Purpose: The purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and ameliorate the learning process for both students and faculty.Design/methodology/approach: In the presented research, data from the online services, provided by a Greek University, prior, during and after the COVID-19 outbreak, are analyzed and utilized in order to ameliorate the offered learning process and provide better quality services to the students. Moreover, according to the learning paths, their presence online and their participation in the services of the University, insights can be derived for their performance, so as to better support and assist them.Findings: The system can deduce the future learning progression of each student, according to the past and the current performance. As a direct consequence, the exploitation of the data can provide a road map for the strategic planning of universities, can indicate how the learning process can be updated and amended, both online and in person, as well as make the learning experience more essential, effective and efficient for the students and aiding the professors to provide a more meaningful and to-the-point learning experience.Originality/value: Nowadays, educational activities in academia are strongly supported by online services, information systems and online educational materials. The learning design in the academic setting is primarily facilitated in the University premises. However, the exploitation of the contemporary technologies and supporting materials that are available online can enrich and transform the educational process and its results.

Recommendations systems have become an important tool for scientists because they simplify the process of discovering related work, without getting distracted by the enormous amount of research publications. Academic research on recommendations systems can be classified on two main categories: the content-based filtering approach, where the papers published by the authors are indexed and the TF-IDF algorithm is applied to calculate the weights for each one of the indexed terms and the collaboration filtering, in which authors are considered to prefer similar publications with authors with akin behavior. In the proposed method, we present a hybrid approach using both of the before mentioned approaches. We contribute to the collaborative filtering by implementing graph based analysis in order to define the importance of each indexed term.

The article describes the academic data, which derived from a University E-government analytic platform, which supports the facilitation of blended learning in a Greek University during and after the COVID19 outbreak [1,2]. University e-government services refer to a set of information systems that facilitate the functionalities of the University and enable the management of the underlying information [3,4]. These educational, research and managerial services, also called U-EGOV, follow the four stages of e-government (Presence, Interaction, Transaction, Transformation) [5]. In the presented approach, the data was aggregated from the university services with an automated process and includes all the individual U-EGOV services, that is the synchronous and asynchronous educational platforms, the teleconferencing tool, etc. The dataset created contains information about the courses, the assignments, the grades, the examinations, as well as other significant academic elements of the synchronous and the asynchronous education that takes place in the University. The analysis spans from the spring semester of the academic year 2019–2020, the winter semester of the academic year 2020–2021 to the spring semester of 2020–2021 (three semesters in total). The sample consists of 4800 records and after the preprocessing 4765 records (statistics of courses attended by students) which include 1661 unique students within the university in twenty (20) courses. We have followed an educational data mining approach on the collected data by utilizing an automated data aggregation mechanism to gather data for the selected courses, in order to enhance the learning process and the quality of services. The dataset can be reused: i) as a reference point to measure the quality of the academic outputs and its progress through the years and ii) as a basis for similar analysis in other Higher Educational Institutions (HEIs).

Purpose: The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain. Design/methodology/approach: The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics. Findings: The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem. Research limitations/implications: This approach is suitable for application domains that are ranked on the basis of multiple criteria. Originality/value: The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

Academia is a complex socio-technical system with multiple aspects and constituents that involve various stakeholders. In order to address stakeholders’ needs and to assist the institutional accountability, this complexity should be considered during the development of academic services. We have designed a dynamic multidimensional ranking approach, easily modifiable to address user requirements, so as to assess and compare the university performance with a clear view to the support of effective institutional strategic planning and policy making. Our approach comprises the following components: the AcademIS ontology to model the academic domain and its multiple dimensions, the AcademIS Information System to manage and display the academic information, published in Linked Open Data format and the visual-aided Multiple Criteria Decision Making component, to evaluate and rank the performance of the academic units. The data are aggregated from several sources, in different formats, LODified by our system, and presented to the user by the interface to ultimately assist the decision making process.