The multiple mediating effects of Korean workers’ perception of the fourth industrial revolution, career attitudes and future learning intent

Purpose This study aims at analyzing the impact of perceptions of the fourth industrial revolution (crisis and opportunity) in the relationship between workers’ career attitudes and future learning intentions. Design/methodology/approach This study analyzed the multiple mediating effect of the perception of the fourth industrial revolution in the relationship between career attitudes and future learning intention using data of 305 Korean workers. As career attitude variables, boundaryless and protean career orientation variables were used, and perception of the fourth industrial revolution was analyzed (opportunity and crisis perception). Findings Both workers’ boundaryless career orientation and protean career orientation influenced future learning intention through the perception of opportunity for the fourth industrial revolution. This result suggested that flexible career attitudes positively recognized the changes of the fourth industrial revolution and had an effect on promoting attitude toward future learning. Research limitations/implications The study confirmed that workers’ flexible career attitudes could promote perception of opportunity rather than crisis in changing situation and strengthen their intention to prepare for the future by mediating this perception. These results suggest that lifelong learning and competency development can be reinforced by facilitating perception of an opportunities for external change for individual career development. Originality/value Insights for personal career development were provided by analyzing the relationship between flexible career attitudes, which are increasing in importance in the modern society, and perceptions of changes in external environment.

Utilization of the Teacher-Made Strategic Intervention Material with Google Classroom in Improving the Performance of the Challenged Learn-ers

This classroom-based action research aimed to improve the performance of the challenged learners in Physical Science. The combination of Strategic Intervention Material (SIM) with the aid of Google Classroom was employed as an innovation to help the respondents master the competency-based skills that they were not developed during regular classroom teaching. In a combination of qualitative and quantitative designs, this research employed observation and participation, survey, and content-based assessment as data-gathering instruments. There were 40 identified challenged learners selected purposively (10 students for each of the four sections) based on their achievement scores after the long quiz, who took part in the study. The quantitative data were processed through Statistical Package for Social Sciences (SPSS), while the obtained qualitative data were interpreted using the phenomenological methodology. Results revealed an increase in the performance level of the challenged learners based on the increase of the mean percentage score of the posttest. Additionally, it showed that SIM promotes learning retention based on their scores in the delayed-posttest, and Google classroom motivates their learning intention towards the subject, Physical Science.

The Antecedents of the Consumers' Mobile Learning Intention during the Covid-19 Pandemic

Purpose Covid-19, which affects the whole world, has caused serious changes in many aspects such as lifestyle, habits and purchasing behaviour. New digital consumers and companies that emerged during the epidemic; they realized that mobile devices, especially mobile phones, have become a solution to many real-world problems such as learning and education anytime and anywhere. This study aims to determine the factors affecting the users' mobile learning (m-learning) usage intention during the Covid-19 pandemic process. Design/methodology/approach – This study contributed to the confirmation of the extended TAM model for a mobile device. The sample of the questionnaire is 460 students from different universities in Turkey. These data obtained were analyzed with the Structural Equation Model (SEM) and LISREL program was used for data analysis. Findings – This study proffers a model that the antecedents of the users' mobile learning intention during the Covid-19 pandemic. According to the results of the study, it was concluded that the factor affecting the users' mobile learning (m-learning) intention to use the most is perceived ease of use, the least effective factor is intrinsic leisure motivation, and the future anxiety factor has a meaningless effect. Originality/value – A holistic view of the antecedents of the users' mobile learning intention during the Covid-19 pandemic would be of important use to practitioners and academics alike. This study is different from previous studies; It is thought that it will contribute to the literature by addressing the effects of internal leisure motivation, future anxiety, behavioural spread and system-service quality dimensions on m-learning. For the researchers, this study took an important step towards explaining the m-learning relationship with students' intrinsic leisure motivation, future anxiety, behavioural spread, and system-service quality learning perspectives. Keywords Mobile learning, Mobile CRM, Technology Acceptance Model

The Impact of Quality of Experience of Chinese College Students on Internet-Based Resources English Learning

Since Internet-based resources provides various and practical forms of English learning materials, Internet-based resources English learning is a common way for the younger generation. However, not like adult learning, university students need stronger motivation to learn English from Internet-based resources. This study surveyed Chinese college students in Central China to reveal the relationship between cultural intelligence, hedonic motivation, English self-efficacy, online experience quality, and willingness to continue learning online English. Using online media platforms and convenient sampling methods, a total of 385 questionnaires were collected. The data analysis was divided into three phases, descriptive analysis, measurement model evaluation, and structural equation model examination. The results showed Internet quality of experience significantly impacted English continuous learning intention. Cultural intelligence, English self-efficacy, and hedonic motivation all influenced significantly on Internet quality of experience and hedonic motivation had the strongest impact. In addition, the mediation effects of Internet quality of experience to these three factors and Internet-based resources English continuous learning intention all existed. Finally, the research results show cultural intelligence, English self-efficacy, and hedonic motivation were all examined significantly impacting Internet quality of experience statistically. English learning hedonic motivation is the most influencing factor. Therefore, English learning material should be attractive, fun, and enjoyable. This is what the teachers should think of and emphasize when to recommend learning material for students.

Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning

Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.