indirect effects through satisfaction on continuance intention. The price level has a negative effect on retail apps usage satisfaction. Even though price level has no direct effect on continuance intention to use retail apps, it has an indirect effect on continuance intention through satisfaction.

Although the success of a marketing channel mainly depends on its continuance usage rather than first-time usage, few studies have paid attention to retail apps services. This study contributes to the advancement of knowledge on retail apps by explaining the roles of system quality, e-service quality and price level on retail apps satisfaction and continuance intention. Interestingly, the findings of multi-group analysis imply that female Gen Y app users are more satisfied than males while such differences do not impact their continuance intention to use the retail apps. The findings also suggested that frequency of using apps has no relevance to retail apps user satisfaction, but highly relevant to their continuance intention to use retail Apps services.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

E-learning has become a polarizing issue. Some say that it enhances accessibility to education and some say that it hinders it. While the literature on the subject underscores the effectiveness of the pedagogical frameworks, strategies and distance learning technologies, the firsthand accounts of students, parents and practitioners challenge the validity of experts’ assessments. There is a gap between theory and practice and between the perceptions of providers and consumers of online learning. Following a period of lockdowns and a transition to online learning during the recent pandemic, the prevailing sentiment toward a distance mode of instruction became one of strong skepticism and negative bias. The aim of the study was to examine why e-learning has struggled to meet stakeholder expectations. Specifically, the study posed two research questions: 1. What are the reasons for dissatisfaction with online learning? 2. What are the implications for future research and practice?

The study used a mixed methods approach to examine the reasons behind negative perceptions of online learning by comparing the firsthand accounts posted on social media with the literature. To this end, n = 62,874 social media comments of secondary and postsecondary students, as well as parents, teachings staff and working professionals, covering the span of over 14 years (2008–2022), were collected and analyzed.

The study identified 28 themes that explain the stakeholder’s discontent with the online learning process and highlighted the importance of user-centric design. The analysis revealed that the perceived ineffectiveness of distance education stems from the failure to identify and address stakeholders’ needs and, more particularly, from the incongruence of instructional strategies, blindness to the cost of decisions related to instructional design, technology selection and insufficient levels of support. The findings also highlight the importance of user-centric design.

To address dissatisfaction with e-learning, it is imperative to remove barriers to learning and ensure alignment between technology and learners’ needs. In other words, the learning experience should be personalized to account for individual differences. Despite its cost-effectiveness, the one-size-fits-all approach hinders the learning process and experience and is likely to be met with resistance.

Drawing from the extensive literature, the study offers an explanation for stakeholders’ discontent with e-learning. Unlike survey research that is prone to social desirability bias, the sample provides a rare opportunity to observe and measure the visceral reactions that provide a more authentic sense of stakeholders’ perceptions toward online learning. The authors offer recommendations and identify areas for future research.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

The ultimate objective of this study is to compare the impact of face-to-face and online flipped learning on students' academic achievements and their perspectives on learning and teaching, offering valuable insights to the field.

The study utilized a quasi-experimental research method that involves pre-test and post-test control groups.

The results indicated that face-to-face and online flipped learning positively impacted learning, with comparable contributions to academic achievement. However, we found that online flipped learning did not affect students' beliefs about learning and teaching, while face-to-face flipped learning positively influenced them.

As distance learning becomes increasingly important in our modern era, this research aims to explore the use of active learning methods, including discussion, writing, animation, drawing, association, analysis, knowledge measurement and games, in virtual learning environments, such as online flipped learning. The study seeks to enhance the existing literature on the impact of face-to-face and online flipped learning models on student success. Additionally, it aims to address a significant gap in the literature by determining the effect of these models on students' epistemological and pedagogical beliefs, which can impact their motivation, learning outcomes, academic achievements and decision-making processes.

Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.

A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.

Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R² is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.

In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.