Learning preference: development in smart learning environments

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lizhao Zhang ◽  
Xu Du ◽  
Jui-Long Hung ◽  
Hao Li
Keyword(s):  
Learning Environments ◽  
Synthesis Method ◽  
Learning Preferences ◽  
Future Research ◽  
Content Type ◽  
Learning Preference ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Art Research ◽  
State Of Art

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.

Firm size and workplace learning processes: a study of the restaurant sector

2020 ◽  
Vol 44 (2/3) ◽  
pp. 305-320
Author(s):  
Daniel Bishop
Keyword(s):  
Informal Learning ◽  
Human Resource Development ◽  
Learning Environments ◽  
Workplace Learning ◽  
Firm Size ◽  
Learning Processes ◽  
Future Research ◽  
Structured Learning ◽  
Individual Choice ◽  
Content Type

Purpose The purpose of this paper asks how workplace learning environments change as firm size increases, and how employees respond to this. In doing so, it looks beyond an exclusive focus on formal training and incorporates more informal, work-based learning processes. Design/methodology/approach The study uses a comparative, qualitative research design, using semi-structured interviews with an under-researched group of workers – waiting for staff in restaurants. The data were collected from six restaurants of different sizes. Findings As formally instituted human resource development (HRD) structures expand as firm size increases are more extensive in larger firms, this leaves less room for individual choice and agency in shaping the learning process. This does not inevitably constrain or enhance workplace learning, and can be experienced either negatively or positively by employees, depending on their previous working and learning experiences. Research limitations/implications Future research on HRD and workplace learning should acknowledge both formal and informal learning processes and the interaction between them – particularly in small and growing firms. Insights are drawn from the sociomaterial perspective help the authors to conceptualise this formality and informality. Research is needed in a wider range of sectors. Practical implications There are implications for managers in small, growing firms, in terms of how they maintain space for informal learning as formal HRD structures expand, and how they support learners who may struggle in less structured learning environments. Originality/value The paper extends current understanding of how the workplace learning environment – beyond a narrow focus on “training” – changes as firm size increases.

Open Learner Models in Smart Learning Environments

2019 ◽  
pp. 346-368 ◽  
Author(s):  
Angeliki Leonardou ◽  
Maria Rigou ◽  
John D. Garofalakis
Keyword(s):  
Learning Environments ◽  
Adaptive Learning ◽  
Real Life ◽  
Self Reflection ◽  
Learner Model ◽  
Self Regulated Learning ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Navigation Support ◽  
Adaptive Learning Systems

Smart learning environments (SLEs), like all adaptive learning systems, are built around the learner model and use it to support a variety of interventions such as mastery learning, scaffolding, adaptive sequencing, and adaptive navigation support. Open learner models (OLMs) “expose” the learner data to users through easily perceivable visual representations aiming to improve student self-reflection and self-regulated learning and also increase user motivation and even foster collaboration. This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations. OLM cases implementing typical SLEs features are described, along with representative real-life scenarios of incorporating OLMs in SLEs. Moreover, the chapter provides guidelines for designing effective OLMs and discusses current research trends in this active scientific field.

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