Towards a Holistic Evaluation Concept for Personalised Learning in Flipped Classrooms (21)

Incorporating the student’s preferences regarding pace, methods, and contents into teaching is particularly hard in today’s higher education, providing courses to large numbers of students often over electronic media. Such personalised learning can be implemented via self-regulated learning approaches using the method of the flipped classroom. However, literature on the design and evaluation of such courses is scarce. Evaluation models and instruments are not adapted to the specific nature of the flipped classroom yet, combining presence and online teaching. The present paper aims at conceptualising a holistic approach towards an evaluation concept for personalised learning. Based on an overview of evaluation models in the learning sciences and information systems domains an evaluation concept is presented and applied to a course instantiation focusing on the topics of (1) fulfilment of general requirements and effects on (2) learning outcomes, (3) adoption, and (4) individual factors of the students

A flexible framework for metacognitive modelling and development

Research in eLearning and technology enhanced learning (TEL) has predominantly focused on the creation of learning materials in appropriate forms, such as learning objects, the assessment methods that can usefully be applied online, and the delivery mechanisms for these materials, particularly in virtual learning environments (VLEs). In more recent times, research has begun to focus on pedagogical issues, and in particular whether there is some specific model that applies explicitly to online learning situations. Through a number of projects over the last ten years the authors have considered issues of learning style, learning strategy, pedagogy, immersive environments, student engagement and motivation, games-based learning, adaptation and personalisation. Emerging from this work, and from extensive consideration of the existing research in this area, this paper argues a need to move not only to a different pedagogic model, but also to change the existing structural approach to learning to support the rising demand for online distance learning provision worldwide. Fundamental to this argument is a need to support a heutagogic model of student learning, which requires that the students involved are sufficiently educationally mature to take control of their own learning experience. Whilst within traditional teaching models in higher education there is an explicit aspiration that students will emerge as educationally mature, metacognitive graduates, this is often seen as an outcome of the learning process itself, rather than as a skillset which can be taught and assessed. The paper describes an approach to metacognitive assessment that has already been used to determine the level and skills displayed by students in making selections of learning materials online. Based on this approach, a structural model for online learning support is proposed, using an assessment, feedback and training loop to ensure that students have the level of metacognitive skills necessary to take effective control of their own online learning experience

eduGraph: A Dashboard for Personalised Feedback in Massive Open Online Courses

Learning Analytics is concerned with the design and implementation of tools and processes for collecting, analysing, and communicating information about teaching and learning. It is enabled by data, but not driven by it, rather it tries to empower human judgements by presenting meaningful facts. This thesis explores the data generated in Open edX courses to understand how it can be analysed and used to impact learners' motivation in online courses. It is carried out using Design Science, a research methodology aiming to produce artefacts that can improve the interaction with problems. In this thesis I present the eduGraph dashboard, a dashboard that uses Learning Analytics to present meaningful insights about learners' learning process in Massive Open Online Courses (MOOCs). Results indicate that learners perceive the dashboard as useful and effective at motivating them to take part in online courses, and that it enables them to keep track of their progress in the courses. I posit that the biggest problem facing Learning Analytics today are the lack of accessible data, and that it is possible for reasearchers to create more accurate learner models by using Learning Anaytics theories and methods in combination with the iterative and technical process of Information Systems development.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

Workers researching the workplace using a work based learning framework: Developing a research agenda for the development of improved supervisory practice

This is a preprint of an article which later appeared in Impact: Journal of Applied Research in Workplace E-learning.The article is case study of academic practice in respect of the supervision of research in the workplace by distance learners using a Work Based Learning (WBL) framework. Key aspects of the WBL are described including the role of technology in delivery. Drawing upon tutor experience at one institution and knowledge of practice elsewhere several conceptual and practical issues are raised as the basis for a planned research exercise to identify commonalities and differences in approach among practitioners. Ultimately, the purpose is to improve the relevance and application of workplace research by practitioners

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

A literature review of personalized learning and the Cloud

In order to provide effective application of the Cloud in education it is essential to know how the learning should and could – if needed – be adapted. In this respect the concept of ‘personalising learning’ is frequently used. But what exactly is personalising learning. And how can it be implemented in using the cloud? The aim of WG3 i-Learner of the School on the Cloud network is to investigate this from the point of view of the learner, whereas WG2 i-Teacher looks on the role of the educators, and WG4 i-Future on the technology. The document has two parts: - The first part starts with an evaluation and synthesis of the definitions of personalized learning (Ch. 3), followed by an analysis of how this is implemented in learning style (e-learning vs. i-learning, m-learning and u-learning, Ch. 4) and learning approach (Ch. 5). To implement this an appropriate pedagogy (Ch. 6) is needed. - The second part is an attempt on how to implement this onto the learner of the future (Ch. 7), as well to the learning process and to the learning place. Recommendations are made in Ch. 8

Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models