Collaborative Educational Systems

This paper starts describing the key concepts of collaborative systems and the impact of this to educational systems. There are presented the main properties and quality characteristics for the collaborative educational systems. For the main quality characteristic, like portability and complexity are presented different types of indicators for an educational system. The article analyzes different ways to increase the efficiency and the performance level in collaborative educational systems.

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Collaborative Educational Systems in the Virtual Environment

The work leads to an original approach to the construction of collaborative systems metrics. The approach is based both on research already conducted by the author, on the experimental results obtained, and the foundation taken from the specific literature. The collaborative systems in knowledgebased economy are formalized and their characteristics are identified. The virtual campus structure is described and a comparison with the classical university is achieved. The architecture of virtual is designed and the categories of agents in virtual campus are analyzed.

Educational Systems, Intergenerational Mobility and Social Segmentation

We show that the very characteristics of educational systems generate social segmentation. A stylised educational framework is constructed in which everyone receives a compulsory basic education and can subsequently choose between direct working, vocational studies and university. There is a selection for entering the university which consists of a minimum human capital level at the end of basic education. In the model, an individual's human capital depends (i) on her/his parents' human capital, (ii) on her/his schooling time, and (iii) on public expenditure for education. There are three education functions corresponding to each type of study (basic, vocational, university). Divergences in total educational expenditure, in its distribution between the three studies and in the selection severity, combined with the initial distribution of human capital across individuals, can result in very different social segmentations and generate under education traps (situations in which certain dynasties remain unskilled from generation to generation) at the steady state. We finally implement a series of simulations that illustrate these findings in the cases of egalitarian and elitist educational systems. Assuming the same initial distribution of human capital between individuals, we find that the first system results in two-segment stratification, quasi income equality and no under education trap whereas the elitist system generates three segments, significant inequality and a large under education trapEducational systems; intergenerational mobility; social segmentation; under-education trap

Sequence Modelling For Analysing Student Interaction with Educational Systems

The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.Comment: The 10th International Conference on Educational Data Mining 201

Learner-centred Accessibility for Interoperable Web-based Educational Systems

This paper describes the need for an information model and specifications that support a new strategy for delivering accessible computer-based resources to learners based on their specific needs and preferences in the circumstances in which they are operating. The strategy augments the universal accessibility of resources model to enable systems to focus on individual learners and their particular accessibility needs and preferences. A set of specifications known as the AccessForAll specifications is proposed

Work, education and scientific and technological development knowledge and training

A new facet of the productive structures is the accrued importance of the component "knowledge and training". Indeed, knowledge and training become parts of the productive process from the beginning to its marketing, the traditional vocational training being a limited part of the activities regarding knowledge and transfer inside the working place. At the same time, educational systems come more and more to resemble productive systems; first of all, because their employees are, in many countries, the most relevant part of the working force, secondly, because their "products" are quite often evaluated by the market; thirdly, because part of these educational systems are integrated in the productive system itself. As productive systems, educational systems are facing the same problems as any other productive system: skilling and deskilling, introduction of technologies, explosions hierarchization, etc.peer-reviewe

Evaluation of usage patterns for web-based educational systems using web mining

Virtual courses often separate teacher and student physically from one another, resulting in less direct feedback. The evaluation of virtual courses and other computer-supported educational systems is therefore of major importance in order to monitor student progress, guarantee the quality of the course and enhance the learning experience for the student. We present a technique for the usage evaluation of Web-based educational systems focussing on behavioural analysis, which is based on Web mining technologies. Sequential patterns are extracted from Web access logs and compared to expected behaviour

The Nigerian Educational Systems and Returns to Education

While each tier of education has at various times been the concurrent (joint) responsibility of both Federal and state governments, the former has historically been much more involved at the post secondary level. The shares of Federal Government recurrent and capital expenditures by level of education between 1996 and 2002. Over the period, the share for the (24) Federal universities has varied between roughly 40 and 50 percent of total Federal expenditures, while those for the (16) polytechnics and (20) colleges of education have remained fairly constant (apart from one year) at around 17 percent and 11 percent respectively. Overall, during the whole period, the tertiary education sub sector has received between 68 percent and 80 percent of the total Federal expenditures for education.Educational System, Returns to Education, Universities Financing in Nigeria