Organizational Unit: Sectoral Education and Professional Development
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ArticlePublication Open Access Price and quality decisions of a service provider under heterogeneous demand(Boğaziçi Üniversitesi, 2019) Özener, Başak Altan; Atahan, Pelin; Economics; Sectoral Education and Professional Development; ÖZENER, Başak Altan; DEMİRCİLER, Pelin AtahanA monopolist service provider's quality and price decisions are analyzed in a vertically differentiated market where customers demand different quantities of a service. We find that depending on the relative sizes of the market segments and the difference in the valuations of different customers, the service provider may find it optimal to either offer a non-discriminating service or a discriminating service serving only high-valuation customers. The service provider never finds it optimal to serve the market segments that have low-valuation for quality when the discrimination strategy is optimal. © 2019 Bogazici Universitesi. All rights reserved.
ArticlePublication Metadata only Transdisciplinarity as a learning challenge: Student experiences and outcomes in an innovative course on wearable and collaborative robotics(IEEE) Kılıç-Bebek, Ebru; Nizamis, K.; Vlutters, M.; Bebek, Özkan; Karapars, Gülhis Zeynep; Ünal, Ramazan; Yılmaz, Deniz; Uğurlu, Regaip Barkan; Industrial Design; Sectoral Education and Professional Development; Mechanical Engineering; Mitchell, J.; BEBEK, Ebru Kılıç; KARAPARS, Gülhis Zeynep; BEBEK, Özkan; ÜNAL, Ramazan; UĞURLU, Regaip Barkan; Yılmaz, DenizContribution: This study provides evidence for the benefit of short online courses for transdisciplinary competence development of graduate students. It shows the significant challenges students face while learning, and provides instructional recommendations to improve students’ learning quality and professionalism. Background: Developing wearable and collaborative robots requires industry collaboration and transdisciplinary competence. Industry’s involvement in long-term programs is becoming infeasible, and the nature of transdisciplinary learning has not been explored to inform instructional practices. Intended Outcomes: This study aimed to provide instructional recommendations based on an in-depth examination of a diverse group of graduate students’ learning and teamwork experiences as well as outcomes in a 5-day online transdisciplinary course. Application Design: 31 graduate students of engineering, industrial design, and health fields from 4 countries participated in online mixed-discipline instructional sessions and teams to address a real industry challenge. A mixed-methods approach was used to examine students’ experiences and learning outcomes based on a competence measure, session participation data, student journal entries, team progress reports, team elaboration visuals, and final team presentations. Findings: Students’ knowledge of industrial design, medical considerations, ethics and standards, effective teamwork, and self-regulated learning were increased. Students’ high motivation helped them deal with the challenges involved. Daily student journals, team reports, and visual elaboration tools were found to be beneficial for determining the challenges and learning quality. The observed student progress within 5 days is promising, making it worthwhile to further explore the benefits of short online courses for increasing graduates’ readiness and establishing university-industry collaborations in education.ArticlePublication Open Access Inspired by the East: How the islamic world influenced Western Art. British museum, 10 Ekim 2019–26 Haziran 2020, Londra. küratörler: Julia Tugwell ve Olivia Threlkeld(Istanbul Research Institute, 2020) Elbirlik, Leyla Kayhan; Sectoral Education and Professional Development; ELBİRLİK, Leyla KayhanInspired by the East: How the Islamic World Influenced Western Art. British Museum, 10 Ekim 2019– 26 Haziran 2020, Londra. Küratörler: Julia Tugwell ve Olivia Threlkeld, sergisinin Leyla Kayhan Elbirlik tarafından yazılmış tenkidi.Conference paperPublication Open Access Discussing modernizing engineering education through the Erasmus + Project Titled "Open Educational Resources on Enabling Technologies in Wearable and Collaborative Robotics (WeCoRD)(Ege University) Kılıç-Bebek, Ebru; Nizamis, K.; Karapars, Gülhis Zeynep; Gökkurt, Muharrem Ali; Ünal, Ramazan; Bebek, Özkan; Vlutters, M.; Vander Poorten, E.; Borghesan, G.; Decré, W.; Aertbelien, E.; Borisova, O.; Borisov, I.; Kolyubin, S.; Kodal, M. I.; Uğurlu, Regaip Barkan; Industrial Design; Sectoral Education and Professional Development; Mechanical Engineering; BEBEK, Ebru Kılıç; KARAPARS, Gülhis Zeynep; GÖKKURT, Muharrem Ali; ÜNAL, Ramazan; BEBEK, Özkan; UĞURLU, Regaip BarkanThe Erasmus + project titled “Open Educational Resources on Enabling Technologies in Wearable and Collaborative Robotics (WeCoRD)", can serve as a model to establish strategic international and multidisciplinary partnerships to modernize engineering education. WeCoRD project is a collaboration among internationally renowned institutions from Turkey, Belgium, Russia, and the Netherlands to create an innovative course on wearable and collaborative robotics with Open Educational Resources (OERs) and an online Virtual Lab aimed at accessibility across Europe. This collaboration involves many fields from engineering, health, and design disciplines as well as an industry partner from the automotive manufacturing sector. The main objectives of the project are to: (1) prepare a graduate-level course in wearable and collaborative robotics, (2) enhance EU higher education capacity in the field with clear use-case scenarios from the industry and medical applications, (3) provide open-source materials including a virtual lab, and (4) fill the skill gap between the industry and the academia while also aiming a continued professional development. With these goals which aim to modernize engineering education and make it more relevant to the industry, the WeCoRD project brings both multidisciplinary and interdisciplinary aspects of robotics education to a new level. Each intellectual output (IO) of the project is allocated to a project partner based on their expertise. The course module design and development is planned as follows: The IO1 (the first course module) on “Components for wearable and collaborative robots” is led by Ozyegin University, Turkey; the IO2 (the second course module) on “Modeling, design and control or wearable and collaborative robots as systems” is led by ITMO, Russia; the IO3 (the third course module) on “Human-robot interaction for wearable and collaborative robots” is led by KU Leuven, Belgium; the IO4 (the fourth course module) on “Medical applications” is led by U.Twente; the IO5 (integration of the first three course modules into one course) on the graduate-level course to be integrated into graduate degree programs and to be adopted for continued professional development (CPD) training programs, as well as the translation of the course materials into Turkish is led by KU Leuven, Belgium; the IO6 on the “Virtual Lab” is led by ITMO, Russia; and finally IO7 on the “Video Collection” is led by Ozyegin University, Turkey. FORD-Otosan, which is one of the industry partners from Turkey will host students, provide site visits and offer workshops. Each project partner and their contributions will be addressing the fundamental need for modernizing engineering education through students’ active participation and boosting students’ skill development. In addition to multidisciplinary and interdisciplinary exposure, students will get a chance to work with industry partners and learn through authentic problem solving and relevant feedback. Providing a deeper and more effective learning experience will be among the core design principles of the course modules, labs, videos, and industry collaborations.ArticlePublication Metadata only Accelerated learning of user profiles(Informs, 2011-02) Atahan, Pelin; Sarkar, S.; Sectoral Education and Professional Development; DEMİRCİLER, Pelin AtahanWebsites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation.Conference paperPublication Metadata only Composing offer sets to maximize expected payoffs(Digital Commons, 2016) Atahan, Pelin; Johar, M.; Sarkar, S.; Sectoral Education and Professional Development; DEMİRCİLER, Pelin AtahanFirms are increasingly using clickstream and transactional data to tailor product offerings to visitors at their site. Ecommerce websites have the opportunity, at each interaction, to offer multiple items (referred to as an offer set) that might be of interest to a visitor. We consider a scenario where a firm is interested in maximizing the expected payoff when composing an offer set. We develop a methodology that considers possible future offer sets based on the current choices of the user and identifies an offer set that will maximize expected payoffs for an entire session. Our framework considers both the items viewed and purchased by a visitor and models the probability of an item being viewed and purchased separately when calculating expected payoffs. The possibility of a user backtracking and viewing a previously offered item is also explicitly modelled. We show that identifying the optimal offer set is a difficult problem when the number of candidate items is large and the offer set consists of several items even for short time horizons. We develop an efficient heuristic for the one period look-ahead case and show that even by considering such a short horizon the approach is much superior to alternative benchmark approaches. Proposed methodology demonstrates how the appropriate use of information technologies can help e-commerce sites improve their profitability.Conference paperPublication Open Access Optimizing offer sets based on user profiles(Social Science Research Network, 2009) Atahan, Pelin; Johar, M.; Sarkar, S.; Sectoral Education and Professional Development; DEMİRCİLER, Pelin AtahanPersonalization and recommendation systems are being increasingly utilized by ecommerce firms to provide personalized product offerings to visitors at the firms’ web sites. These systems often recommend, at each interaction, multiple items (referred to as an offer set) that might be of interest to a visitor. When making recommendations firms typically attempt to maximize their expected payoffs from the offer set. This paper examines how a firm can maximize its expected payoffs by leverag ing th e kn owledge of the profiles of visitors to their site. We provide a methodology that accounts for the interactions among items in an offer set in order to determine the expected payoff. Identifying the optimal offer set is a difficult problem when the number of candidate items to rec ommend is large. We develop an efficient heuristic for this problem, and show that it performs well for both small and large problem instances.Conference paperPublication Metadata only Sponsored search: the sum is larger than its parts(2011) Ghoshal, A.; Sarkar, S.; Menon, S.; Atahan, Pelin; Sectoral Education and Professional Development; DEMİRCİLER, Pelin Atahan