Gossip Management at Universities using Big Data Warehouse Model Integrated with a Decision Support System

Big Data has recently been used for many purposes like medicine, marketing and sports. It has helped improve management decisions. However, for almost each case a unique data warehouse should be built to benefit from the merits of data mining and Big Data. Hence, each time we start from scratch to form and build a Big Data Warehouse. In this study, we propose a Big Data Warehouse and a model for universities to be used for information management, to be more specific gossip management. The overall model is a decision support system that may help university administraitons when they are making decisions and also provide them with information or gossips being circulated among students and staff. In the model, unsupervised machine learning algorithms have been employed. A prototype of the proposed system has also been presented in the study. User generated data has been collected from students in order to learn gossips and students’ problems related to school, classes, staff and instructors. The findings and results of the pilot study suggest that social media messages among students may give important clues for the happenings at school and this information may be used for management purposes.The model may be developed and implemented by not only universities but also some other organisations

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Assessment, development and experimental evaluation of self-regulatory support in online learning

Online learning requires a higher level of self-regulation than face-to-face learning. Learners are likely to differ in their cognitive, metacognitive, affective or motivational resources to meet this demand. Individual differences in self-regulation is one major factor contributing to success or failure in online learning, other factors include characteristics of the online learning environment and the complexity of the learning content itself. Lack of self-regulation is likely to affect learners’ engagement with the course content, may result in sub-optimal learning outcomes, including failure to complete the course. A virtual learning assistant has been designed and developed to support online learners. This research aims at ascertaining the effectiveness of providing adaptive assistance in terms of (a) compensatory and (b) developmental effects. Online learners involved in the empirical part of this study (N = 157) were randomised into one of two experimental conditions. For the intervention group, the online learning assistant provided personalised in-browser notifications. This feature was disabled for the learners in the control condition. Results indicate that the adaptive assistance did not result in noticeable developmental shifts in learners’ self-regulation as assessed via conventional self-report measures. However, learners allocated to the intervention group spent less time online per day in first three weeks of being exposed to the adaptive assistance, reduced their time commitment to entertainment websites during first two weeks, and increased their engagement with educational web resources during the first ten days. In addition to the time-varying effects, these compensatory (behavioural) shifts were moderated by learners’ individual differences in personality. The outcome of this study suggests that the utilisation of a virtual learning assistant that provides adaptive assistance can be effective in compensating for not yet developed self-regulatory skills, and subsequently help facilitating success in learning on short online courses