Engin Bumbacher

Position:
PhD Student
Institution:
Stanford University Graduate School of Education
Responsibilities:
Research, Facilitation
Description:

In his research, Engin Bumbacher explores alternative approaches to STEM education that leverage novel technologies, teaching and learning practices. This work is motivated in part by the question of what it means when people say that they are or are not the 'science type' of person, and how such self-limitations can be overcome. To this end, his work can be divided into two parts: first, the use of machine learning techniques to advance understanding of the students' cognitive and learning processes based on unstructured data, such as artifacts, programming codes, discussion threads, etc.; and second, the design of frameworks for technologies that foster the students' self-efficacy and learning process in project-based learning environments. He is particularly interested in developing measures that well-capture progress in such learning environments.

Engin Bumbacher holds a BSc in Physics and MSc in Neural Systems and Computation from the Swiss Federal Institute of Technology, Zurich.

 

Current research topics:

My general interests lie in STEM education, how science education can be done such that people see that:
1. science is relevant to their lifes,
2. science is about messing in a systematic way with things,
3. they can use science for artistic and self expression, and
4. that everyone has the ability to make sense of something by just asking questions and sincerely answering them.
I believe that given the right means and put in the right context, people can make sense of phenomena at hand through scientific inquiry. I want to understand what this context and these means are. Instead of just looking at hands-on vs simulation, I believe we should look at relevant design features of any learning environment or toolkit, that is designed for science inquiry. In line with that I am currently wrapping up a research project that looked at how people make sense first of mass&spring systems and zhen electric circuits, either in a hands-on or simulation environment. In both cases, the environments differ in terms of how easy it is for people to change something, and how clear the output and feedback of the system is. I found that indeed, while in mass&spring people of the hands-on group are more systematic in their inquiry and hence learn more, it is the opposite for electric circuits. So, the environment influences the strategies used by people.

Future plans:

1. do similar studies of science inquiry for biology using the remote biology labs

2. expand on the presented research project and examine the following:

A. how does instruction targeting mechanistic explanations impact scientific inquiry as opposed to mathematical equations-driven instruction?

B. What are crucial design features of a learning toolkit that enhances scientific inquiry?

C. What is the impact of talking about strategies in general on strategy use in specific contexts?

3. what psychological processes enhance or hinder science inquiry? What happens to their reasoning when people afraid of science are confronted with their fear?

People at the TLTL

Faculty
Executive Director
Post-Doctoral Researcher
Post-Doctoral Researcher
PhD Student
PhD Student
PhD Student
PhD Student
FabLearn Research Fellow
Staff
Staff
Staff
Staff
Collaborator
Collaborator
Collaborator
Collaborator
Collaborator
Collaborator