Roman Taraban received a 2020 Chancellor’s Council Distinguished Teaching Award.
In February, the Texas Tech University System announced its 2020 Chancellor's Council Distinguished Teaching and Research Awards to honor outstanding faculty members who provide exceptional opportunities for students both in and out of the classroom. We are highlighting the seven Texas Tech University faculty members who were recognized. This is the seventh in this series.
Roman Taraban, a professor of experimental psychology in the Department of Psychological Sciences, within the College of Arts & Sciences, loves learning in all forms. While this certainly carries through his research, much of which focuses on machine learning, it's also true of his interactions in the classroom. He enjoys teaching because he can help students learn and, at the same time, he can learn how they think.
Perhaps for this reason, he doesn't limit himself to teaching psychology students. In addition to that subject, he's served as a faculty adviser for the theses and dissertations of students in computer science, information systems, communications, higher education, business administration, special education, classical and modern languages, plant and soil science, instructional technology, industrial engineering, applied linguistics, mechanical engineering and art.
With such a passion for learning, it may come as no surprise that he's deeply involved in education. Taraban was inducted into the Texas Tech Teaching Academy in 2003 and became an Integrated Scholar in 2014. As a former Fulbright Scholar (2010), he serves on the university's Fulbright committee, is a faculty adviser for the Center for the Integration of STEM Education & Research (CISER) and is a member of the research committee for the STEM Center for Outreach, Research & Education.
Along the way, he's received numerous awards, including the Texas Tech President's Excellence in Teaching Award in 2013, the President's Academic Achievement Award in 2016, the College of Arts & Sciences' Teaching Innovation Award in 2017, the CISER Light Year Award and the President's Excellence in Teaching Professorship in 2018, not to mention awards from the National Association of Developmental Education and the American Society of Engineering Education.
On Feb. 5, Taraban added the newest entry to this list: a 2020 Chancellor's Council Distinguished Teaching Award.
Can you describe your research and its impact, both in academics and society?
My current research focuses primarily on engineering ethics. The research is a collaboration
with my Texas Tech colleague William Marcy in the Murdough Center for Engineering Professionalism and colleagues in India and is currently supported by the Texas Tech Center for Global Communication. On one hand, we are interested in how engineering students reason about ethical
issues and how thinking might differ between U.S. and Indian students. We also are
developing machine methods to analyze students' reactions to ethical dilemmas, which
they submit to the online platform Ethical Engineer. This work is currently of special interest to engineering educators in the U.S.
and in India and has been presented at conferences here and abroad.
What projects are you working on at this time?
• National Science Foundation-Innovation in Graduate Education supported research
with Danny Reible in civil, environmental and construction engineering, developing a graduate engineering course that incorporates arts and the humanities.
• An undergraduate-education project supported by the Texas Tech Center for Global
Communication using the Ethical Engineer platform to encourage cross-cultural exchanges
between U.S. and Indian students.
• I am the assessment director for CISER, with a primary focus on developing a worldwide
presence of Texas Tech undergraduate researchers through the online Emerging Research platform.
• Applied research on metacognitive strategies that engineering students use to solve
problems, with Edward Anderson in mechanical engineering and Sheima Khatib in chemical engineering.
What areas are you interested in for future research?
My doctoral training is in psycholinguistics. Language processing issues run through
all my research projects. I currently have a deep interest in expanding machine tools
and artificial intelligence (AI) to improve the ability of these tools to process
meaning.
What rewards do you get from teaching?
My greatest rewards come from interacting with students in the classroom. I love challenging
students with questions as I teach and learning about their perspectives and ways
of thinking.
What motivated you to pursue a career in academia?
My primary motivation has been a simple love of knowledge and an interest in basic
questions related to what we know, what it means to think and the nature and limits
of language. What better place to reflect on these questions than in academia?
How has Texas Tech helped you advance your research and teaching?
Since my arrival at Texas Tech in 1989, the university has allowed me to freely pursue
productive and fulfilling interdisciplinary research projects. One of the longest
and fruitful relationships has been with Dr. Anderson in mechanical engineering. Another
long-term association has been with Julie Isom and CISER. More recent collaborations
include those with Dr. Marcy and Dr. Reible. These, and other collaborations, have
allowed me to contribute my expertise in learning and assessment to a variety of applied
projects that have been funded by the Howard Hughes Medical Institute, the National
Science Foundation and the Fulbright program.
Who has had the biggest impact on you and your career, and why?
In terms of my theoretical orientation in cognitive science and my teaching and research
in this field, I owe a great deal to the influence and mentorship of Herbert Simon,
James McClelland and Brian MacWhinney at Carnegie Mellon University, who were there
when I did my doctoral work. Each of them, in their own distinct way, has left an
indelible impact on cognitive theories and research. At the time of my doctoral work,
there was a bit of a rift between traditional models of cognition, AI and emerging
neural brain-style models. Much of what was potentially pulling the discipline apart
at that time has come together theoretically and practically in contemporary cognitive
neuroscience and deep-learning AI.
Is there anything else you'd like to add?
I appreciate the support that Texas Tech has provided over the course of my career.
