Teaching through urban sensorium: urban spatiality as a smart learning environment

This paper qualitatively analyses the implication of urban sensorium as a pedagogic mode in the teaching of Urban Studies. Underpinned by the frames of smart learning environments, the paper reiterates experiencing urban ontologies as spatial learning environments. By drawing from a range of transdisciplinary and experiential modes of learning, this paper maps how an undergraduate course on Bangalore city in India served learners to critically engage with and experience spatial urban ontologies both digitally, and in real-world experiences of learning, furthering learner autonomy and reflection. The methodological prisms of this paper are autoethnography and critical reflection. It is organised around enabling learners recognize the experiential, embodied urban spaces through the urban sensorium via real-life engagements with urban spaces, and creation of digital portfolios that map this learning. Findings from the learners’ knowledge of sensory learning, the city’s intersectional aspects, and the student’s embodied and emplaced self in built environments and digital spaces are analysed via cognitive and affective-reflection levels; the course instructor's reflection is analysed via a process-reflection level. These reflections hold implications for the pedagogy of urban studies in undergraduate classrooms by foregrounding spatiality and urban sensorium as significant critical and affective pedagogic tools. The paper has also accommodated critical engagement with an external faculty member as a co-author, in order to manage any bias or researcher subjectivity in the design.

Developing Smart Learning Environments in Line with the Future Requirements of Artificial Intelligence: Saudi Universities

The current study aims to develop smart learning environments in Saudi universities in line with the future requirements of artificial intelligence. To achieve this goal, a systematic review was conducted on studies published on Scopus and Google Scholar databases from 1990 until 2021 on the development of e-learning in the light of artificial intelligence (in addition to the relevant Arab studies). First, a list of challenges and opportunities for developing smart learning environments according to the future requirements of artificial intelligence was composed. Then, a questionnaire was prepared and reviewed by several academic experts in educational technology in Saudi universities. The study results include many challenges expected to be encountered in the smart learning environments in Saudi universities concerning the future preconditions for artificial intelligence. It also presented a number of opportunities and procedures for facing such challenges and exploiting the opportunities. Finally, some recommendations and suggestions were presented.

Smart learning environments: a basic research towards the definition of a practical model

This paper is basic research focused on the analysis of scientific advances related to Smart Learning Environments (SLE). Our main objective is to single out the common aspects to propose a new definition which will constitute the starting point to design an innovative model which we can apply to the analysis of real cases and good practices. For this, we have proposed a qualitative methodology that has been implemented in two phases: on the one hand, a documentary analysis of the existing definitions for SLE using the NVIVO program (frequency of words, coding and cross-references) and, on the other, an expert judgement by means of the Delphi method in order to validate the proposed model. The main results are reflected in the coalescence of a new definition of SLE and the proposal of the model entitled SLE-5. With the present research, we have been able to provide a model, defined in five dimensions and other key elements in SLE such as ergonomics and learning analytics, which transcends the technological-pedagogical gap of the SLE and offers a framework for the design and analysis of didactic proposals based on this model.

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of time, place, platform, and form. In this paper we introduce the iCollab platform, an adaptive environment where learning activities are moderated through conversation with an intelligent agent who can operate across multiple web-based platforms, integrating formal and informal learning opportunities. Fifty-eight undergraduate computer science students were randomly assigned to either an intervention or control group for the 12 weeks of the pilot study. Learning analytics were used to examine their interactions with iCollab, while their course performance investigated the impact of using iCollab on learning outcomes. Results from the study showed a high level of interaction with iCollab, especially social interaction, indicating an interweaving of formal learning within their informal network spaces. These findings open up new possibilities for ways that SLEs can be designed to incorporate different factors, improving the ability of the system to provide adaptive and personalised learning experiences in relation to context and time.

Learning preference: development in smart learning environments

Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

Blockchain Technology Adoption in Smart Learning Environments

The conventional education system in developing countries has been enhanced recently by implementing the latest technology of distributed ledger. Disruptive technology is a fundamental requirement for greater accountability and visibility. We explored the key factors affecting the intentions of educational institutions to use blockchain technology for e-learning. This study proposed an expanded model of Technology Acceptance Model by integrating the diffusion of innovation theory. Based on an online survey, the conceptual model was tested and validated using structural equation modeling. The results showed that compatibility had a significant impact on blockchain use in smart learning environments. Other significant effects were also found on adoption of blockchain technology. This study offers an expanded Technology Acceptance Model for implementing blockchain that could assist decision makers in building a smart learning environment for the educational institutes for the emerging economies.