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Çağıltay, Nergiz
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Name Variants
Çağıltay, Nergis E.
Nergiz, Çağıltay
Çağıltay, Nergiz
Nergiz, Cagiltay
Ç.,Nergiz
C.,Nergiz
N., Cagiltay
N.,Çağıltay
Cagiltay, Nergiz
Cagiltay,N.
Çağıltay,N.
N.,Cagiltay
C., Nergiz
Çağıltay, Nergiz Ercil
Cagiltay, Nergiz Ercil
Çağıltay, Nergiz E.
Nergiz, Çağıltay
Çağıltay, Nergiz
Nergiz, Cagiltay
Ç.,Nergiz
C.,Nergiz
N., Cagiltay
N.,Çağıltay
Cagiltay, Nergiz
Cagiltay,N.
Çağıltay,N.
N.,Cagiltay
C., Nergiz
Çağıltay, Nergiz Ercil
Cagiltay, Nergiz Ercil
Çağıltay, Nergiz E.
Job Title
Profesör Doktor
Email Address
nergiz.cagiltay@atilim.edu.tr
Main Affiliation
Software Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
4
QUALITY EDUCATION
33
Research Products
7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
3
Research Products
17
PARTNERSHIPS FOR THE GOALS
3
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
137
Articles
79
Views / Downloads
544/3630
Supervised MSc Theses
26
Supervised PhD Theses
3
WoS Citation Count
1157
Scopus Citation Count
1469
WoS h-index
17
Scopus h-index
20
Patents
0
Projects
0
WoS Citations per Publication
8.45
Scopus Citations per Publication
10.72
Open Access Source
17
Supervised Theses
29
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| 18th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication -- SEP 03-07, 2007 -- Athens, GREECE | 7 |
| IEEE Access | 5 |
| Computers & Education | 3 |
| International Journal of Human–Computer Interaction | 3 |
| Surgical Innovation | 3 |
Current Page: 1 / 10
Scopus Quartile Distribution
Competency Cloud
15 results
Scholarly Output Search Results
Now showing 1 - 10 of 15
Article Citation - WoS: 22Citation - Scopus: 34An Analysis of Course Characteristics, Learner Characteristics, and Certification Rates in MITx MOOCs(Athabasca Univ Press, 2020) Cagiltay, Nergiz Ercil; Cagiltay, Kursat; Celik, Berkan; Software EngineeringMassive Open Online Courses (MOOCs), capable of providing free (or low cost) courses for millions of learners anytime and anywhere, have gained the attention of researchers, educational institutions, and learners worldwide. Even though they provide several benefits, there are still some criticisms of MOOCs. For instance, MOOCs' high dropout rates or predominantly elite participation are considered to be important problems. In order to develop solutions for these problems, a deeper understanding of MOOCs is required. Today, despite the availability of several research studies about MOOCs, there is a shortage of in-depth research on course characteristics, learner characteristics, and predictors of certification rates. This study examined MOOC and learner characteristics in detail and explored the predictors of course certification rates based on data from 122 Massachusetts Institute of Technology MOOCs (MITx) on edX platform as well as data about the 2.8 million participants registered in these MOOCs. The results indicated that as the number of courses offered and the number of learners enrolled increased in years, there was a decrease in the certification rates among enrolled learners. According to our results, the number of average chapters completed, total forum messages, and mean age predicted course certification rates positively. On the other hand, the total number of chapters in a course predicted the course certification rates negatively. Based on these results, shorter and more interactive MOOCs are recommended by considering the needs of the learners, course content design, and strategies encouraging the enrolled students to enter the courses.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.Article Citation - WoS: 20Citation - Scopus: 25Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling(Ieee-inst Electrical Electronics Engineers inc, 2022) Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil; Soylu, AhmetThe landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today's research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.Article Citation - WoS: 10Citation - Scopus: 12Distinguishing Intermediate and Novice Surgeons by Eye Movements(Frontiers Media Sa, 2020) Menekse Dalveren, Gonca Gokce; Cagiltay, Nergiz ErcilSurgical skill-level assessment is key to collecting the required feedback and adapting the educational programs accordingly. Currently, these assessments for the minimal invasive surgery programs are primarily based on subjective methods, and there is no consensus on skill level classifications. One of the most detailed of these classifications categorize skill levels as beginner, novice, intermediate, sub-expert, and expert. To properly integrate skill assessment into minimal invasive surgical education programs and provide skill-based training alternatives, it is necessary to classify the skill levels in as detailed a way as possible and identify the differences between all skill levels in an objective manner. Yet, despite the existence of very encouraging results in the literature, most of the studies have been conducted to better understand the differences between novice and expert surgical skill levels leaving out the other crucial skill levels between them. Additionally, there are very limited studies by considering the eye-movement behaviors of surgical residents. To this end, the present study attempted to distinguish novice- and intermediate-level surgical residents based on their eye movements. The eye-movement data was recorded from 23 volunteer surgical residents while they were performing four computer-based simulated surgical tasks under different hand conditions. The data was analyzed using logistic regression to estimate the skill levels of both groups. The best results of the estimation revealing a 91.3% recognition rate of predicting novice and intermediate surgical residents on one scenario were selected from four under the dominant hand condition. These results show that the eye-movements can be potentially used to identify surgeons with intermediate and novice skills. However, the results also indicate that the order in which the scenarios are provided, and the design of the scenario, the tasks, and their appropriateness with the skill levels of the participants are all critical factors to be considered in improving the estimation ratio, and hence require thorough assessment for future research.Review Citation - WoS: 7Citation - Scopus: 7A Systematic Review on Classification and Assessment of Surgical Skill Levels for Simulation-Based Training Programs(Elsevier Ireland Ltd, 2023) Tonbul, Gokcen; Topalli, Damla; Cagiltay, Nergiz ErcilBackground: Nowadays, advances in medical informatics have made minimally invasive surgery (MIS) procedures the preferred choice. However, there are several problems with the education programs in terms of surgical skill acquisition. For instance, defining and objectively measuring surgical skill levels is a challenging process. Accordingly, the aim of this study is to conduct a literature review for an investigation of the current approaches for classifying the surgical skill levels and for identifying the skill training tools and measurement methods.Materials and Methods: In this research, a search is conducted and a corpus is created. Exclusion and inclusion criteria are applied by limiting the number of articles based on surgical education, training approximations, hand movements, and endoscopic or laparoscopic operations. To satisfy these criteria, 57 articles are included in the corpus of this study.Results: Currently used surgical skill assessment approaches have been summarized. Results show that various classification approaches for the surgical skill level definitions are being used. Besides, many studies are con-ducted by omitting particularly important skill levels in between. Additionally, some inconsistencies are also identified across the skill level classification studies.Conclusion: In order to improve the benefits of simulation-based training programs, a standardized interdisci-plinary approach should be developed. For this reason, specific to each surgical procedure, the required skills should be identified. Additionally, appropriate measures for assessing these skills, which can be defined in simulation-based MIS training environments, should be refined. Finally, the skill levels gained during the developmental stages of these skills, with their threshold values referencing the identified measures, should be redefined in a standardized manner.Article Citation - WoS: 10Citation - Scopus: 16Student Engagement Research Trends of Past 10 Years: a Machine Learning-Based Analysis of 42,000 Research Articles(Springer, 2023) Gurcan, Fatih; Erdogdu, Fatih; Cagiltay, Nergiz Ercil; Cagiltay, KursatStudent engagement is critical for both academic achievement and learner satisfaction because it promotes successful learning outcomes. Despite its importance in various learning environments, research into the trends and themes of student engagement is scarce. In this regard, topic modeling, a machine learning technique, allows for the analysis of large amounts of content in any field. Thus, topic modeling provides a systematic methodology for identifying research themes, trends, and application areas in a comprehensive framework. In the literature, there is a lack of topic modeling-based studies that analyze the holistic landscape of student engagement research. Such research is important for identifying wide-ranging topics and trends in the field and guiding researchers and educators. Therefore, this study aimed to analyze student engagement research using a topic modeling approach and to reveal research interests and trends with their temporal development, thereby addressing a lack of research in this area. To this end, this study analyzed 42,517 peer-reviewed journal articles published from 2010 to 2019 using machine learning techniques. According to our findings, two new dimensions, "Community Engagement" and "School Engagement", were identified in addition to the existing ones. It is also envisaged that the next period of research and applications in student engagement will focus on the motivation-oriented tools and methods, dimensions of student engagement, such as social and behavioral engagement, and specific learning contexts such as English as a Foreign Language "EFL" and Science, Technology, Engineering and Math "STEM".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 - WoS: 26Citation - Scopus: 36Exploratory Analysis of Topic Interests and Their Evolution in Bioinformatics Research Using Semantic Text Mining and Probabilistic Topic Modeling(Ieee-inst Electrical Electronics Engineers inc, 2022) Gurcan, Fatih; Cagiltay, Nergiz ErcilBioinformatics, which has developed rapidly in recent years with the collaborative contributions of the fields of biology and informatics, provides a deeper perspective on the analysis and understanding of complex biological data. In this regard, bioinformatics has an interdisciplinary background and a rich literature in terms of domain-specific studies. Providing a holistic picture of bioinformatics research by analyzing the major topics and their trends and developmental stages is critical for an understanding of the field. From this perspective, this study aimed to analyze the last 50 years of bioinformatics studies (a total of 71,490 articles) by using an automated text-mining methodology based on probabilistic topic modeling to reveal the main topics, trends, and the evolution of the field. As a result, 24 major topics that reflect the focuses and trends of the field were identified. Based on the discovered topics and their temporal tendencies from 1970 until 2020, the developmental periods of the field were divided into seven phases, from the "newborn" to the "wisdom" stages. Moreover, the findings indicated a recent increase in the popularity of the topics "Statistical Estimation", "Data Analysis Tools", "Genomic Data", "Gene Expression", and "Prediction". The results of the study revealed that, in bioinformatics studies, interest in innovative computing and data analysis methods based on artificial intelligence and machine learning has gradually increased, thereby marking a significant improvement in contemporary analysis tools and techniques based on prediction.Article Citation - WoS: 72Citation - Scopus: 98Mapping Human-Computer Interaction Research Themes and Trends From Its Existence To Today: a Topic Modeling-Based Review of Past 60 Years(Taylor & Francis inc, 2021) Gurcan, Fatih; Cagiltay, Nergiz Ercil; Cagiltay, KursatAs it covers a wide spectrum, the research literature of human-computer interaction (HCI) studies has a rich and multi-disciplinary content where there are limited studies demonstrating the big picture of the field. Such an analysis provides researchers with a better understanding of the field, revealing current issues, challenges, and potential research gaps. This study aims to explore the research trends in the developmental stages of the HCI studies over the past 60 years. Automated text mining with probabilistic topic modeling has been used to analyze 41,720 journal articles that are indexed by the SCOPUS database between 1957 and 2018. The results of this study reveal 21 major topics mapping the research landscape of HCI. By extending the discovered topics beyond a snapshot, the topics were analyzed considering their developmental stages, volume, and accelerations to provide a panoramic view that shows the increase and decrease of trends over time. In this context, the transition of HCI studies from machine-oriented systems to human-oriented systems indicates its future direction toward context-aware adaptive systems.Article Citation - WoS: 31Citation - Scopus: 40Research Trends on Distance Learning: a Text Mining-Based Literature Review From 2008 To 2018(Routledge Journals, Taylor & Francis Ltd, 2023) Gurcan, Fatih; Cagiltay, Nergiz ErcilToday's dynamic distance learning environments offer a flexible, comfortable, and lifelong learning experience, independent of space and time. In this way, it also supports and develops existing traditional training programs. The increasing importance of knowledge, skills and learning in today's technological life cycle has led to an increase and diversification of research and applications in distance learning. Accordingly, distance learning literature has a rich content supported by a multidisciplinary background. From this point of view, it is crucial to perceive the research landscape reflecting the general themes and trends studied in the field of distance learning. This study aims at revealing the distance learning research themes and trends by analyzing the 27,735 articles of journal conducted in the last decade. The methodology of the study is based on semantic content analysis implemented by N-gram-based text categorization technique. As a result, 10 main themes are discovered, namely, "System establishment", "Media", "Assessment", "Method", "Content", "Education levels", "Learner", "Research methods", "Interaction-Communication", and "Resource-Material-Tool". In this context, the findings of the study are expected to provide significant insights to guide prospective research and practice in the field and to develop continuous improvements and standards for distance education communities.
