Athens Journal of Τechnology & Engineering最新文献

{"title":"Management Strategies for Engineering Faculties under Consideration of Current Developments in the Higher Education Sector","authors":"U. Quapp, K. Holschemacher","doi":"10.30958/ajte.9-2-4","DOIUrl":"https://doi.org/10.30958/ajte.9-2-4","url":null,"abstract":"Faculty management organizes the effective operation of a faculty and is responsible for all occurring problems. As the work will be done mainly behind the scenes, faculty management’s influence on performance and development of a faculty often is underestimated. The challenge for faculty managers lies in balancing the conflict between governing and supporting faculty members while being in an uncomfortable sandwich position – between the central university administration and the faculty members. The big ambition of administration should be to appear “invisible” for university staff, to work efficiently and to avoid a waste of faculty resources. Nevertheless, administration has a strong positon in a higher education institution. Its decisions about resources and facilities are able to influence teaching and research to advantage or disadvantage of a faculty. The paper explains typical tasks of faculty management and shows in which way it influences engineering teaching and research by using examples from the daily working practice. Additionally, author gives advices how to improve faculty administration at engineering faculties. Efficient faculty management can contribute to teaching and research immensely, and, as a result, decide about success or failure of faculty performance. Keywords: faculty management, administration, engineering education and research","PeriodicalId":197899,"journal":{"name":"Athens Journal of Τechnology & Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128376696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital University: Investigating the Impact of the Pandemic on the Acceptance of E-Learning","authors":"Tilia Stingl de Vasconcelos Guedes, Jasmin Séra","doi":"10.30958/ajte.9-2-2","DOIUrl":"https://doi.org/10.30958/ajte.9-2-2","url":null,"abstract":"This article explores a comparative study on the Digitalization in Teaching conducted by the FHWien der WKW (FHW) at the very beginning of the pandemic, with a follow-up one year later, after the complete changeover to distance learning. The study investigated behaviour and preferences of students and teaching staff as linked to their experience with digital tools both initially and after that year. The results were compared to the results of similar studies, focusing on answering the question about the impact of digital education on the acceptance of the digital tools and processes. This paper presents the findings of the FHW study examining the acceptance or rejection of e-learning by students and teaching staff by exploring their needs, questions, and requests. The research uses acceptance theory in its theoretical underpinnings. Its methodology consists of a quantitative survey of students and teaching staff, as well as the review of studies on related topics. The outcome of this study shows that, after a year of being forced to work with digital tools, attitudes among students and teaching staff generally became more accepting and shifts in their needs and requests could be observed. Keywords: distance learning, digital tools, post-secondary education, e-learning, acceptance","PeriodicalId":197899,"journal":{"name":"Athens Journal of Τechnology & Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127669851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Didactic Connection between Spreadsheet and Teaching Programming","authors":"Gábor Törley, László Zsakó, P. Bernát","doi":"10.30958/ajte.9-2-1","DOIUrl":"https://doi.org/10.30958/ajte.9-2-1","url":null,"abstract":"When we talk about problem-solving skills, then, generally, programming comes to our minds as an activity that can develop algorithmic thinking and abstraction. Regarding the spreadsheet, the software application area could be our first, and mathematics could be our second thought. When spreadsheets and programming are mentioned together, programming of macros is in focus, which is in fact programming. In this paper, we want to focus on how these two areas impact each other, and we want to emphasize that the spreadsheet is an efficient tool to develop algorithmic thinking. Moreover, there is more “crosstalk” between these two tools. This paper will show through examples that there is a two-way connection between spreadsheet and programming; that is why it can be useful to build the concepts of these two topics mutually on each other. Keywords: spreadsheet, programming, problem solving, algorithmic thinking, teaching methodologies","PeriodicalId":197899,"journal":{"name":"Athens Journal of Τechnology & Engineering","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124436050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies","authors":"T. Jurić","doi":"10.1101/2022.03.15.22272428","DOIUrl":"https://doi.org/10.1101/2022.03.15.22272428","url":null,"abstract":"Background: This paper shows that Big Data and the so-called tools of digital demography, such as Google Trends (GT) and insights from social networks such as Instagram, Twitter and Facebook, can be useful for determining, estimating, and predicting the forced migration flows to the EU caused by the war in Ukraine. Objective: The objective of this study was to test the usefulness of Google Trends indexes to predict further forced migration from Ukraine to the EU (mainly to Germany) and gain demographic insights from social networks into the age and gender structure of refugees. Methods: The primary methodological concept of our approach is to monitor the digital trace of Internet searches in Ukrainian, Russian and English with the Google Trends analytical tool (trends.google.com). Initially, keywords were chosen that are most predictive, specific, and common enough to predict the forced migration from Ukraine. We requested the data before and during the war outbreak and divided the keyword frequency for each migration-related query to standardise the data. We compared this search frequency index with official statistics from UNHCR to prove the significations of results and correlations and test the models predictive potential. Since UNHCR does not yet have complete data on the demographic structure of refugees, to fill this gap, we used three other alternative Big Data sources: Facebook, Twitter and Instagram. Results: All tested migration-related search queries about emigration planning from Ukraine show the positive linear association between Google index and data from official UNHCR statistics; R2 = 0.1211 for searches in Russian and R2 = 0.1831 for searches in Ukrainian. It is noticed that Ukrainians use the Russian language more often to search for terms than Ukrainian. Increase in migration-related search activities in Ukraine such as [gcy][p][a][ncy][icy][tscy][a] (Rus. border), [kcy][o][p][dcy][o][ncy][u] (Ukr. border); [Pcy][o][lcy][softcy][shchcy][a] (Poland); [Gcy][e][p][m][a][ncy][icy][yacy] (Rus. Germany), [H]i[m][e][chcy][chcy][icy][ncy][a] (Ukr. Germany) and [U][gcy][o][p][shchcy][icy][ncy][a] and [V][e][ncy][gcy][p][icy][yacy] (Hungary) correlate strongly with officially UNHCR data for externally displaced persons from Ukraine. All three languages show that the interest in Poland is the highest. When refugees arrive in nearby countries, the search for terms related to Germany, such as crossing the border + Germany, etc., is proliferating. This result confirms our hypothesis that one-third of all refugees will cross into Germany. According to Big Data insights, the estimate of the total number of expected refugees is to expect 5,4 Million refugees. The age group most represented is between 24 and 45 years (data for children are unavailable), and over 65% are women. Conclusion: The increase in migration-related search queries is correlated with the rise in the number of refugees from Ukraine in the EU. Thus this method allows reliab","PeriodicalId":197899,"journal":{"name":"Athens Journal of Τechnology & Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131118098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Digital Humanities for Understanding COVID-19: Lessons from Digital History about earlier Coronavirus Pandemic","authors":"T. Jurić","doi":"10.1101/2022.02.02.22270333","DOIUrl":"https://doi.org/10.1101/2022.02.02.22270333","url":null,"abstract":"Background: At the time of the COVID-19 epidemic, it is useful to look at what lessons (digital) history can give us about the past pandemics and dealing with them. We show that the Google Ngram (GNV) can discover hidden patterns in history and, therefore, can be used as a window into history. By using the approach of Digital Humanities, we analysed the epidemiological literature on the development of the Russian flu pandemic for hints on how the COVID-19 might develop in the following years. Objective: Our study is searching for evidence that the COVID-19 is not a unique phenomenon in human history. We are testing the hypothesis that the flu-like illness that caused loss of taste and smell in the late 19th century (Russian flu) was caused by a coronavirus. We are aware that it is difficult to formulate a hypothesis for a microbiological aetiology of a pandemic that occurred 133 years ago. But differentiating an influenza virus infection from a COVID-19 patient purely on the clinical ground is difficult for a physician because the symptoms overlap. The most crucial observation of similarities between the Russian flu pandemic and COVID-19 is the loss of smell and taste (anosmia and ageusia). The objective was to calculate the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19. Methods: The primary methodological concept of our approach is to analyse the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19 with the Google NGram analytical tool. Initially, keywords were chosen that are specific and common for the Russian flu and COVID-19. We show the graphic display on the Y-axis what percentage of words in the selected corpus of books (collective memory) over the years (X-axis) make up the word. To standardise the data, we requested the data from 1800 to 2019 in English, German and Russian (to 2012) book corpora and focused on the ten years before, during and after the outbreak of the Russian flu. We compared this frequency index with non-epidemic periods to test the model analytical potential and prove the signification of the results. Results: The COVID-19 is not a unique phenomenon because the Russian flu was probably the coronavirus infection. Results show that all the three analysed book corpora (including newspapers and magazines) show the increase in the mention of the symptoms loss of smell and loss of taste during the Russian flu (1889-1891), which are today undoubtedly proven to be key symptoms of COVID-19. In the English corpus, the frequency rose from 0.0000040433 % in 1880 to 0.0000047123 % in 1889. The frequency fell sharply after the pandemic stopped in 1900 (0.0000033861%). In the Russian corpus, the frequency rises from 0 % in 1880 to 0.0000004682 % in 1889 and decreased rapidly after the pandemic (1900 = 0.0000011834 %). In the German corpus, t","PeriodicalId":197899,"journal":{"name":"Athens Journal of Τechnology & Engineering","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131011039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}