About

John Steven Hutchinson is a Professor of Chemistry at Rice University and serves as the Associate Chair for Undergraduate Studies in the Department of Chemistry. His research specialties are in theoretical chemical physics, specifically computational studies of the vibrational dynamics of reactive molecules, and in chemical education research, focusing on the effectiveness of educational innovations. He has published over 50 papers on these subjects and supervised the doctoral dissertations of eight students and the master’s theses of three students. His research has been supported by organizations such as the National Science Foundation, the Robert A. Welch Foundation, the Research Corporation, the Petroleum Research Fund, the Brown Foundation, and the Dreyfus Foundation. Hutchinson has taught General Chemistry for twenty-eight years and developed various approaches to teaching introductory science. He created an online textbook, 'Concept Development Studies in Chemistry,' designed for interactive, constructivist teaching. For the past decade, he has also taught courses to high school science teachers on the concept development approach and has presented invited papers on interactive teaching at numerous conferences and universities. His professional service includes serving on the College Board’s Curriculum Design and Assessment Committee for the Advanced Placement program in Chemistry. He earned his undergraduate degree with highest honors from the University of Texas in 1977 and completed his doctoral work there, with his dissertation recognized as the Outstanding Dissertation in the university in 1981. Before joining Rice University in 1983, he taught at the University of Denver and the University of Colorado, Boulder. At Rice, he has held several administrative roles, including Dean of Undergraduates from 2010 to 2018, Associate Vice President for Student Affairs, Interim Vice President for Student Affairs, and Director of Academic Advising. He has also served as the resident faculty master of Brown College and Wiess College. Hutchinson has received numerous awards for his teaching and contributions to undergraduate education, including the George R. Brown Certificate of Highest Merit, multiple George R. Brown Awards for Teaching Excellence and Superior Teaching, the Nicolas Salgo Distinguished Teacher Award, the Phi Beta Kappa Teaching Prize, and recognition as a Piper Professor by the Minnie Stevens Piper Foundation. He is a native of Texas, born in Dallas and raised in Corpus Christi, and is married to Paula Krumboltz Hutchinson, with whom he has two daughters. In his spare time, he enjoys playing folk guitar and hiking in the Colorado mountains.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Information Retrieval
• Political Science
• Natural Language Processing
• Data science
• Mathematics education
• Multimedia
• Pedagogy
• Telecommunications
• Psychology
• Business
• Environmental science
• Nuclear engineering

Selected publications

• A Comparative Study of Two Active Learning Approaches for General Chemistry

  Journal of Chemical Education · 2024-11-23 · 3 citations

  articleSenior authorCorresponding

  Active learning has been shown to increase student learning outcomes and engagement in STEM (Science, Technology, Engineering, and Mathematics) courses, which has prompted many instructors to integrate more active learning techniques into their classrooms. For the past three decades at Rice University, General Chemistry has been taught using one such approach that combines Socratic dialogue with a curriculum based on the Concept Development Study method. In recent years, we have also developed and analyzed the effectiveness of the Student-Centered Active Learning at Rice (SCALAR) approach, which incorporates regular small group discussions into Socratic dialogue. Here, we report on a side-by-side study directly comparing how these two active learning pedagogies impact the outcomes of three different sets of student groups: silent versus vocal students, female versus male students, and first-generation students versus continuing-generation students. We found that both active learning pedagogies produced significant learning gains for students in all cohorts.

  PublisherDOI

• Automated Long Answer Grading with RiceChem Dataset

  arXiv (Cornell University) · 2024 · 1 citations

  • Computer Science
  • Natural Language Processing
  • Computer Science

  We introduce a new area of study in the field of educational Natural Language Processing: Automated Long Answer Grading (ALAG). Distinguishing itself from Automated Short Answer Grading (ASAG) and Automated Essay Grading (AEG), ALAG presents unique challenges due to the complexity and multifaceted nature of fact-based long answers. To study ALAG, we introduce RiceChem, a dataset derived from a college chemistry course, featuring real student responses to long-answer questions with an average word count notably higher than typical ASAG datasets. We propose a novel approach to ALAG by formulating it as a rubric entailment problem, employing natural language inference models to verify whether each criterion, represented by a rubric item, is addressed in the student's response. This formulation enables the effective use of MNLI for transfer learning, significantly improving the performance of models on the RiceChem dataset. We demonstrate the importance of rubric-based formulation in ALAG, showcasing its superiority over traditional score-based approaches in capturing the nuances of student responses. We also investigate the performance of models in cold start scenarios, providing valuable insights into the practical deployment considerations in educational settings. Lastly, we benchmark state-of-the-art open-sourced Large Language Models (LLMs) on RiceChem and compare their results to GPT models, highlighting the increased complexity of ALAG compared to ASAG. Despite leveraging the benefits of a rubric-based approach and transfer learning from MNLI, the lower performance of LLMs on RiceChem underscores the significant difficulty posed by the ALAG task. With this work, we offer a fresh perspective on grading long, fact-based answers and introduce a new dataset to stimulate further research in this important area. Code: \url{https://github.com/luffycodes/Automated-Long-Answer-Grading}.

  PublisherOA PDFDOI

• Relating Bond Orders and Bond Energies to Ionization Energies and Electron Affinities: A Quantitative Approach

  Journal of Chemical Education · 2024-07-12 · 3 citations

  article1st authorCorresponding

  In General Chemistry, students are often asked to predict changes in bond energy based on changes in bond order when electrons are either added to or removed from a molecule. This gives a qualitative understanding of the correlation between bond order and bond energy, but it is not descriptive of the actual changes in the bond energies involved. In this paper, we present a quantitative approach to this prediction based on ionization energy, electron affinity, and conservation of energy (or Hess’ law). These are all concepts that are familiar to General Chemistry students. With this quantitative approach, we can more fully illustrate how bonding and antibonding electrons contribute to bond strength and better relate these to ionization energies and electron affinities. Multiple examples are given to illustrate a range of possibilities and to compare variations of bond energies.

  PublisherDOI

• Automated Long Answer Grading with RiceChem Dataset

  Lecture notes in computer science · 2024-01-01 · 11 citations

  book-chapter
  PublisherDOI

• Silent Students in the Active Learning Classroom

  Springer eBooks · 2020 · 4 citations

  Senior authorCorresponding
  • Computer Science
  • Mathematics education
  • Artificial Intelligence
  PublisherDOI

• Application of the Rossi-alpha method to simulations of HEU and organic scintillators

  Transactions of the American Nuclear Society - Volume 123 · 2020 · 4 citations

  • Computer Science
  • Environmental science
  • Computer Science
  PublisherDOI

• Update on Benchmark Analysis of Component Critical Configurations of KRUSTY

  Transactions of the American Nuclear Society - Volume 121 · 2019-01-01

  article
  PublisherDOI

• The Varied and Vital Roles of Faculty in Residential College Life

  SensePublishers eBooks · 2016-01-01

  book-chapter1st authorCorresponding

  The strengths arising from the residential college system are multiple and varied, as detailed in the chapters of this book. Many of these arise from the opportunities created by the residential college for students to work together in an organised community. However, the residential college organisation also creates a variety of unique opportunities for interactions between faculty and students. Significantly, these faculty activities enhance the students’ university experiences both within the residential college and in the classroom, the research programme, and beyond.

  PublisherDOI

• Scholarship of Teaching: Online Courses as a Means of Publishing Innovations

  ACS symposium series · 2016-01-01

  book-chapter1st authorCorresponding

  Though the scholarship of teaching has been increasingly recognized in the past two decades, teaching as a form of scholarship differs from other forms in that it is much more difficult to publish. We publish lesson plans, we publish innovative ideas, and we publish research on teaching. But publishing the teaching itself requires non-traditional means. The rise in availability of online courses creates exciting possibilities for demonstrating how lesson plans, innovative ideas, and creative approaches are actually implemented. At Rice University, we have designed a novel General Chemistry curriculum based on the development of concepts through inductive reasoning. Teaching using the Concept Development Study approach has been published via our Coursera courses, Chemistry Concept Development and Application. In this chapter, we will discuss our motivations, approaches, observations, and results.

  PublisherDOI

• Data First: Building Scientific Reasoning in AP Chemistry via the Concept Development Study Approach

  Journal of Chemical Education · 2014-07-31 · 11 citations

  articleSenior author

  This article introduces the “Data First” approach and shows how the observation and analysis of scientific data can be used as a scaffold to build conceptual understanding in chemistry through inductive reasoning. The “Data First” approach emulates the scientific process by changing the order by which we introduce data. Rather than using data to solve problems after the concept is taught, the “Data First” approach allows students to analyze trends, evaluate discrepant events, and construct knowledge by developing a strong conceptual foundation. Not only is this approach aligned to the AP Chemistry Curriculum Framework, it also is based on research about how people learn. Included are examples showing how this approach can be used to teach a range of chemistry topics. These include using photoelectron spectroscopy data to understand electron configuration, using heats of vaporization to understand intermolecular forces, and using vapor pressure to understand dynamic equilibrium. The “Data First” approach is a resource to support the shift to more conceptual and inquiry-based teaching and learning in AP chemistry. This contribution is part of a special issue on teaching introductory chemistry in the context of the advanced placement (AP) chemistry course redesign.

  PublisherDOI

Frequent coauthors

• William P. Reinhardt

  University of Washington

  27 shared

• James T. Hynes

  University of Colorado Boulder

  26 shared

• P. J. Broadbent

  University of Leeds

  19 shared

• Edwin L. Sibert

  University of Wisconsin–Madison

  15 shared

• Kevin D. Sinclair

  University of Nottingham

  7 shared

• F. T. G. Prunty

  St Thomas' Hospital

  7 shared

• Thomas A. Holme

  Iowa State University

  7 shared

• Carrie Obenland

  San Jacinto College

  6 shared

Education

• Doctor of Philosophy, Chemistry

  University of Texas

  1981

Awards & honors

• George R. Brown Certificate of Highest Merit (2007)
• George R. Brown Award for Teaching Excellence (1997, 2004)
• George R. Brown Award for Superior Teaching (1994, 1996, 199…
• Nicolas Salgo Distinguished Teacher Award (1988, 1995)
• Phi Beta Kappa Teaching Prize (1987)