Browsing by Author "Eryilmaz, Meltem"

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Citation - Scopus: 1
Analyzing Students' Academic Success in Pre-requisite Course Chains: A Case Study in Turkey
(Tempus Publications, 2018) Karakaya, Murat; Eryilmaz, Meltem; Ceyhan, Ulas; Computer Engineering; Computer Engineering
There are several principles which have been accepted as approaches to successful curriculum development. In spite of the differences in the proposed sequencing of topics, all approaches basically depend on the pre-requisite chains to implement their educational approach in the curriculum development for specifying the order of the subjects. In this research, two prerequisite chains representing two different curriculum development approaches are taken into consideration in a case study. The first research question considered is whether academic success in a follow-up course is positively related to success attained in the pre-requisite course. The second one is whether or not the selected curriculum development approach for deciding the chains has a significant impact on the academic success relationships between a pre-requisite and its follow-up course. To answer these questions, course data of 441 undergraduate students who graduated from the Atilim University between Fall 2001 and Spring 2015 semesters were collected and analyzed. The results indicate that the succes levels gained in a pre-requisite and its follow-up course are corelated. Moreover, different cirriculum development methods can affect this corelation. Thus, cirriculum developers should consider appropriate approaches to improve student success for deciding chaining courses and their contents.
Citation - Scopus: 1
Biletini Devret: a Secure Mobile App for Ticket Sales
(Ieee, 2021) Ak, Firat; Ozkan, Veli Batuhan; Gonder, Gokhan; Sumeroglu, Ersun; Eryilmaz, Meltem; Computer Engineering
It has been known that smartphones are the first thing that comes to mind when technology is mentioned. Almost every person has a smartphone, and they are used for social media, shopping, trade, and more. In the past, phones were just used for calculating something, or text messaging each other. However, nowadays, as mentioned above, they are used for complicated applications or works. Therefore, users need security for their private information. The Biletini Devret application in this study keeps users' private information secure with the help of Google Cloud Platforms and this application has two-factor verification to be more secure and to prevent unauthorized users. In particular, the Biletini Devret application has a Face Recognition System which has the most reliable authentication system all in the world.
Citation - WoS: 24
Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance
(Mdpi, 2020) Eryilmaz, Meltem; Adabashi, Afaf; Computer Engineering
In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students' pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.
Citation - WoS: 1
The Evaluation of Students' Academic Achievements in Adaptive Environments
(Turkish Education Assoc, 2014) Eryilmaz, Meltem; Simsek, Nurettin; Computer Engineering; Computer Engineering
In this experimental research, academic performance of the students who were grouped according to their prior knowledge levels. In the research, three different environments were developed on the Mood le platform. The environments were designed without adaptations, with content adaptations that contain changes pertaining to the presentation style of the information that is to be included on pages, and with navigation adaptations which offer the suitable links by changing the link structure. The students worked for a period of five weeks in the designed environments. A multiple choice test and a practice examination were prepared in order to determine the knowledge level of the students pertaining to the subject matter prior to the experimental process, and to measure their academic performance after the experimental process. The findings derived from the scores of the tests which were given as pre test and final test indicated that the academic performance of the students who study in adaptive and non-adaptive learning environments may differ.
Citation - WoS: 37
Citation - Scopus: 46
Individual Flipped Learning and Cooperative Flipped Learning: Their Effects on Students' Performance, Social, and Computer Anxiety
(Routledge Journals, Taylor & Francis Ltd, 2019) Eryilmaz, Meltem; Cigdemoglu, Ceyhan; Public Relations and Advertising; Computer Engineering
The purpose of this study is to differentiate the effect of cooperative learning strategy integrated with a flipped learning (FL) model from sole FL implementation in promoting students' performances while decreasing their social and computer anxiety in an undergraduate course. As a method, a classical experimental design is used. The participants were from the department of English Language and Literature, and Translation and Interpretation. Students were randomly assigned to individual FL (the control group) class; and FL with cooperative activities (experimental group) class. The groups were randomly assigned as experimental and control by tossing a coin. The implementation took 10 weeks. Students' performances (grades), social anxiety, and computer anxiety were dependent variables of the study and they were compared through multivariate analysis of variance. The results indicated that there is no significant mean difference between groups' performances; however; the group of FL with cooperative activities had less social anxiety, but no significant change occurred at their computer anxiety level.
Citation - WoS: 1
Citation - Scopus: 1
Machine Vs. Deep Learning Comparision for Developing an International Sign Language Translator
(Taylor & Francis Ltd, 2022) Eryilmaz, Meltem; Balkaya, Ecem; Ucan, Eylul; Turan, Gizem; Oral, Seden Gulay; Computer Engineering
This study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.
A New E-Commerce Model Suggestion of Agricultural Products
(Springer international Publishing Ag, 2024) Eryilmaz, Meltem; Briman, Mohammed Khalid Hilmi; Yakut, Gokce; Computer Engineering
This study aims to create an e-commerce application that enables farmers to sell their products directly to their customers without resorting to intermediaries by a smart cargo box. It presents a method by which the agricultural industry can gain momentum in e-commerce. The main contributions of the study are an e-commerce application that enables farmers to sell their products directly to the customer without the need for an intermediary and the design and implementation of a smart cargo box that is tailored to carry agricultural items throughout the delivery process that locks and unlocks only via QR code generated by the application after order. Django FrameworkNext.js, React.js, and Redux are used for the system development. The database is created with PostgreSQL using migrations, and AmazonWeb Services is used for this database. The "ESP32 Cam" is used to read the QR Code.
Citation - WoS: 3
Citation - Scopus: 3
Online Learning Perceptions Amid Covid-19 Pandemic: the Engineering Undergraduates' Perspective
(Tempus Publications, 2022) Eryilmaz, Meltem; Kalem, Guler; Kilic, Hurevren; Tirkes, Guzin; Topalli, Damla; Turhan, Cigdem; Yazici, Ali; Information Systems Engineering; Computer Engineering; Software Engineering; Information Systems Engineering; Computer Engineering; Software Engineering
The COVID-19 pandemic caused face-to-face education in just about all universities worldwide to shift to online education. For most students, this educational model was a compulsory first experience. In this study, the survey results are analyzed and discussed related to a group of students in the Engineering Faculty of a university in Turkey regarding their online education perceptions. Briefly summarized, the findings of the study indicate that: (a) most of the students still prefer face-to-face learning, which is also favored if accompanied by distance learning; (b) the concentration level of the students has dropped due to the concerns about the COVID-19 pandemic which affects their learning negatively; and (c) around half of the students participating in the study feel that the online exams conducted without a secure exam software, is considered unsafe. Additionally, the study's results were further extended to evaluate the questionnaire results and reported along with the suggestions of necessary actions in emergency online learning (EOL).
Citation - Scopus: 2
The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: an Extensive Simulation Study
(Mdpi, 2024) Omay, Tolga; Akdi, Yilmaz; Emirmahmutoglu, Furkan; Eryilmaz, Meltem; Economics; Computer Engineering
The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.
Citation - WoS: 4
Citation - Scopus: 2
Using Artificial Intelligence Methods to Predict Student Academic Achievement
(Springer international Publishing Ag, 2022) Al-Khafaji, Mustafa; Eryilmaz, Meltem; Computer Engineering
This study applies two artificial intelligence methods represented by both the neural network and fuzzy logic to predict student achievement in the exam. The dataset used in this study was taken from an Iraqi engineering college and it represents data of 200 students who have enrolled in the computer science course. Gender, age, resources downloaded, videos viewed, discussion chat joined, exam scores used as the data set. The type of artificial neural network used was pattern neural network. Levenberg-Marquardt's algorithm was used to train the neural networks. On the other hand Sugeno fuzzy inference system was used for the fuzzy logic. The study results showed that the students who spend more time on the learning system have the most success rate. According to the results the neural network accuracy rate 73% and the fuzzy was 88%. This high accuracy rates support that artificial intelligence methods can be used to predict student academic achievement.