Browsing by Author "Ozan, Ozlem"

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Challenges in analyzing unstructured learner generated qualitative big data
(Academic Conferences Limited info@academic-conferences.org, 2015) Ozlem Ozan; Ozan, Ozlem; M. Cubric , A. Jefferies
As the amount of educational data grows a necessity is occurring to define characteristics of Unstructured Learner Generated Qualitative Big Data (ULGQBD) and develop a conceptual framework to analyze these data sets. According to the author there are three main challenges in analyzing ULGQBD. First one is related to qualitative big data itself. There is an ambiguity of definition and methodology of analyzing ULGQBD. The answer of the question How much amount of qualitative data is big? is blurry. Therefore there is no a simple straightforward specific methodology to overcome the struggles originated from seeking the meaning within such a big amount of unstructured data. Second problem is absence of conceptual frameworks for analyzing ULGQBD. Third problem is related to Qualitative Data Analysis (QDA) software. Traditional QDA software is not ready to big amount of data and cloud based text analytics tools do not have enough language support for stemming for agglutinative languages such as Turkish. In this paper author shares her experience of analyzing ULGQBD. The data set which she worked on contained 13000 responses to an open ended survey whose main purpose to get feedback about the courses from the customers to improve educational effectiveness. She provides a framework for structural coding of ULGQBD and brief road map for researchers who deals with ULGQBD. © 2017 Elsevier B.V. All rights reserved.
Exploring Content Moderation Research: Insights from a Bibliometric Analysis
(ACAD CONFERENCES LTD, 2024) Ozlem Ozan; Ali Riza Sadikzade; Ozan, Ozlem; Sadikzade, Ali Riza; P Fotaris
Rapid technological advances have intensified user-content interactions leading to real-world consequences and the implementation of complex regulation mechanisms such as AI filtering and industrial and user moderation. This study aims to introduce the contemporary topics surrounding the subject by comprehensively examining the content moderation research by conducting a bibliometric analysis of 202 publications between 2016 and 2023 from the Web of Science and Scopus databases. This study aims to identify the influential authors universities countries journals funding agencies network maps of keywords and co-authorship. The findings of this study demonstrate that the Queensland University of Technology is the most influential in the field. The United States of America England and Australia are the most productive countries. The National Science Foundation and the European Research Council are the most supporting funding institutions. New Media & Society Social Media + Society and Big Data & Society are the most influential journals. Ysabel Gerrard is the most productive author. Seven clusters occur in author collaboration networks. The network map of the keywords suggests that researchers mainly focus on social media, Facebook Instagram YouTube and Twitter are the most investigated platforms. There is a shift from transparency to hate speech and misinformation among the research themes. The academic research has exhibited a consistent upward trajectory since 2016. Given the demonstrable momentum of interest in this field it is reasonable to anticipate a further increase in research with a diverse array of academic disciplines.
Citation - Scopus: 2
Factors Influencing the Learner's Cognitive Engagement in a Language MOOC: Feature Selection Approach
(Institute of Electrical and Electronics Engineers Inc., 2023) Murat Kılınç; Orkun Teke; Ozlem Ozan; Yasin Ozarslan; Kilinc, Murat; Teke, Orkun; Ozan, Ozlem; Ozarslan, Yasin
This study aims to predict the cognitive engagement rate in a Language MOOC (Massive Open Online Course) based on the features extracted from learners' engagement behaviors within the content and activities. The features were extracted from the data of the Language MOOC 'Türkçe Öǧreniyorum (I learn Turkish)' which aims to provide self-paced learning materials for those interested in developing their skills in Turkish as a foreign language. After the data preprocessing processes were carried out with the data set obtained for cognitive engagement classification feature selection processes were performed using filtering and wrapper methods. Afterward the machine learning model trained using the Logistic Regression (LR) algorithm performed the classification with 94% accuracy. The model evaluation metrics also support the classification result obtained. Based on the extracted features and the classification results obtained the model will be able to capture learners' interaction behaviors with the content and activities in a Language MOOC and detect changes in learner behavior over time. Prediction accuracy is essential to offer dynamic content and activities in a Language MOOC for adjusting the individual needs of each learner providing personalized learning experiences that are tailored to their skills knowledge and preferences. © 2023 Elsevier B.V. All rights reserved.
Supporting SDG-Oriented Knowledge Construction and Idea Diffusion in Online Higher Education
(MDPI, 2026) Ozarslan, Yasin; Ozan, Ozlem
This study investigates how online discussion forums in an undergraduate Social Responsibility course support students' SDG-oriented idea generation and collaborative knowledge construction. It also examines how participation roles, behavioral intensity, interaction-network influence, and goal-aligned discourse shape idea visibility and discussion. Using a mixed-methods learning analytics design, we analyzed forum logs and message texts across five SDG-linked themes (SDGs 6, 7, 12, 14, 15) by classifying contributor types, computing a Behavioral Participation Index (BPI), constructing a directed reply network and estimating PageRank centrality, extracting solution proposals, scoring semantic goal alignment, modelling weekly temporal dynamics, and fitting multivariate regressions predicting visibility (reads) and engagement (replies) while controlling for theme, message level, time, PageRank, and BPI. Results show role-differentiated participation (N = 514), meaningful cross-theme solution proposals that varied across academic groups, and peak-driven weekly activity. PageRank centrality emerged as the strongest and most consistent predictor of both visibility and engagement, whereas goal alignment showed weaker direct effects after controls, suggesting that SDG-aligned ideas do not necessarily diffuse without structural embeddedness. Among highly goal-aligned posts, specific communicative features differentiated which proposals attracted attention and interaction. These findings suggest that SDG forum design benefits from structured interaction pathways and scaffolded discourse strategies to support equitable diffusion and productive sustainability dialogue. The study does not evaluate the normative quality of sustainability positions but examines how interaction structures and discourse features shape the visibility and diffusion of student-generated ideas.
Citation - WoS: 1
Citation - Scopus: 1
TOWARDS AN ADAPTIVE LANGUAGE MOOC: EXAMINING DIFFERENCES OF LANGUAGE ERROR PATTERNS ACROSS CULTURAL DOMAINS
(Anadolu Universitesi, 2025) Ozlem Ozan; Yasin Ozarslan; Sevgi Calisir Zenci; Calisir Zenci, Sevgi; Ozan, Ozlem; Ozarslan, Yasin
This study analyzed linguistic errors as part of the Differentiated Distance Education of Turkish as a Foreign Language Project which pursues the development of an adaptive MOOC for Turkish as a second language. Therefore the Turkish CEFR (Common European Framework of Reference for Languages) A1-level writing exam papers of 177 learners were analyzed. Linguistic error analysis techniques were used. A Chi-square test of independence a Kruskal-Wallis H test and a Mann-Whitney U test were conducted to examine the data. The results show a relationship between error frequency and learner group (Arabic–Farsi Turkic Balkan and Other). Similarly the error density varied as a function of the learner group. There is also a relationship between error frequency and the language family of the learner’s mother language. On the other hand there is no significant difference in error density by language family. The number of languages the learner knows has no significant effect on error frequency and density. The findings suggest that there are gender-based differences in error density among learners but that these differences are not reflected in the frequency of errors. The topics for differentiation were identified based on the error distribution of learner groups. The topic that requires the most differentiation is noun phrases. The learner groups that need the most differentiation are the Arabic and Farsi Nations while the Turkic Nations require the least differentiation. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 50
Citation - Scopus: 63
Video lecture watching behaviors of learners in online courses
(Routledge info@tandf.co.uk, 2016) Ozlem Ozan; Yasin Ozarslan; Ozan, Ozlem; Ozarslan, Yasin
This paper examines learners’ behaviors while watching online video lectures to understand learner preferences. 2927 students’ 18144 video events across 13 courses on Sakai CLE LMS which were integrated with Kaltura Video Platform and Google Analytics were analyzed. For the analysis of the quantitative data one-way ANOVA Chi-square test of independence and descriptive statistics were utilized. The main results revealed that there was a tendency toward watching interview-style video lectures completely. In addition the percentage rate of Watching Completely behavior was higher in shorter videos and a tendency toward watching long video lectures by seeking was found. According to our results watching patterns was also affected by lecturers’ characteristics. Watching Completely rate of female lecturers was significantly different than those of male lectures in favor of females as well as watching in FullScreen mode. Furthermore learners who watched online video lectures completely had higher scores on the final exam than others. Our analysis could help those who plan to optimize online video lectures in e-learning programs. © 2016 Elsevier B.V. All rights reserved.