Frontiers in Education: Digital Learning Innovations

IntroductionThis study explores the motivators to use learning management systems (LMS) adopted and used in eLearning by Hong Kong universities among Mainland Chinese postgraduate students amid COVID-19 pandemic.MethodsAn adapted and extended UTUAT2 model was first proposed and tested using the structural equation modeling approach. Through self-report online questionnaire, data were collected in 2022 from 352 Mainland Chinese postgraduate students of nine universities in Hong Kong. The reliability and validity of the data were tested using the confirmatory factor analysis, followed by path analysis to test the hypotheses in the proposed model.ResultsThe study revealed nine motivators which explained 50% of the variance in LMS use intention. Trust (0.204), instructor characteristics (0.202), performance expectancy (0.181), and facilitating conditions (0.181) were identified as strong motivators of behavioral intention. Other factors such as effort expectancy (0.148), learning value (0.118) and social influence (0.115) also had significant positive effects on LMS use intention. Habit (0.014) and hedonic motivation (−0.016) had no significant direct effect on it.DiscussionThese findings provide inspirations for educational stakeholders to promote the acceptance of LMS platforms among distance and online learners who adopt the cyberspace as the only means of learning. They also offer insights about instructor recruitment and evaluation methods.

IntroductionThough technologies for individualization appear to benefit primary school students’ learning, studies suggest that their integration remains sparse. Technology acceptance research has largely focused on exploring teachers’ general acceptance of educational technologies, although factors might predict usage intentions differently depending on the specific usage purpose of an educational technology. Digital learning platforms for individualized practice are comparably challenging and complex to use and so far, predictors of primary school teachers’ intention to integrate such technologies into lessons are largely unknown. Meanwhile, research on teachers’ technology acceptance generally lacks comparability due to the absence of a shared theoretical model and usage purpose specification.MethodsIn a sample of 272 German primary school teachers, this study aims to identify predictors of teachers’ acceptance of digital learning platforms for students’ individualized practice in consideration of the unified theory of acceptance and use of technology (UTAUT). To ensure a shared understanding, teachers were provided with a video which specified the addressed usage purpose. Regarding teachers’ usage intention, the explanatory power of the standard UTAUT predictors was investigated and compared with an extended UTAUT model accounting for seven additional context-specific predictors.ResultsThe standard UTAUT significantly explained teachers’ usage intention, with performance expectancy, effort expectancy, and the availability of the necessary technical infrastructure showing significant associations with intention. However, neither a significant nor meaningful increase in explained variance was observed for the extended UTAUT model.DiscussionResults suggest that the standard UTAUT model is sufficient in explaining teachers’ usage intention and that its extension by context-specific predictors provides no added value. Acceptance facilitating interventions should therefore target performance and effort expectancy as well as the availability of technical infrastructure. Thus, underlining that successful implementation of complex educational technologies should consider both, individual and structural factors.

The increasing interest in Learning Experience Design (LXD) has consolidated this field as a new way to transform educational practices. Thanks to its interdisciplinary nature which is mainly rooted in the close relation between human–computer interaction, user experience design and the learning sciences, LXD is a field that demands frameworks to design and develop products that can grow into services, to create interactive learning environments able to provide improvement-driven analytics and to guarantee a significant and satisfactory experience, designed to achieve learning outcomes. Innovative Village serious video game (IVVG) is a service-oriented product within an entrepreneurship and innovation system of platforms developed as an abilities-focused learning environment, and that builds a case study for LXD. This research aims to contribute to the consolidation of the emergent field of Learning Experience Design by providing a case around the Entrepreneurship and Innovation area from EAFIT University in Medellin, a learning system that comprises several service-oriented products; by being one of the products that constitute this system, and as a serious video game, Innovative Village has proven to be a key player in facilitating the learning outcomes and the knowledge integration that stem from the learning environment of the Interactive Design program, where the students’ learning experiences take place. First, related theoretical concepts and historical data will be analyzed to provide background information, then the case study will be addressed focusing on the materials, methods, and results. The study shows that the video game encourages collaborative behavior between players, as perceived by a significant proportion of participants. The research establishes a link between this perception and the role of creaticides in the game. Learning Experience Design (LXD) is about creating products that link the learning process with key competences. The game “Innovative Village” exemplifies this approach and provides insights into design, use, and required competencies. It also presents a framework for designing user-centered learning experiences that incorporate assessments to enhance the learning process. This framework is applicable from the early stages and can be tested in real learning environments.

The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model.