https://hdl.handle.net/20.500.14124/6560 Web of Science platform 2026-04-20T01:22:03Z https://hdl.handle.net/20.500.14124/10745 An Analysis of User Experience Based on Physical Environmental Components in VR-Supported Educational Spaces Using a Mixed-Methods Approach Kurtuluş, Minel; Tekin, Çiğdem Background: Virtual reality (VR) technology offers an innovative learning environment that enriches user experience in education. However, there is limited research on the impact of physical environmental components within VR-supported educational spaces on user perception and satisfaction. Objectives: This study aims to evaluate user experience and satisfaction levels in VR-supported educational environments through a mixed-methods approach, focusing on the influence of physical environmental components. Methodology/Approach: Four distinct VR platforms (Mozilla Hubs, Engage, Meta Horizon, and MeetinVR) were selected as experimental settings. Twenty participants, comprising ten interior architecture students and ten faculty members from Istanbul Gelisim University, received orientation sessions to familiarize themselves with the VR equipment and platforms. Each participant engaged in 10-minute sessions on each platform, experiencing four different virtual environments in total. Following each session, participants completed structured experience questionnaires and open-ended feedback forms addressing physical environmental elements such as material, texture, lighting, form, color, size, and scale. The collected data were analyzed using IBM SPSS and MAXQDA software. Findings and Discussion: The results demonstrate that the design elements of VR-based learning spaces have a direct effect on user perception and satisfaction. In particular, material, lighting, and scale components play a significant role in enhancing users' sense of realism and educational effectiveness. The study offers practical recommendations for the user-centered design of VRsupported educational environments, contributing to the advancement of virtual learning spaces within interior architecture education. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.14124/10744 Constructing National Identity Through Museums in Early Republican Turkey: Historical Narrative, Spatial Transformation, Exhibiting Modernity, and Monumentality Atalay Şimşek, Duygu This article examines the role of museums in the construction of national identity during the Early Republican Period in Turkey (1923-1950). Drawing on theoretical approaches that interpret museums as spaces in which collective memory and national identity are materially organized and publicly communicated, the study analyzes museums as key sites through which the ideological foundations of the new nation-state were articulated. The study adopts a qualitative historical approach based on document analysis of representative primary and secondary sources and proposes an analytical interpretation derived from a comparative reading of museum practices and institutional transformations. Its principal original contribution lies in identifying four operational analytical categories: historical narrative, spatial transformation, exhibiting modernity, and monumentality that clarify the structural functions of museums in the nation-building process. Museums are interpreted as spaces through which national identity was constructed along several interconnected dimensions. First, museums functioned as spaces of national memory in which a newly constructed historical narrative was materialized and communicated. Second, the transformation of Ottoman palaces and religious environments into museums symbolized the political and ideological rupture between empire and republic. Third, museums contributed to the dissemination of painting and sculpture as visible expressions of modernization. Finally, museums acquired monumental meanings and functioned as symbolic environments representing the founding leaders and ideals of the Republic. Taken together, these dimensions demonstrate how museums functioned as key cultural spaces through which national identity was structured and communicated in the Early Republican state. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.14124/10695 Development of a dispersive solid phase extraction method based on copper-metal organic framework nanoparticles for the determination of lead at trace levels in stream samples Karakebap, Kübra; Serbest, Hakan; Turak, Fatma; Bakırdere, Sezgin In this study, a facile, rapid, and cost-effective method was developed for the determination of lead ions using copper-metal organic framework (Cu-MOF) nanoparticles based dispersive solid phase extraction (DSPE) in slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS) system. Cu-MOF nanoparticles were used as sorbents, and SQT was used to increase the residence time of lead atoms in the light path. The limit of detection (LOD) and limit of quantification (LOQ) of the Cu-MOF-DSPE-SQT-FAAS system were calculated as 7.1 µg L-1 and 23.5 µg L-1 under optimum experimental conditions, respectively. The regression coefficient (R2) was found to be 0.9967, and the linear operating range was determined between 15 and 300 µg L-1. Thanks to the developed method, a 103.7-fold improvement was achieved for the sensitivity of the traditional FAAS system by comparing the slopes of the linear calibration plot equations. The feasibility of the proposed method was investigated by spiking experiments with utilizing the stream water samples. The good recovery results obtained in the range of 90.8% to 127.1% demonstrated the applicability of the developed method to river water samples with high accuracy and precision. Cu-MOF structures have been employed for the first time for the preconcentration of Pb ions, and their prominent surface properties suggest that they may also be applicable for other analytical processes for different analytes. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.14124/10662 Interpretable Machine Learning for Compressive Strength Prediction of Fly Ash-Based Geopolymer Concrete Ahadian, Farnaz; Işıkdağ, Ümit; Bekdas, Gebrail; Niğdeli, Sinan Melih; Çakıroğlu, Celal; Geem, Zong Woo Fly ash-based geopolymer concrete (GPC) is a sustainable alternative to conventional cementitious materials; however, its compressive strength is governed by complex and highly correlated mixture parameters, making experimental optimization expensive and data-driven modeling challenging. While machine learning (ML) techniques have been widely applied to predict GPC strength, most studies prioritize predictive accuracy without explicitly addressing multicollinearity among input variables, which can distort feature importance, reduce model stability, and limit engineering interpretability. This study proposes a multicollinearity-integrated and interpretable ML framework that systematically embeds correlation diagnostics and structured feature screening within the modeling pipeline rather than treating interpretability as a post-processing step. Multiple conventional and ensemble learning algorithms were comparatively evaluated using cross-validation to ensure generalization robustness. The proposed framework achieved a maximum coefficient of determination (R2) of 0.96 with low prediction error, outperforming baseline regression models while demonstrating improved stability under correlated input conditions. Unlike existing studies that rely solely on black-box optimization, the integrated interpretability analysis revealed physically consistent dominance of curing temperature, alkali content, and water-related parameters in governing strength development. By explicitly coupling predictive performance with multicollinearity mitigation and engineering-oriented interpretability, this work advances beyond accuracy-driven ML applications and provides a robust and transparent decision-support tool for sustainable geopolymer mix design. 2026-01-01T00:00:00Z