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Article Citation - WoS: 27Citation - Scopus: 43Digital Transformation Strategies, Practices, and Trends: a Large-Scale Retrospective Study Based on Machine Learning(Mdpi, 2023) Gurcan, Fatih; Boztas, Gizem Dilan; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThe purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, "Sustainable Energy", "DT in Health", "E-Government", "DT in Education", and "Supply Chain" emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. "Sustainable Energy" and "Supply Chain" have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.Article Citation - WoS: 53Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation(Athabasca Univ Press, 2021) Gurcan, Fatih; Ozyurt, Ozcan; Cagiltay, Nergiz ErcilE-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics "MOOC," "learning assessment," and "elearning systems" were found to be key topics in the field, with a consistently high volume. In addition, the topics of "learning algorithms," "learning factors," and "adaptive learning" were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly.Conference Object Citation - WoS: 5Citation - Scopus: 7VISUAL AND TEXTUAL FEATURE FUSION FOR AUTOMATIC CUSTOMS TARIFF CLASSIFICATION(Ieee, 2015) Turhan, Bilgehan; Akar, Gozde B.; Turhan, Cigdem; Yuksel, CihanThe Harmonized Tariff Schedule for the classification of goods is a major determinant of customs duties and taxes. The basic HS Code is 6 digits long but can be extended according to the needs of the countries such as application of custom duties based on details of the product. Finding the correct, consistent, legally defensible HS Code is at the heart of Import Compliance. However finding the best code can be complicated, especially in the case of specialized products. In this paper, we propose an automatic HS code detection system based on visual properties of the product together with textual analysis of its labels/explanations. The proposed system first uses morphological parsing in order to extract roots of the words occurring in the textual phrases. Processed text information is further processed by the topic modeling module of the system to find the best matching HS Code definitions within the system. The result of the topic modeling is used to trigger visual search based on quantized local features. The proposed algorithm is evaluated using a database of 4494 Binding Tariffs published in 2014 by the European Union. The results show that accuracy rate above 80 % can be achieved for 4-digit HS Codes.Article Citation - WoS: 16Citation - Scopus: 26Evolution of Software Testing Strategies and Trends: Semantic Content Analysis of Software Research Corpus of the Last 40 Years(Ieee-inst Electrical Electronics Engineers inc, 2022) Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil; Roman, Dumitru; Soylu, AhmetFrom the early days of computer systems to the present, software testing has been considered as a crucial process that directly affects the quality and reliability of software-oriented products and services. Accordingly, there is a huge amount of literature regarding the improvement of software testing approaches. However, there are limited reviews that show the whole picture of the software testing studies covering the topics and trends of the field. This study aims to provide a general figure reflecting topics and trends of software testing by analyzing the majority of software testing articles published in the last 40 years. A semi-automated methodology is developed for the analysis of software testing corpus created from core publication sources. The methodology of the study is based on the implementation of probabilistic topic modeling approach to discover hidden semantic patterns in the 14,684 published articles addressing software testing issues between 1980 and 2019. The results revealed 42 topics of the field, highlighting five software development ages, namely specification, detection, generation, evaluation, and prediction. The recent accelerations of the topics also showed a trend toward prediction-based software testing actions. Additionally, a higher trend on the topics concerning "Security Vulnerability", "Open Source" and "Mobile Application" was identified. This study showed that the current trend of software testing is towards prediction-based testing strategies. Therefore, the findings of this study may provide valuable insights for the industry and software communities to be prepared for the possible changes in the software testing procedures using prediction-based approaches.Article Citation - Scopus: 64Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation(2021) Gurcan,F.; Ozyurt,O.; Cagiltay,N.E.E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “elearning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly. © 2021. International Review of Research in Open and Distance Learning. All Rights Reserved.Article Citation - WoS: 9Citation - Scopus: 20Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data From 2003 To 2023 Using Machine Learning(Mdpi, 2023) Gurcan, Fatih; Ayaz, Ahmet; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThe widespread use of business intelligence products, services, and applications piques the interest of researchers in this field. The interest of researchers in business intelligence increases the number of studies significantly. Identifying domain-specific research patterns and trends is thus a significant research problem. This study employs a topic modeling approach to analyze domain-specific articles in order to identify research patterns and trends in the business intelligence field over the last 20 years. As a result, 36 topics were discovered that reflect the field's research landscape and trends. Topics such as "Organizational Capability", "AI Applications", "Data Mining", "Big Data Analytics", and "Visualization" have recently gained popularity. A systematic taxonomic map was also created, revealing the research background and BI perspectives based on the topics. This study may be useful to researchers and practitioners interested in learning about the most recent developments in the field. Topics generated by topic modeling can also be used to identify gaps in current research or potential future research directions.Article Citation - WoS: 20Citation - Scopus: 27Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets(Mdpi, 2022) Ozyurt, Ozcan; Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThis study aims to investigate up-to-date career opportunities and in-demand competence areas and skill sets for cloud computing (CC), which plays a crucial role in the rapidly developing teleworking environments with the COVID-19 pandemic. In this paper, we conducted a semantic content analysis on 10,161 CC job postings using semi-automated text-mining and probabilistic topic-modeling procedures to discover the competency areas and skill sets as semantic topics. Our findings revealed 22 competency areas and 46 skills, which reflect the interdisciplinary background of CC jobs. The top five competency areas for CC were identified as "Engineering", "Development", "Security", "Architecture", and "Management". Besides, the top three skills emerged as "Communication Skills", "DevOps Tools", and "Software Development". Considering the findings, a competency-skill map was created that illustrates the correlations between CC competency areas and their related skills. Although there are many studies on CC, the competency areas and skill sets required to deal with cloud computing have not yet been empirically studied. Our findings can contribute to CC candidates and professionals, IT organizations, and academic institutions in understanding, evaluating, and developing the competencies and skills needed in the CC industry.Article Citation - WoS: 10Citation - Scopus: 20Covid-19 and E-Learning: an Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic(Ieee-inst Electrical Electronics Engineers inc, 2022) Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, MohammadE-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as "online learn ", "online education ", "online teach " and "distance learn ", as well as trigrams such as "higher education institution ", "emergency remote teach ", "education online learn " and "online teach learn ". Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of "Learning Needs ", "Higher Education " and "Social Impact " respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.Article Citation - WoS: 90Citation - Scopus: 132Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using Lda-Based Topic Modeling(Ieee-inst Electrical Electronics Engineers inc, 2019) Gurcan, Fatih; Cagiltay, Nergiz ErcilSoftware engineering is a data-driven discipline and an integral part of data science. The introduction of big data systems has led to a great transformation in the architecture, methodologies, knowledge domains, and skills related to software engineering. Accordingly, education programs are now required to adapt themselves to up-to-date developments by first identifying the competencies concerning big data software engineering to meet the industrial needs and follow the latest trends. This paper aims to reveal the knowledge domains and skill sets required for big data software engineering and develop a taxonomy by mapping these competencies. A semi-automatic methodology is proposed for the semantic analysis of the textual contents of online job advertisements related to big data software engineering. This methodology uses the latent Dirichlet allocation (LDA), a probabilistic topic-modeling technique to discover the hidden semantic structures from a given textual corpus. The output of this paper is a systematic competency map comprising the essential knowledge domains, skills, and tools for big data software engineering. The findings of this paper are expected to help evaluate and improve IT professionals' vocational knowledge and skills, identify professional roles and competencies in personnel recruitment processes of companies, and meet the skill requirements of the industry through software engineering education programs. Additionally, the proposed model can be extended to blogs, social networks, forums, and other online communities to allow automatic identification of emerging trends and generate contextual tags.Article Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation(2021) Gurcan,F.; Ozyurt,O.; Cagiltay,N.E.E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “elearning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly. © 2021. International Review of Research in Open and Distance Learning. All Rights Reserved.
