Researcher:
Canbaloğlu, Gülay

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Undergraduate Student

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Gülay

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Canbaloğlu

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Canbaloğlu, Gülay

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2021 - 202310

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Researcher Of Publications: search.filters.isAuthorOfPublication.4234195b-7b09-4e10-9b53-33477c410177

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    A second-order adaptive network model for organizational learning and usage of mental models for a team of match officials
    (2022) Kuilboer, Sam; Sieraad, Wesley; van Ments, Laila; Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    This paper describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organizational learning in team-related performances. The paper describes the value of using shared mental models to illustrate the concept of organizational learning, and factors that influence team performances by using the analogy of a team of match officials during a game of football and show their behavior in a simulation of the shared mental model. The paper discusses potential elaborations of the different studied concepts, as well as implications of the paper in the domain of teamwork and team performance, and in terms of organizational learning.
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    Equilibrium analysis for linear and nonlinear aggregation in network models: applied to mental model aggregation in multilevel organisational learning
    (Taylor & Francis Ltd, 2022) Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    In this paper, equilibrium analysis for network models is addressed and applied in particular to a network model of multilevel organisational learning. The equilibrium analysis addresses properties of aggregation characteristics and connectivity characteristics of a network. For aggregation characteristics, it is shown how certain classes of nonlinear functions enable equilibrium analysis of the emerging dynamics within the network like linear functions do. For connectivity characteristics, by using a form of stratification for the network's strongly connected components, it is shown how equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. In addition, concerning aggregation characteristics, two specific types of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are analysed. It is illustrated in detail how by using certain function transformations also methods for equilibrium analysis based on a symbolic linear equation solver, can be applied to make predictions about equilibrium values for them. All these results are applied to a network model for organisational learning. Finally, it is analysed in some depth how the function transformations applied can be described by the more general notion of function conjugate relation, also often used for coordinate transformations.
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    Multilevel organisational learning in a project-based organisation: computational analysis based on a 3rd-order adaptive network model
    (Elsevier B.V., 2022) Treur, Jan; Wiewiora, Anna; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: Recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control. © 2022 Elsevier B.V.. All rights reserved.
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    Adaptive network modeling of the influence of leadership and communication on learning within an organization
    (Elsevier B.V., 2023) Bouma, Debby; Treur, Jan; Wiewiora, Anna; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organizational learning as an alternative route, which includes feed forward and feedback learning. The results show that effective communication (triggered by the active team leader, and/or by natural, informal communication), leads to a faster learning process within an organization compared to the longer route via feed forward and feedback formal organisational learning. However, this more direct form of bilateral learning in general may take more of the employee's time, as a quadratic number of dyadic interactions in general is less efficient than a linear number of interactions needed for feed forward and feedback organisational learning. © 2023 The Authors
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    Computational modelling of the role of leadership style for its context-sensitive control over multilevel organisational learning
    (Springer Nature, 2023) Treur, Jan; Wiewiora, Anna; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed.
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    Modeling context-sensitive metacognitive control of focusing on a mental model during a mental process
    (Springer Nature, 2021) Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    Focusing on a proper mental model during mental processes is often crucial. Metacognition is used to control such focusing in a context-sensitive manner. In this paper, a second-order adaptive mental network model is introduced for this form of metacognitive control. The second-order adaptive network model obtained is illustrated by a case scenario concerning social interaction.
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    Context-sensitive mental model aggregation in a second-order adaptive network model for organisational learning
    (Springer International Publishing AG, 2022) Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    Organisational learning processes often exploit developed individual mental models in order to obtain shared mental models for the organisation by some form of unification or aggregation. The focus in this paper is on this aggregation process, which may depend on a number of contextual factors. It is shown how a second-order adaptive network model for organisation learning can be used to model this process of aggregation of individual mental models in a context-dependent manner.
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    An adaptive self-modeling network model for multilevel organizational learning
    (Springer Nature, 2023) Treur, Jan; Roelofsma, Peter; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    Multilevel organizational learning concerns an interplay of different types of learning at individual, team, and organizational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.
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    Using Boolean Functions of context factors for adaptive mental model aggregation in organisational learning
    (Springer International Publishing AG, 2022) Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    Aggregation of individual mental models to obtain shared mental models for an organization is a crucial process for organizational learning. This aggregation process usually depends on several context factors that may vary over circumstances. It is explored how Boolean functions of these context factors can be used to model this form of adaptation. For adaptation of aggregation of mental model connections (represented by first-order self-model states), a second-order adaptive self-modeling network model for organizational learning was designed. It is shown how in such a network model, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process.
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    PublicationOpen Access
    Computational modeling of organisational learning by self-modeling networks
    (Elsevier, 2022) Treur, Jan; Roelofsma, Peter H. M. P.; Department of Computer Engineering; Canbaloğlu, Gülay; Department of Computer Engineering; Graduate School of Sciences and Engineering
    Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.