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- Bogazici University Theses
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Resilient youths’ perspectives on their current subjective well- being in retrospect to early childhood adverse experiences
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2023., 2023) Şahin Altan, Neslihan.; Erdemir, Ersoy.
The primary aim of this interpretive phenomenological study was to gain an in-depth understanding of the lived experiences of university students who self-identified themselves as resilient and had been retrospectively exposed to adverse childhood experiences (ACEs) as well as their current subjective well- being (SWB). The secondary aim was to differentiate the protective factors among participants with higher SWB and lower SWB was the other objective. The study was conceptually guided by family resilience theories and ecological model perspectives. Eleven participants, chosen among 41 university students from a high-ranked state university in Turkey, participated in the study. The first phase of data collection involved identifying demographical variables and utilizing a subjective well-being scale to determine participants with higher and lower SWB. The second phase involved conducting individual semi-structured interviews with participants profiled according to their SWB in the first phase. Interpretative phenomenological analyses (IPA) were carried out along with researcher reflection notes. Findings revealed the complex nature of the participants’ lived experiences of ACEs and their perceived effects on their current SWB. Participants had diverse perceptions of resilience, the majority of which were negative. All participants, including those with higher SWB, were currently experiencing psychological or physical challenges, unpleasant feelings, and negative carryovers, yet all demonstrated outstanding academic achievement. Lastly, supportive and caring people in early childhood emerged as a crucial protective factor that led to differences between participants with higher and lower SWB. Findings offer significant implications for promoting SWB and fostering resilience among individuals with ACEs.
Language challenges in English medium higher education and translingual assessment as an alternative tool
(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Güllü, Talip.; Bayyurt, Yasemin.
This thesis involves three studies conducted in a private university in Türkiye to observe students’ language use in content exams which do not impose language constraints, to understand factors influencing their language use, and to investigate the impact of providing textual input in the first language (L1) on students’ written production in their second language (L2). The studies combine quantitative and qualitative data, including the development, implementation, and scoring of assessment tasks, and student interviews. The findings revealed that students encountered challenges in L2 comprehension and production in lessons and exams. The participants engaged in both monolingual and translingual practices in the content exams given as part of the current thesis. Their language use was associated with relative proficiency in the L2, encoding-retrieval match, and compartmentalization of languages. Additionally, students performed better in the L2 writing task when both input texts were in the L2 compared to when one of the texts was in the L1. Overall, the findings show that translanguaging is a common practice both inside and outside the classroom and that offering flexibility in terms of language use in content exams may serve at least as a temporary accommodation which allows students to express their content knowledge more fully, particularly in time-constrained exams. However, this should not lead to decreased provision of L2 input, as input contributes to students’ L2 proficiency and may ultimately lead to a level where students are able to, and choose to, express their content knowledge in the L2 without considerable language-related hindrance.
Influence of an audience-oriented task, executive function, and trait perspective taking on EFL writing
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2023., 2023) Coşkun, Turgut.; Yiğitoğlu, Nur, 1983- .
This study adopted a cognitive approach and investigated the effect of an audienceoriented task on the passages composed by novice EFL writers by considering their executive function and trait perspective-taking capabilities. With this purpose, the writing performance of two groups assigned to either an experimental or a control condition was compared. The audience- oriented experimental group was exposed to a writing task that emphasized the audience. Most importantly, they were asked to read three short messages shared by their audience, then take their perspective and write down whatever came to their minds. The non-audience-oriented control group was also asked to read the messages but was not encouraged to take the audience's perspective. They were just asked to write down whatever came to their minds. Following that, both groups wrote the main passages and completed four executive function tasks and a trait perspective-taking scale. The results revealed that exposing upper-intermediate novice EFL writers to an audience- oriented task increased the overall writing quality of the participants with high executive function or trait perspective- taking capabilities. Regarding the level of persuasiveness, the task's effect may depend on the availability of sufficient executive function resources. These findings confirm the importance of a task for directing and managing cognitive resources and show the executive function's central role and trait perspective-taking’s importance in writing. The tasks that encourage employing these individual resources may enrich the instructor toolboxes.
Realized volatility forecasting using hybrid neural networks : an application for The Istanbul Stock Exchange
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2023., 2023) Gültekin, Mehmet Ekin.; Badur, Bertan Yılmaz.
Volatility forecasting in the financial markets is important in the areas of risk management and asset pricing, among others. In this study, BIST 100’s 1-day, 5-day, and 10-day-ahead return volatilities are examined. Two types of hybrid models are utilized to improve individual GARCH-family models’ predictions. For the first hybrid model, a group of GARCH- family models is constructed to produce volatility estimates which were then fed into neural network to increase the predictive power. The second hybrid model received GARCH- family models’ specifications instead of volatility estimates as inputs for ANN to conduct the learning process. Hybrid neural networks were also fed a set of exogenous, endogenous, and dummy variables. One of the main conclusions is that both hybrid models increased the forecasting precision of individual GARCH-family models while the second hybrid model provided better volatility forecasts for all error measures used in this study. Equal forecast accuracy test also showed that the hybrid models’ out-of-sample predictions were significantly better than GARCH-family methods. All model performances deteriorated as forecast horizon was extended, although the steepest decline happened for hybrid models rather than the GARCH-family. Lastly, as the complexity of the neural network architecture was increased, the loss measures for the out-of- sample forecasts improved except on the last case where the network overfitted using the highest number of neurons per hidden layer among the searched hyperparameter grid.
Using social media big data with machine learning to improve customer satisfaction
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2023., 2023) Demir, Hilal.; Taşkın, Nazım.
With the increasing importance of customer relationship management and improving customer support in today's competitive business landscape, there is a growing need to leverage machine learning techniques for gaining insights, forecasts, and better decision-making. Sentiment analysis, in particular, has emerged as a powerful tool for improving customer support services. In this study, we explore the use of three gradient boosting algorithms, XGBoost, CatBoost, and LightGBM, for sentiment classification on Twitter data. We employ ensemble classifications to analyze the sentiment of the data and observe improvements in performance. Our results are compared to other two algorithms that are popularly used in the context of sentiment analysis and show that the ensemble classification of the three algorithms yields the highest accuracy and F1 score. By addressing the gap in understanding how different machine learning algorithms can be used to enhance customer support processes, this research aims to contribute to the improvement of customer satisfaction and loyalty. Specifically, the study aims to improve the accuracy of sentiment classification, thereby enabling businesses to better meet customer expectations for fast and efficient customer support.