Dalveren, Gonca Gökçe Menekşe

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Profile URL
https://hdl.handle.net/20.500.14411/7994
Name Variants
Gonca Gökçe Menekşe, Dalveren
Dalveren, Gonca Gokce Menekse
G., Dalveren
G.,Dalveren
D.,Gonca Gökçe Menekşe
Gonca Gokce Menekse, Dalveren
Dalveren, Gonca Gökçe Menekşe
G.G.M.Dalveren
D.,Gonca Gokce Menekse
D., Gonca Gokce Menekse
Dalveren,G.G.M.
Job Title
Doktor Öğretim Üyesi
Email Address
gonca.menekse@atilim.edu.tr
Main Affiliation
Information Systems Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

2

ZERO HUNGER
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0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

0

Research Products

14

LIFE BELOW WATER
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0

Research Products

6

CLEAN WATER AND SANITATION
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0

Research Products

1

NO POVERTY
NO POVERTY Logo

0

Research Products

5

GENDER EQUALITY
GENDER EQUALITY Logo

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

3

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

0

Research Products

15

LIFE ON LAND
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0

Research Products

10

REDUCED INEQUALITIES
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0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

1

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

0

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

5

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

1

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

0

Research Products

13

CLIMATE ACTION
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0

Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

28

Articles

18

Views / Downloads

127/918

Supervised MSc Theses

3

Supervised PhD Theses

1

WoS Citation Count

196

Scopus Citation Count

273

WoS h-index

9

Scopus h-index

10

Patents

0

Projects

0

WoS Citations per Publication

7.00

Scopus Citations per Publication

9.75

Open Access Source

14

Supervised Theses

4

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JournalCount
IEEE Access5
Sustainability2
2018 International Symposium on Networks, Computers and Communications, ISNCC 2018 -- 2018 International Symposium on Networks, Computers and Communications, ISNCC 2018 -- 19 June 2018 through 21 June 2018 -- Rome -- 1424272
Journal of Eye Movement Research2
Avrupa Bilim ve Teknoloji Dergisi1
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Subject: topic modeling

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 20
    Covid-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, Mohammad
    E-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.
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    Article
    Citation - WoS: 20
    Citation - Scopus: 27
    Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets
    (Mdpi, 2022) Ozyurt, Ozcan; Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, Mohammad
    This 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.
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    Article
    Citation - WoS: 27
    Citation - Scopus: 43
    Digital 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, Mohammad
    The 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.
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    Article
    Citation - WoS: 9
    Citation - Scopus: 20
    Business 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, Mohammad
    The 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.
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    Article
    Citation - WoS: 16
    Citation - Scopus: 26
    Evolution 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, Ahmet
    From 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.