We'll performing necessary maintenance on the EDUC8 platform on Sunday, May 31. As a result, the platform will not be accessible between the hours of 8:00am and 12.00 pm on that date.
EDUC8
Personalized and self-evolving learning pathways
Integrated software infrastructure
A core challenge faced by the Higher Education sector is at the same time to improve the educational services while reducing the organizational and financial costs of education. Today a learning pathway in Higher Education nearly always implies an expansion of educational options beyond the course sequences historically offered to students so as to include multi-facet educational experiences, that occur outside of traditional classroom settings or HEI buildings, such as summer schools, independent research projects, online classes, internships, student exchange programs, or dual-enrollment experiences. While a learning pathway may encompass a wide variety of educational experiences in diverse settings, these experiences impose challenges to HEIs so as to provide support and guidance to students at key decision points or exceptional situations that require appropriate modifications/reconfigurations of the academic plans of a student, thus increasing the flexibility of the learning processes. A possible solution to this challenge is the utilization of personalized academic procedures alongside with (more...)
their modeling while covering all the possible subdomains of higher education. The provision of standardized, and as much as possible, personalized higher education services can yield tremendous benefits from the economies of scale that can be achieved both in terms of tangible as well as of intangible resources of an institution. In this regard, a research team in UTH university is performing research activities regarding self-evolving learning pathways for the provision of highly personalized higher education learning schemes based on the domain knowledge of the complete of stakeholders inside a HEI. More specifically, in order to address the abovementioned challenges, the team has designed an integrated software infrastructure that leverages the provision of personalized education services by the utilization of machine learning techniques and semantic web technologies.
Technical architecture
Under EDUC8 pathways, a semantic web-technologies, machine-learning based infrastucture supports the reengineering of learning pathways.
Semantic model
The integrated software infrastructure utilizes the EDUC8 semantic model in conjunction with the semantic rules defined so as to model the pathways both in terms of academic knowledge enclosed as well as in terms of business processes.
EDUC8 can be parameterized to serve various educational and business needs and scenarios. Machine learning models together with modeling and simulation are utilized to conceptualize the learning pathway organization and system’s future behavior.
The EDUC8 Project
The timeline for the development phases of the EDUC8 is illustrated below:
Technical details
The present proposal proposes the incorporation of an integrated ICT based solution (more...)
regarding the modeling and the dynamic adjustment of the Higher Education procedures of student learning pathways tightly integrated with machine learning and semantic web technologies, aiming at optimizing the quality of the offered services by the Higher Educational Institutions (HEI). The cornerstone of the implemented software environment, is the EDUC8 semantic model which realizes the appropriate semantics so as to model the pathways both in terms of academic knowledge enclosed as well as in terms of business processes. The EDUC8 Ontology models the needed domain knowledge streams for learning pathways in four (4) main domains (Fig. 1): a) the learner domain, b) the learning pathway, c) the organizational domain, and d) the quality assurance domain. Another important attribute to the technical architecture (Fig. 2) of the implemented prototype is the unsupervised machine learning mechanism, which iteratively revises the patterns of educational processes and provides HEI decision makers with alternatives to understand student parameters, learning trajectories and numbers in the age of "big data".
EDUC8 comprises a software platform that can offer personalization of the education plans for every single student. EDUC8 main technical highlights could be summarized below:
Dynamic composition of learning pathways: the EDUC8 approach is based on continuous reasoning over the existing knowledge (represented by a respective rule set), in order to recommend and execute the most appropriate “next” sub-process of the education plan under execution. Rule-based matching of recommendations to student's parameters, preferences and goals and their tight integration with workflows increase the likelihood that the provided advice by EDUC8 will be followed. The innovation feature of EDUC8 approach is the fact that the learning pathway is being composed during its execution, as the platform recommends, combines and executes sub-processes stored inside a repository;
EDUC8 rule base: the integrated EDUC8 environment encloses a rule-set repository created in order to ensure the interoperation between the rule base and the semantic model. The rule base is able to produce new facts and update the semantic model accordingly, thus creating new knowledge as each pathway evolves and concludes.
Learning pathway rule generator: taking under consideration that one of the most vital components of the software infrastructure is the rule set repository since it encloses the required knowledge assets, the UTH team designed and implemented a software infrastructure that could facilitate its continuous maintenance by the domain experts in an integrated way.
Integrated software environment: the design and implementation of EDUC8 environment is based on the assumption that that a HEI supports different fields of study and contains faculty mentors, academic advisors, managerial and administrative personnel with various specialties and expertise, thus comprising a multidisciplinary team with different needs and know-how. EDUC8 integrated software infrastructure offers a unified interface to all the participating actors (faculty, academic advisors, administrative and managerial personnel) covering their personal experience, their task participation and the multi-facet nature of the learning pathways.
The properties of interoperability and extensibility are ensured by utilizing widely accepted standards regarding the implementation of the EDUC8 platform, while at the same time the technical decisions were heavily influenced by the best practices that are proposed by experts in the software engineering domain.
Functional Features
EDUC8 software framework covers the complete lifecycle of learning pathways (both design and execution mode) by offering a toolkit incorporating a set of functional features (more...)
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Personalization: Personalized learning pathways in higher education should be designed in a way that learners are being educated in a unique way, based on their personal parameters and performance to the academic plan offered to them. Personalization requires continuous reconfiguration of the academic plans since the academic status and parameters of each learner constantly change. The personalization is empowered by the design and deployment of a semantic information structure that provides assistive decision-making.
Maintenance: From HEI's perspective, learning pathways reflect on the academic, on the techno-economic and on the administrative part of the higher education business processes. They must therefore be easily maintained so that the academic part follows the modern educational trends and the administrative business processes respond to the changes that may arise in the HEIs. EDUC8 encloses a semantic model utilized for the representation of the various domains of knowledge required, as well as the creation of a set of semantic rules. EDUC8 software infrastructure offers the required UIs to the academic personnel in order to maintain and update the stored knowledge and experience
Academic advising guidelines formalization: The formalization of academic advising guidelines is being performed in a specific and per case manner. Their formalization is required since their parameters will be able to be handled by an IT infrastructure that supports their execution. EDUC8 provides the framework for the formalization of the learning pathways in a machine-interpretable manner, so as to be maintained and utilized by an IT platform. The resulting guidelines can be used as a valuable evolving enchiridion by the academic advisors to provide academic orientation and guidance.
Learning pathways modeling: The modeling of learning pathways lacks a formal structure. Different approaches exist in the area of modeling. Their interoperation could be of major significance since the learning pathway exchange between HEIs could optimize the mobility and respective education plans. EDUC8 utilizes the established semantic model in conjunction with the semantic rules defined so as to model the pathways both in terms of business processes as well as in terms of knowledge enclosed. Moreover, the framework utilizes machine learning as an assistive artifact within the process of conceptual modeling.
Knowledge evolution: Knowledge evolution is also a core challenge since the higher education domain encloses a rapidly evolving domain, which requires corresponding changes in decision-making. In any execution cycle, new knowledge and facts originated from both the rule base and the learning pathway metamodels that are established during their execution are created, which constitute the evolving knowledge base of EDUC8 platform.
