Umit AslanNorthwestern University | NU · School of Education and Social Policy
Umit Aslan
PhD Candidate in Learning Sciences
About
12
Publications
892
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38
Citations
Citations since 2017
Introduction
Navigating the ever increasing complexity in our daily lives as individuals, but also as members of collective societies, is a primary concern of our time. We struggle with figuring out who to trust, where to move, what to invest in, and even what to buy in the supermarket because all of these decisions require reckoning with concepts such as emergence, feedback loops, and uncertainty. So, I primarily conduct studies on how people intuitively reason about complex systems. In addition, I develop
Education
September 2010 - April 2014
September 2005 - June 2010
Publications
Publications (12)
In the decades since Papert published Mindstorms (1980), computation has transformed nearly every branch of scientific practice. Accordingly, there is increasing recognition that computation and computational thinking (CT) must be a core part of STEM education in a broad range of subjects. Previous work has demonstrated the efficacy of incorporatin...
Code-first learning entails the use of computer code to learn a concept, and creating computational models is one such effective method for learning about scientific phenomena. Many code-first learning approaches employ the visual block-based programming paradigm in order to be accessible to school children with no prior programming experience, pro...
We propose agent-based construction (a-b-c) interviews as a new research methodology specifically designed to expose patterns of reasoning about emergent phenomena and complex systems. In an a-b-c interview, the researcher acts as an active mediator between the participant and an agent-based modeling environment. As the participant describes the mo...
Multi-agent modeling is a computational approach to model behavior of complex systems in terms of simple micro level agent rules that result in macro level patterns and regularities. It has been argued that complex systems approaches provide distinct advantages over traditional equation-based mathematical modeling approaches in the process of scien...
Multi-agent modeling is a computational approach to model behavior of complex systems in terms of simple micro level agent rules that result in macro level patterns and regularities. It has been argued that complex systems approaches provide distinct advantages over traditional equation-based mathematical modeling approaches in the process of scien...
Being able to recognize the impossibility or the very weak probability of an outcome, such as winning the lottery, is an important challenge in probabilistic reasoning. We reproduce a classical Piagetian experiment using computer-based modeling to test if we can replicate Piaget and Inhelder's findings on the idea of "chance as the negotiation of m...
An ordinary, non-scientist person's exposure to science through pop-culture is ever growing. However, science is still believed to have a high threshold of entry. For most people, doing science means going through rigorous training of literature, specialization, and learning formal mathematics. Many people feel that they have no means to figure out...
Teaching programming and creating games have attracted much attention over the years, mostly the attention of curriculum developers and teachers. This study designed and developed a video game-based intervention, then investigated the effects of this intervention on middle school students’ learning of probability concepts. In the study, the student...
Projects
Project (1)
Phenomenological programming is a new approach to creating code-first science learning environments with the aim of making computer programming similar to how students intuitively perceive the world around them. It uses a block-based environment built with NetTangoWeb (similar to Scratch or PencilCode), but aims to help students start creating agent-based models of real world phenomena using custom-made code blocks with little to no programming instruction by their teachers. I design the code blocks according to students’ intuitive understanding of the real-world objects, patterns, and events. For example, instead of using blocks such as "move 10", "turn 15 degrees" etc., students use blocks like "move erratically" or "bounce like a balloon".
