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The purpose of this paper is to examine the roles of task-technology fit (TTF), learning-technology fit (LTF) and cognitive absorption (CA) in determining medical professionals’ cloud-based electronic learning (e-learning) system continuance intention and performance outcomes and evaluate whether medical professionals’ perceived impact on learning can affect their perceived impact on tasks within medical institutions.

Sample data for this study were collected from medical professionals at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 373 (62.2%) usable questionnaires were analyzed using structural equation modeling in this study.

In this study, medical professionals’ perceived TTF and LTF as antecedents to their cloud-based e-learning continuance intention and performance outcomes were validated, and medical professionals’ perceived impact on learning had a positive effect on their perceived impact on tasks. Synthetically speaking, this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that this study introduces a new construct, “LTF,” to conceptualize, define and measure it, and further contributes to the application of capturing both expectation–confirmation model and CA (i.e. an intrinsic motivator) for completely explaining medical professionals’ perceived TTF and LTF as external variables to their cloud-based e-learning continuance intention and performance outcomes.

The Consumer Electronics Show (CES) offers an opportunity for librarians to add their important perspective to the broader technology conversation. This paper aims to give librarians tips for preparing and strategizing for a successful experience. It also provides newcomers to the show with ideas on how they can gain access and who should go.

This study is a conference report.

The CES is a worthwhile event for librarians.

CES is unknown to many librarians. There are many misunderstandings about the ability to gain access. There is much uncertainty about what goes on at the show. This paper explains CES and how librarians can put their experience to good use for the profession and their organizations.

The research aims to measure the effectiveness of collaborative learning exchanges transpired through digital tools and technologies (DT&Ts) employed by the mentor universities during the COVID-19 pandemic by conducting an empirical study on undergraduate students in Indian higher educational institutions (HEIs) under the mentorship program based on the corporate social responsibility (CSR) initiative. The pandemic scenario, its impact on the mentor university's social responsibility and the way DT&Ts can assist are investigated in this article.

The interactions with experts and students were conducted to explore the DT&Ts for learning exchanges. Next, structural equation modeling (SEM) was performed to validate the model and perform regression analysis. The quantitative data collection was made through questionnaires during the second deadly wave of COVID-19 that hit India.

The independent variables (IVs) such as the IT infrastructure support (IT_IS), virtual collaborative tools (VCTs) and future-oriented technologies (FOTs) have a significant impact on the CSR learning outcomes (CSR_LOs) of undergraduate students under the mentorship program. However, IV research instruments for innovation could not make a significant effect.

The IVs IT_IS, VCTs and FOTs influence the CSR_LOs, while RII does not have an influential impact.

As the online learning environment is expected to stay at least in a blended form, adequate CSR funding in infrastructure is necessitated to harness the full potential of this important resource, technology. The results of this empirical investigation affirm that IT_IS, VOTs and FOTs significantly impact CSR_LOs during the crisis. The study findings would encourage the mendtor universities and their stakeholders, including the mentee universities, to evolve and create an ecosystem for effective management of these resources to attain positive outcomes. The study findings can guide the mentor universities in managing uncertainties like pandemics and effectively using the earlier-mentioned critical resources for social responsibility. This research also allows the development of future applications adnd models in mentor-mentee universities for social responsibility, post-pandemic transformation and resilience.

The DT&Ts came to the immediate rescue during the pandemic and positively affected collaborative CSR_LOs by the mentor universities, but they have not evolved to a level where offline learning can be replaced entirely. Hence, it can be inferred that a hybrid model is preferable. The study also improves the understanding of how DT&Ts are being harnessed to aid collaborative learning in fulfilling the mentors' CSR in fatal emergencies. The purpose is to equip the education system through mentorship so that universities can sustain, innovate and grow even in trying times. Also, it discusses the dynamics of various DT&Ts for creating a sustainable learning environment and utilizing them to make the teaching prolific and influential.

There is a scarcity of literature regarding the learning outcomes realized through CSR initiatives and collaboration between mentor-mentee institutions. There is a need to understand how these knowledge exchanges continued despite the physical restrictions during the pandemic. In this direction, this study helps to understand how the DT&Ts played a critical role in continuing learning and keeping abreast in a knowledge society from the perspective of resource-based view (RBV) in these precarious situations.

There have been calls to embed Information and Communication Technology (ICT) into pre-service teacher courses in preference to technology only courses as a means to provide graduate pre-service teachers with the necessary skills to integrate ICT into their teaching practice. This chapter describes a case study of a pre-service science education curriculum course that was designed to embed ICT into its curriculum, assessment and delivery. The tutor modelled best teaching practice in the use of learning technologies. The theoretical framework is Technological Pedagogical and Content Knowledge (TPACK) viewed through a representation construction approach. This approach involved the students undertaking a series of representational challenges where they constructed and critiqued representations. The study found increased student engagement with learning technologies and an enhanced TPACK over the period of the course. Several factors that may have led to these findings are discussed.

As the use of Web‐based learning technologies to supplement college classroom instruction has increased, a key issue for faculty members is how to entice their students to accept these technologies. Research suggests that student experience with the technologies may influence their acceptance. The technology acceptance model was used as a framework to study the effect of student experience with Web‐based learning technologies on their acceptance of those technologies. Analysis showed that experience with the technology positively influenced perceived ease of use. Perceptions of ease of use and usefulness positively influenced attitudes towards the technology. Attitude positively influenced intention to use, and intention positively influenced usage. Results suggest that as student experience with a technology increases, they perceive it to be easier to use and more useful, and therefore, are more likely to use it. When introducing the technology, instructors should emphasize how easy the technology is to use and how useful it is in improving grades.

This study aims to examine current key debates on learning technologies in the everyday life of lecturers and explores their experiences of learning technologies.

The research adopted a mixed method ethnographic approach to explore the perceptions of academics at a new university in the UK. The narrative and image data was collected via 30 interviews and an online forum and then subjected to grounded theory analysis. This study presents the findings of the study and discusses aspects of quality and meaningful engagement by academic staff with learning technologies in a higher education environment.

Analysis of the data revealed the central concepts of paradigms, paradoxes and professionalism. The diversity of perspectives of staff and students, skills, motivations and capabilities is fundamental to developing, supporting and promoting the innovative use of e‐learning and learning technologies in learning, teaching and assessment.

This study examined the reality of lecturers’ relationships with learning technologies in everyday life and the diversity of those lived experiences in relation to social change and educational ideologies. This study is, therefore, significant and adds to knowledge and understanding of academics’ perspectives which Hanson claims have previously been neglected. This research also contributes to previous studies of e‐learning and Actor Network theory (ANT).

The purpose of this paper is to emphasize the role of education as the formal means for preparing people for the future, outlining the need for interdisciplinary and systemic use of new learning technologies to re‐align with present learning needs.

Using the lens of Miller's Living Systems Theory, such a collaboration between learning scientists, educators, and policy makers is now both possible and necessary because of emerging learning technologies.

Using these learning technologies across the curriculum throughout a learner's life will assist them to adapt within increasingly fast‐paced, interconnected, and complex systems.

Administrators should collaborate with teachers, organizations, learning scientists, and students, themselves to integrate these new learning technologies in substantive new ways that enhance future conciousness.

The paper provides a potential bridge between the abstract notion of future consciousness and the proliferating use of new learning technologies. Educational leaders may find here ideas for situating their institutional visions, missions, and strategic directions.

This paper aims to investigate the factors that affect the acceptance of distance learning systems by university academic staff and students in Jordan. To achieve this objective, it has been proposed to examine the distance learning experience of Jordanian Higher Education Institutions (HEIs) after the universities institution suspended face-to-face (traditional) courses delivery owing to novel Coronavirus’ (COVID-19) fears.

This study expands upon unified theory of acceptance and use of technology 2 by incorporating contextual variables such as trust (TR), autonomy (AUT) and compatibility (CMP). Data collection has been carried out through an online survey, which targeted participants at public and private universities during the crisis time of coronavirus. Structural equation modelling has been used to validate the proposed research model.

The outcomes revealed that performance expectancy, facilitating conditions, TR and AUT were the significant predictors of distance learning acceptance in both samples. By identifying the factors affecting the acceptance of distance learning systems, it will be more useful to offer better services of distance learning. This will also help to demonstrate that distance learning will be capable of delivering the educational aims of HEIs to areas where a pandemic outbreak in the Middle East.

Distance learning provides university students with quality education, engaging platforms and most significantly a safe teaching environment. The results and implications to both practice and theory are described.

The use of learning management systems (LMSs) such as Google Classroom has increased significantly in higher education institutes during the COVID-19 pandemic. However, only a few studies have investigated instructors’ continued intention to reuse LMS. The purpose of this study is to investigate the factors that influence instructors’ intention to reuse an LMS in higher education institutes.

This study adopted a mixed-method research design. In the quantitative section, an integrated model of technology acceptance model and information system success model is proposed to explore the effects of system quality, service quality, information quality, perceived ease of use and perceived usefulness on instructors’ satisfaction and how their satisfaction will influence their intention to reuse Google Classroom in the future. In the qualitative section, to gain more understanding, instructors were asked to identify the challenges that inhibit the adoption of e-Learning technologies in public universities in Iraq and what are their recommendations to rectify them.

The findings revealed that service quality had no positive influences on the satisfaction of instructors, while other factors had varying levels of influence, the findings further showed that inadequate internet service and students lack of interest are the biggest challenges instructors faced during their experience with Google Classroom.

To improve the generalizability of the results, future studies are recommended to include larger samples, in addition, further studies are also advised to take individual traits such as age and gender into consideration.

The outcomes of this study are expected to benefit researchers, policymakers and LMS developers who are interested in factors that affect instructors’ intention to reuse LMS in higher education institutes in developing countries.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.