Multiple criteria decision-making (MCDM) is considered as a complex decision-making (DM) tool involving both quantitative and qualitative factors. In recent years, several MCDM techniques and approaches have been suggested to choosing the optimal probable options. The purpose of this article is to systematically review the applications and methodologies of the MCDM techniques and approaches. This study reviewed a total of 393 articles published from 2000 to 2014 in more than 120 peer reviewed journals (extracted from Web of Science). According to experts’ opinion, these articles were grouped into 15 fields. Furthermore, these articles were categorised based on authors, publication date, name of journals, methods, tools, and type of research (MCDM utilising research, MCDM developing research, and MCDM proposing research). The results of this study indicated that in 2013 scholars have published articles more than in other years. In addition, the analytic hierarchy process (AHP) method in the individual tool...

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Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014

Web-Based Education for All: A Tool for Development Adaptive Courseware.

Massive open online courses ( MOOCs) offer an exciting range of opportunities to widen access and participation in education. The massive and open nature of MOOCs places the control of learning at the discretion of the learner. Therefore, it is essential to understand learner behaviour. This paper examines the psychological considerations inherent in learning and explores the psychological determinants of learner behaviours relevant to MOOCs. A number of psychological challenges specific to the development and use of MOOCs such as the motivational, emotional and intellectual commitment of MOOC learners, and the skills profile that effective MOOC learners require are discussed. The psychological barriers that learners may encounter when engaging with MOOCs are identified. In doing so, we highlight the importance of considering the psychosocial and cognitive profile of the learner, and provide a psychological characterisation of many of the practical and theoretical issues that inform the design, development and delivery of MOOCs. For example, digital literacy skills, individual differences in motivation and self-regulation are key learner attributes in the context of MOOC-based learning. The temporal dimension of learning is also addressed: how learners perceive time online, how this influences time spent on tasks and how this may impact on the effective use of MOOCs. Given that MOOCs are increasingly being accessed using mobile devices, the psychological challenges of mobile MOOC-based learning are explored. It is anticipated that the insights derived from a psychologically informed consideration of MOOC-based learning will serve as a catalyst for debate, discussion and future research. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs): Insights and challenges from a psychological perspective

E-learners face a large amount of fragmented learning content during e-learning. How to extract and organize this learning content is the key to achieving the established learning target, especially for non-experts. Reasonably arranging the order of the learning objects to generate a well-defined learning path can help the e-learner complete the learning target efficiently and systematically. Currently, knowledge-graph-based learning path recommendation algorithms are attracting the attention of researchers in this field. However, these methods only connect learning objects using single relationships, which cannot generate diverse learning paths to satisfy different learning needs in practice. To overcome this challenge, this paper proposes a learning path recommendation model based on a multidimensional knowledge graph framework. The main contributions of this paper are as follows. Firstly, we have designed a multidimensional knowledge graph framework that separately stores learning objects organized in several classes. Then, we have proposed six main semantic relationships between learning objects in the knowledge graph. Secondly, a learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner’s target learning object. The experiment results indicate that the proposed model can generate and recommend qualified personalized learning paths to improve the learning experiences of e-learners.

A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning

A proposed paradigm for smart learning environment based on semantic web

Recommending suitable learning scenarios according to learners' preferences: An improved swarm based approach

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Applying Robotics in School Education: a Systematic Review

Recommending suitable learning paths according to learners' preferences

Evaluation of quality of personalised learning scenarios: An improved MCEQLS AHP method

The aim of the paper is to present a new MCEQLS AHP method for the expert evaluation of quality of learning scenarios. A special attention is paid to suitability of scenarios to particular learner groups (styles). Solution of learning scenarios quality evaluation and optimisation problem could help educational institutions to select suitable scenarios for particular learning styles. The research results will be implemented in iTEC – a four-year, largest pan-European e-learning R&D project focused on the design of the future classroom funded by 7th Framework Programme. A novel method of consecutive triple application of AHP is explored in more detail. Suitability of several iTEC scenarios to particular learner groups is also analysed in the paper. A number of multiple criteria decision analysis principles are applied to create a comprehensive quality model (criteria tree) for evaluating quality of scenarios. Several optimisation methods are explored and applied to optimise learning scenarios in co...

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Inga Zilinskiene

Papers

Recommending suitable learning scenarios according to learners' preferences: An improved swarm based approach

Applying Robotics in School Education: a Systematic Review

Recommending suitable learning paths according to learners' preferences

Evaluation of quality of personalised learning scenarios: An improved MCEQLS AHP method

New MCEQLS AHP method for evaluating quality of learning scenarios