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When observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.
Marija Majda Perisic; Mario Štorga; John S. Gero. COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK. Proceedings of the Design Society 2021, 1, 691 -700.
AMA StyleMarija Majda Perisic, Mario Štorga, John S. Gero. COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK. Proceedings of the Design Society. 2021; 1 ():691-700.
Chicago/Turabian StyleMarija Majda Perisic; Mario Štorga; John S. Gero. 2021. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK." Proceedings of the Design Society 1, no. : 691-700.
The conventional prescriptive and descriptive models of design typically decompose the overall design process into elementary processes, such as analysis, synthesis, and evaluation. This study revisits some of the assumptions established by these models and investigates whether they can also be applied for modelling of problem-solution co-evolution patterns that appear during team conceptual design activities. The first set of assumptions concerns the relationship between performing analysis, synthesis, and evaluation and exploring the problem and solution space. The second set concerns the dominant sequences of analysis, synthesis, and evaluation, whereas the third set concerns the nature of transitions between the problem and solution space. The assumptions were empirically tested as part of a protocol analysis study of team ideation and concept review activities. Besides revealing inconsistencies in how analysis, synthesis, and evaluation are defined and interpreted across the literature, the study demonstrates co-evolution patterns, which cannot be described by the conventional models. It highlights the important role of analysis-synthesis cycles during both divergent and convergent activities, which is co-evolution and refinement, respectively. The findings are summarised in the form of a model of the increase in the number of new problem and solution entities as the conceptual design phase progresses, with implications for both design research and design education.
Tomislav Martinec; Stanko Škec; Marija Majda Perišić; Mario Štorga. Revisiting Problem-Solution Co-Evolution in the Context of Team Conceptual Design Activity. Applied Sciences 2020, 10, 6303 .
AMA StyleTomislav Martinec, Stanko Škec, Marija Majda Perišić, Mario Štorga. Revisiting Problem-Solution Co-Evolution in the Context of Team Conceptual Design Activity. Applied Sciences. 2020; 10 (18):6303.
Chicago/Turabian StyleTomislav Martinec; Stanko Škec; Marija Majda Perišić; Mario Štorga. 2020. "Revisiting Problem-Solution Co-Evolution in the Context of Team Conceptual Design Activity." Applied Sciences 10, no. 18: 6303.
The students frequently regard the fundamental mechanical engineering courses as demanding, and they often have high drop-out rates. Due to the width of prerequisite knowledge which needs to be integrated, advanced, and applied, Machine elements is one of such courses. In this article, the authors have used mathematical methods to determine the predictors of student performance in Machine elements course held at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb. The secondary education data and grades of preceding courses were collected for 729 students enrolled in Machine elements course. The obtained data were described using basic statistical methods and further used to develop models for predicting the students’ performance on the Machine elements course. Building on the results, the authors have answered three research questions: The preceding courses are better predictors when compared to secondary education (1). The Strength of Materials and Mathematics II were the best predictors; generally, the course’s complexity, rather than its scope, was an indicator of its importance for the prediction of student’s future success (2). Lastly, it was possible to group the students based on predicted future academic performance which, consequently, enables early segmentation and detection of students at risk (3).
Daniel Miler; Marija Majda Perišić; Robert Mašović; Dragan Žeželj. Predicting student academic performance in Machine elements course. Mechanical Engineering and Materials 2019, 825 -834.
AMA StyleDaniel Miler, Marija Majda Perišić, Robert Mašović, Dragan Žeželj. Predicting student academic performance in Machine elements course. Mechanical Engineering and Materials. 2019; ():825-834.
Chicago/Turabian StyleDaniel Miler; Marija Majda Perišić; Robert Mašović; Dragan Žeželj. 2019. "Predicting student academic performance in Machine elements course." Mechanical Engineering and Materials , no. : 825-834.
The paper presents the results of research aimed at contributing to a better understanding of the effect of team experience and learning on the performance of a design team. An agent-based model of the design team was developed, and computational simulations were utilized to study how agent’s knowledge changes by its use and what are the effects of such changes on the team behavior.
Marija Majda Perišić; Mario Štorga; John S. Gero. Exploring the Effect of Experience on Team Behavior: A Computational Approach. Design Computing and Cognition '18 2019, 595 -612.
AMA StyleMarija Majda Perišić, Mario Štorga, John S. Gero. Exploring the Effect of Experience on Team Behavior: A Computational Approach. Design Computing and Cognition '18. 2019; ():595-612.
Chicago/Turabian StyleMarija Majda Perišić; Mario Štorga; John S. Gero. 2019. "Exploring the Effect of Experience on Team Behavior: A Computational Approach." Design Computing and Cognition '18 , no. : 595-612.
Marija Majda Perišić; Mario Štorga; Vedran Podobnik. Agent-Based Modelling and Simulation of Product Development Teams. Tehnicki vjesnik - Technical Gazette 2018, 25, 524 -532.
AMA StyleMarija Majda Perišić, Mario Štorga, Vedran Podobnik. Agent-Based Modelling and Simulation of Product Development Teams. Tehnicki vjesnik - Technical Gazette. 2018; 25 (Supplement):524-532.
Chicago/Turabian StyleMarija Majda Perišić; Mario Štorga; Vedran Podobnik. 2018. "Agent-Based Modelling and Simulation of Product Development Teams." Tehnicki vjesnik - Technical Gazette 25, no. Supplement: 524-532.