The Experience of Development and Application Perspectives of Learning Integrated Expert Systems in the Educational Process

The main principles and experience of development of learning integrated expert systems based on the third generation instrumental complex AT-TECHNOLOGY are considered.

Technology for Ontological Engineering Lifecycle Support

The research is partially supported by Russian Foundation for Basic Research (grants 06-01-81005 and 07-01- 00053)

Teaching Strategies and Ontologies for E-Learning

* The work is partially suported by Russian Foundation for Basic Studies (grant 02-01-00466).

Knowledge Construction Technology through Hypermedia-Based Intelligent Conversational Channel

There have been multifarious approaches in building expert knowledge in medical or engineering field through expert system, case-based reasoning, model-based reasoning and also a large-scale knowledge-based system. The intriguing factors with these approaches are mainly the choices of reasoning mechanism, ontology, knowledge representation, elicitation and modeling. In our study, we argue that the knowledge construction through hypermedia-based community channel is an effective approach in constructing expert’s knowledge. We define that the knowledge can be represented as in the simplest form such as stories to the most complex ones such as on-the-job type of experiences. The current approaches of encoding experiences require expert’s knowledge to be acquired and represented in rules, cases or causal model. We differentiate the two types of knowledge which are the content knowledge and socially-derivable knowledge. The latter is described as knowledge that is earned through social interaction. Intelligent Conversational Channel is the system that supports the building and sharing on this type of knowledge.

KFTKGA: Knowledge Flow Tracer and Growth Analyzer for Community Learning

The advances in building learning technology now have to emphasize on the aspect of the individual learning besides the popular focus on the technology per se. Unlike the common research where a great deal has been on finding ways to build, manage, classify, categorize and search knowledge on the server, there is an interest in our work to look at the knowledge development at the individual’s learning. We build the technology that resides behind the knowledge sharing platform where learning and sharing activities of an individual take place. The system that we built, KFTGA (Knowledge Flow Tracer and Growth Analyzer), demonstrates the capability of identifying the topics and subjects that an individual is engaged with during the knowledge sharing session and measuring the knowledge growth of the individual learning on a specific subject on a given time space.

Project Proposals Development – Entrepreneurship and Reflection in Teaching Information Technology

This work presents a model for development of project proposals by students as an approach to teaching information technology while promoting entrepreneurship and reflection. In teams of 3 to 5 participants, students elaborate a project proposal on a topic they have negotiated with each other and with the teacher. The project domain is related to the practical application of state-of-theart information technology in areas of substantial public interest or of immediate interest to the participants. This gives them ample opportunities for reflection not only on technical but also on social, economic, environmental and other dimensions of information technology. This approach has long been used with students of different years and programs of study at the Faculty of Mathematics and Informatics, Plovdiv University “Paisiy Hilendarski”. It has been found to develop all eight key competences for lifelong learning set forth in the Reference Framework and procedural skills required in real life.

On a Virtual Learning Environment

Education in the Information Society is based on asynchronism in time and space, interactivity and virtual restructuring of the educational space. One way to implement such a model of training is web-based - use of the WWW as a virtual environment to access educational materials or to organize the learning process. This work presents a virtual learning environment (VLE) developed for students and made up of modules of dynamically changing content implemented by authorized users. The aim is, through advanced technology for e-learning, testing and self-testing to stimulate students’ activity to focus their potential on the acquisition of the necessary knowledge, skills and competences. The VLE was developed under the Human Resources Development Operational Programme.

Use of Start Pages for Building a Mashup Personal Learning Environment to Support Self-Organized Learners

The paper explores the functionalities of eight start pages and considers their usefulness when used as a mashable platform for deployment of personal learning environments (PLE) for self-organized learners. The Web 2.0 effects and eLearning 2.0 strategies are examined from the point of view of how they influence the methods of gathering and capturing data, information and knowledge, and the learning process. Mashup technology is studied in order to see what kind of components can be used in PLE realization. A model of a PLE for self-organized learners is developed and it is used to prototype a personal learning and research environment in the start pages Netvibes, Pageflakes and iGoogle.

Transforming Mathematics Learning for Students and Teachers with Digital Technologies

Силия Хойлс, Ричардс Нос - Това представяне е вдъхновено от делото на Сиймър Пепърт и много други от целия свят (включително Джим Капут и наши колеги от България), които са работили и работят в духа на конструкционизма и с които сме имали щастието да си сътрудничим в областта на математическото образование и дигиталните технологии в течение на десетилетия.

A sphere of resonance for networked learning in the ‘non-places’ of our universities

The logic of ‘time’ in modern capitalist society appears to be a fixed concept. Time dictates human activity with a regularity, which as long ago as 1944, George Woodcock referred to as The Tyranny of the Clock. Seventy years on, Hartmut Rosa suggests humans no longer maintain speed to achieve something new, but simply to preserve the status quo, in a ‘social acceleration’ that is lethal to democracy. Political engagement takes time we no longer have, as we rush between our virtual spaces and ‘non-places’ of higher education. I suggest it’s time to confront the conspirators that, in partnership with the clock, accelerate our social engagements with technology in the context of learning. Through Critical Discourse Analysis (CDA) I reveal an alarming situation if we don’t. With reference to Bauman’s Liquid Modernity, I observe a ‘lightness’ in policy texts where humans have been ‘liquified’ Separating people from their own labour with technology in policy maintains the flow of speed a neoliberal economy demands. I suggest a new ‘solidity’ of human presence is required as we write about networked learning. ‘Writing ourselves back in’ requires a commitment to ‘be there’ in policy and provide arguments that decelerate the tyranny of time. I am though ever-mindful that social acceleration is also of our own making, and there is every possibility that we actually enjoy it.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Digital learning, discourse and ideology

In recent decades, digital technologies have seen widespread use across global society and adoption at all levels of education. Digital learning might therefore simply be described as learning that is facilitated by digital technologies, but to discuss digital learning only in this way obscures important complexities linked to language, culture, politics, and the economy. To talk or write about learning as if it were directly facilitated by technology of any kind, places a strong focus on what technology has, or seems to be, achieving. At the same time, this marginalizes, or reduces the visibility of, human roles within the academia and beyond (Hayes and Jandrić 2014).

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

"How prepared are engineering educators to implement active learning?" - The emerging findings

This paper discusses the question of how well prepared Engineering Educators are to implement Active Learning approaches within Higher Education undergraduate Engineering Programmes in Malaysia. As the role of Higher Education has shifted from being that of a 'knowledge provider' to become primarily focused upon 'learning facilitation', so the role of teachers or academic staff has changed in that they have become the key to implementing successful Active Learning. Based upon the emergent findings from a case study conducted at a Malaysian institution of higher education, the paper reveals that the engineering educators within the institution concerned were neither prepared nor ready to implement Active Learning. Indeed, it is evident from the study findings that a huge effort is needed in terms of educational policy and practice to ensure that Malaysian institutions offering engineering education should move efficiently and effectively towards the unilateral adoption of Active Learning approaches.

Lecturers' experiences of assessing generic skills in an active learning environment for engineering education in Malaysia - Emergent findings

The aim of this paper is to explore the engineering lecturers' experiences of generic skills assessment within an active learning context in Malaysia. Using a case-study methodology, lecturers' assessment approaches were investigated regarding three generic skills; verbal communication, problem solving and team work. Because of the importance to learning of the assessment of such skills it is this assessment that is discussed. The findings show the lecturers' initial feedback to have been generally lacking in substance, since they have limited knowledge and experience of assessing generic skills. Typical barriers identified during the study included; generic skills not being well defined, inadequate alignment across the engineering curricula and teaching approaches, assessment practices that were too flexible, particular those to do with implementation; and a failure to keep up to date with industrial requirements. The emerging findings of the interviews reinforce the arguments that there is clearly much room for improvement in the present state of generic skills assessment.