Iatrellis Omiros

In the era of data-driven decision-making, Higher Education Institutions (HEIs) can greatly benefit from the potential of eXplainable Artificial Intelligence (XAI) to provide transparent and interpretable insights. This paper presents the KONX (CONNECTS) approach, a comprehensive methodology that leverages semantic web technologies to create a dynamic and comprehensive knowledge graph for advanced predictive models in academic advising. The KONX methodology focuses on harmonizing heterogeneous educational data sources, enabling seamless data querying and manipulation. By incorporating a feedback mechanism, the KONX approach remains adaptable to changes in the academic domain, continuously updating and maintaining its knowledge representation. To practically apply and evaluate the proposed methodology, a prototype was implemented and tested on an experimental case study concerning student outcomes prediction. The implemented prototype includes a graphical SPARQL generator interface to streamline the construction of SPARQL queries in an integrated way. In this way, this paper proposes both a comprehensive XAI methodology and a holistic technological infrastructure for applying the methodology in real-time scenarios. By bridging the gap between AI decision-making and human-comprehensible explanations, the KONX approach enhances transparency and user trust in AI-driven systems in the education sector. CCS CONCEPTS • Computing methodologies • Artificial intelligence • Explainable Artificial Intelligence

Students attending Higher Education Institutions (HEIs) are faced with a variety of complex decisions and procedures. To provide students with more sustained and personalized advising, many HEIs turn to online academic advising systems and tools as a way to minimize costs and streamline their advising services. However, in such systems, uncertainty in the learner’s parameters is a factor, which makes the decision-making process more difficult. Fuzzy logic, a multivalued logic similar to human thinking and interpretation, is highly suitable and applicable for developing knowledge-based academic advising systems that conserve the inherent fuzziness in learner models. In this paper, an innovative hybrid software infrastructure is presented which integrates expert system, fuzzy reasoning, and ontological tools to provide reliable recommendations to students for the next appropriate learning step. The software comprises a fuzzy logic component that determines the student’s interest degree for a specific academic choice accompanied by an ontological model and a conventional rule-based expert system for the composition of personalized learning pathways. In order for the system to recommend the next step of the learning pathway, the output of the fuzzy logic component together with the knowledge that is modeled as part of the multi-facet ontology and the machine perceptible academic advising guidelines expressed as semantic rules interoperate in a dynamic and seamless manner. The paper presents the key modeling artifacts of the proposed approach and the architecture of the implemented prototype system. During the case study, the developed system yielded satisfactory results in terms of overall inter-rater reliability and usefulness.

Smart cities are complex ecosystems that use information and communication technologies for helping their citizens and organizations to face the challenges of urbanization, safety, resilience, and sustainability. The SmartDevOps project proposes a framework aiming to address the shortage of professional skills in municipalities. Following the increased use of AI methods to provide recommendations in training, there is also an increased need for formalization of existing courses to enable recommendations. This work is dedicated to the conceptual formal modeling of the delivered courses for gaining the competences required for smart city professionals by following the SmartDevOps methodology. We present the Smart City Competence Ontology (SCCompO) that provides a formalism for modeling concepts like competence, learning objective and outcome of courses that aims to cooperate with MOOC platform for training of Smart City professionals. It follows the modular, extensible structure of the curriculum, and it is designed to respond to questions regarding prerequisites and outcomes of job profiles, competences, and courses. The impact of using the developed ontology is to augment the MOOC platform by providing reasoning on course selection and their learning outcomes as well on the relations among these concepts in order to make decisions about the learners’ curricula.

Purpose

The purpose of this paper is to thoroughly assemble, analyze and synthesize previous research to investigate and identify teaching staff competencies derived from the roles and tasks attributed to university professors.
Design/methodology/approach

In this literature review, the authors looked at both the conceptual framework exploring the educational concepts and the learning theories focusing on teaching staff roles and competencies in higher education. Thirty-nine scientific papers were studied in detail from a total of 102 results, which were eligible based on the preferred reporting items for systematic reviews and meta-analyses statement.
Findings

A multi-dimensional approach to teacher competencies in higher education was proposed, which consists of six main dimensions with their respective characteristics. Thirty-two discrete teaching staff competencies were identified and distributed in the aforementioned dimensions. The research revealed that specific competencies, such as the digital competence of teachers, which have lately become of high importance worldwide due to the COVID-19 pandemic implications, surprisingly, until recently, they were considered secondary in the educational process.
Research limitations/implications

The study was based on the existing literature without using data drawn from an appropriate questionnaire addressed to students and/or interviews with academics. In addition, in an effort to maintain a homogeneous base of teacher competencies, inclusion of domains of expertise was avoided. Further research should focus on designing and developing a holistic model using analytical learning approaches that will contribute to the assessment of teachers’ competencies and explore the relationship of these competencies to students’ academic achievement, contributing quality to higher education.
Practical implications

A specific framework of teacher competencies in higher education, in practice, can be a useful reference point not only for ensuring quality in the selection of teachers and their career-long professional development but also for national education policy strategies. The definition of teacher competencies framework contributes to facilitating effective dialogue for the evaluation and quality assurance in education between agencies, authorities, researchers, teachers, policymakers, education managers and different communities at large.
Social implications

These competencies are at the heart not only of the teaching and learning process but also in the workplace and in society in general and are increasingly recognized as essential. An adequately prepared community and management equipped with the required employee competencies is able to react immediately and in a positive way to any obstacle, yielding optimal results.
Originality/value

This is the first review, to the authors’ knowledge, to comprehensively explore the literature to identify, classify and rank the teaching staff competencies in higher education, revealing the gap between perceived and actual importance of various competencies.

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

In this paper we describe an irrigation scheduling system based on wireless sensor network technology as applied in Precision Agriculture and particularly in Precision Irrigation. Precision agriculture, as opposed to traditional agriculture where the whole field is treated as uniform and homogeneous, is a modern method of agricultural practice where temporal and spatial information regarding the field and its parameters is utilized in order to optimize the efficiency of influxes (such as water and fertilizers), while minimizing the environmental effects of their use. Irrigation is a key agricultural process with direct consequences on the crop's yield and quality as well as on the environment, with soil humidity (water content of field) being a very important parameter in composing the appropriate irrigation routine for a field. Until recently, in order to measure soil humidity a farmer had to use practices that are both time and money consuming (e.g. use probes in various field...

Covid-19 epidemic has created new challenges for the development of Smart and Sustainable Cities. It has proven that it is not anymore sufficient just to focus on providing services for quality of life, or for a better business ecosystems, but we need to prepare cities, so they are able to manage, adapt, maintain and ensure city services and enhance quality of life in the face of hazards, shocks and stresses (ISO 37123). According to this definition resilience does not include only earthquakes, fires, floods, etc. but as well whatever disrupts significantly the operation of a city either occasionally or periodically. Examples include high unemployment;  endemic  violence;  health epidemics and  chronic  food  and  water  shortages (Cities, 2016)
Even though some standards and projects exist in this area, we have not yet consensus on a common city resilience model that will able to describe what exactly constitutes resilience and a resilient city (Spaans, & Waterhout,  2017).  Furthermore, up to now little emphasis has been given to the way municipalities are organized for addressing hazards and even less on training their personnel to the new skills required. Currently, these new required job profiles do not exist, they are overlooked, or they are partially described.
Rockefeller Foundation, founded in 2013 the “100 Resilient Cities (100RC)” project having as objective to help cities face three major threats and challenges: urbanization, globalization, and climate change. In the context of this project, a job profile named “City Chief Resilience Officer” was defined, but without sufficiently describing the required skills. In parallel, other projects e.g. “Smart DevOps competencies for smart cities” (devops.uth.gr) are attempting to define the required skills and job profiles needed for Smart and Sustainable Cities professionals (Kaufmann, 2020).
Obviously, we need to address the skills’ gap between today’s and future’s skills demands of municipal workforce by emphasizing on these emerging needs and by combining the needs for smart and resilient cities development. Exactly on this subject area,  this paper presents the results of a survey that attempts to define the required skills for a “Smart and Resilience City Officers”.

The present research work proposes the development of an integrated framework for the personalization and parameterization of learning pathways, aiming at optimizing the quality of the offered services by the Higher Educational Institutions (HEI). In order to achieve this goal, in addition to the educational part, the EDUC8 framework encloses the set of parameters that cover both the technical and the financial dimensions of a learning pathway, thus providing a complete tool for the optimization and calculation of the offered services by the HEIs in combination with the minimization of respective costs. Moreover, the proposed framework incorporates simulation modeling along with machine learning for the purpose of designing learning pathways and evaluating quality assurance indicators and the return on investment of implementation. The study presents a case study in relation to tertiary education in Greece, with a particular focus on Computer Science programs. Data clustering is specifically applied to learn potential insights pertaining to student characteristics, education factors and outcomes. Generally, the framework is conceived to provide a systematic approach for developing tertiary policies that help optimize the quality and cost of education.

It is well known that in a turbo decoder extrinsic information increases with every iteration. In published literature it is shown that there are different techniques which improve the performance of Soft Output Viterbi Algorithm (SOVA) and max-log-Maximum A Posteriori (MAP) turbo decoding algorithms by applying a scaling factor at the extrinsic information. Most of these techniques give good Bit Error Rate (BER) and Frame Error Rate (FER) performance results, but the drawback is increased complexity for the turbo decoder. Following well known techniques and using 3rd Generation Partnership Projext (3GPP) parameters for flat Rayleigh fading channels, this paper shows that for a reconfigurable SOVA/log-MAP turbo decoder, a common constant scaling factor can improve BER and FER performance significantly.

The improvement in quality of services offered by educational institutions is one of the main challenges of the modern education informatics. The personalization of educational process requires adaptive learning schemes since the student status and conditions inside an institution constantly change. In this paper, we present the EDUC8 (EDUCATE) system, which aims at providing a new approach concerning real-time personalization and adaptation of learning business processes. The EDUC8 system consists of a learning process execution engine supported by a semantic framework, which is based on an ontology enclosing the knowledge and the information and provides decisions and recommendations for the next steps of the learning process. Furthermore, the results of the rule-set execution may produce new objects that will be inserted in the ontology as new knowledge.

Information technology has the potential to greatly improve the quality of services offered by educational institutions. Personalized learning requires adaptive learning schemes since the student status and conditions inside an institution constantly change. In this paper, we present the EDUC8 (EDUCATE) system, which aims at providing a new approach concerning real-time personalization and adaptation of learning business processes. The EDUC8 system consists of a learning process execution engine supported by a semantic framework, which is based on an ontology enclosing the knowledge and the information and provides decisions and recommendations for the next steps of the learning process. Moreover, the results of the rule-set execution may create new objects that will be stored in the ontology as new knowledge.

During the last decade, educational institutions are implementing reforms geared towards improving student outcomes and quality of studies. Personalization of education services is one of the challenges to be confronted. However, personalization requires continuous reconfiguration of the learning schemes since the academic status of each student, program studies and circumstances inside an educational institution constantly change. In this paper, we present EDUC8 (EDUCATE) prototype that provides an information technology solution concerning the dynamic recommendation and execution of personalized education processes. The EDUC8 prototype comprises an educational process execution engine based on a semantic infrastructure for reconfiguring the learning pathways for each student. The semantic infrastructure consists of an ontological framework enclosing the required knowledge and a semantic …

This article aims to provide the reader with a comprehensive background for understanding current knowledge and research works on ontologies for software project management (SPM). It constitutes a systematic literature review behind key objectives of the potential adoption of ontologies in PM. Ontology development and engineering could facilitate substantially the software development process and improve knowledge management, software and artifacts reusability, internal consistency within project management processes of various phases of software life cycle. The authors examined the literature focusing on software project management ontologies and analyzed the findings of these published papers and categorized them accordingly. They used qualitative methods to evaluate and interpret findings of the collected studies. The literature review, among others, has highlighted lack of standardization in …

One of the main challenges to be confronted in Higher Education, so as to increase quality, is the personalization of education services, since each student constitutes a unique case. In this paper, we present the conceptualization of the domain of Learning Pathways in Higher Education. We present the EDUC8 (EDUCATE) ontology, which models the needed domain knowledge streams for the learning pathways and consists of four (4) main modules: 1) the learner model 2) the learning pathway model 3) the business model and 4) the quality assurance model. Taking into account the multifaceted nature of a learning pathway in a Higher Educational Institution (HEI), our proposal achieves a holistic conceptualization of the domain of educational provision, in order to be further utilized for the implementation of a Semantic Web Rules repository. This rule base is in control of the required streams of knowledge …

One of the main challenges to be confronted by modern tertiary sector, so as to improve quality is the personalization of learning, which has to be combined with a minimization of the respective costs. However, personalization requires continuous reconfiguration of the academic plans since the academic status of each student, educational options and circumstances inside a Higher Educational Institution constantly change. In this paper, we present EDUC8 (EDUCATE) software environment that provides an integrated information technology solution concerning the dynamic recommendation and execution of personalized education processes. The implemented EDUC8 prototype aggregates a process execution engine, a rule engine and a semantic infrastructure for reconfiguring the learning pathways for each student. The semantic infrastructure consists of an ontology enclosing the required knowledge …

The present PhD thesis proposes the development of an integrated framework for the personalization and parameterization of learning pathways, aiming at optimizing the quality of the offered services by the Higher Educational Institutions (HEI). In order to achieve this goal, the way in which learning pathways can be transformed from an abstract concept into a model, quality and efficiency improvement process and final product for a HEI is described with the learner being in the center of this attempt. The proposed framework EDUC8 (EDUCATE) aims to address the problems of existing research studies, which are not sufficiently focusing on the dynamic evolution of the “circumstances” inside a HEI, the academic status, interests and needs of individual learners as well as the available educational options themselves. Thus, it leverages the recommendation and execution of self-evolving learning pathways, in a way …

Difficulty in linking data and models across organizations is one of the barriers to be overcome in developing integrated decision-making systems since not all models exist in the same location. OpenMI is a popular standard for coupling spatially and temporarily hydrological models but it requires that all involved models exist on the same machine. In this paper we present a Web Services based collaborative framework to couple hydrological models. This is achieved by converting the interface of the OpenMI configuration to be webbased and to remotely invoke the computational engines of models. Our case study shows the remote linking of a water balance model and a reservoir model applied for the reservoir of the restored Lake Karla in Thessaly, Greece. The results show that the collaboration process is not affected by the communication overhead introduced and it is bounded by the time, space and optimization characteristics of the coupled models.

One of the main challenges to be confronted by Higher Educational Institutions (HEI), so as to increase quality, is the provision of personalized education services in a wide range of educational settings, often beyond the course sequences historically offered to students. However, this personalization requires the continuous reconfiguration and adaptation of the selected academic plans, since each student is a unique case and both the educational options and current circumstances inside an educational institution change rapidly. In this paper, we present an innovative software environment offered to the academic staff and personnel that provides an integrated information technology solution concerning the dynamic and personalized composition of students’ learning pathways during execution phase. The software environment comprises a process execution engine based on a semantic infrastructure …

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Academic Advising Systems (AAS) and its impact on learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of AAS in generic educational strategic planning. The researchers examined the literature on experimental case studies conducted in the domain during the past ten years (2008–2017). Search terms identified 98 mature pieces of research work, but inclusion criteria limited the key studies to 43. The authors analyzed the research questions, methodology, and findings of these published papers and categorized them accordingly. The results have highlighted three distinct major directions of the AAS empirical research. This paper discusses the emerged added value of AAS research and highlights the significance of further implications. Finally, the authors set their thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.