NSF Org: EFMA Emerging Frontiers & Multidisciplinary Activities
Award Number: 1745919
ENG Directorate For Engineering
Start Date: September 1, 2017
End Date: August 31, 2020 (Estimated)
Awarded Amount to Date: $299,962.00
Investigator(s): Dan Perlman firstname.lastname@example.org (Principal Investigator), Andrew Minigan (Co-Principal Investigator), Daniel Rothstein (Co-Principal Investigator), & Luz Santana (Co-Principal Investigator)
Sponsor: Brandeis University
This project is designed to achieve a simple, yet ambitious goal; to create a model that all doctoral students can use to improve their ability to ask questions. Question formulation is an often overlooked yet profoundly important intellectual ability that includes knowing how to produce one’s own questions, refine and improve them, and strategize on how to use the questions to guide research. This study will expand upon an evidence-based method, the Question Formulation Technique (QFT) pioneered by the Right Question Institute, to create a rigorous Question Improvement Model (QIM) that will facilitate a more direct path to better research and new discoveries. It is anticipated that the QIM will make an important contribution by providing a key resource for improving doctoral education. The QIM will have broader impacts in three areas. First, the ability to efficiently produce and improve one’s research questions should serve as a cornerstone of a strong infrastructure for research and education. In turn, this is likely to lead to beneficial economic outcomes. Finally, the current widespread adoption of the Question Formulation Technique in many fields and diverse communities suggests that the QIM could lead to increased participation of women, persons with disabilities, and underrepresented minorities in STEM.
The QFT involves sequential steps focusing on divergent thinking, convergent thinking, and metacognitive thinking, and provides a powerful tool for generating novel questions. The QFT has demonstrated efficacy in K-12 settings, but has not been deployed in graduate education. In this study, the QFT will be refined to provide a structured method for generating and improving questions that is suitable for training graduate students in the natural sciences. In the quasi-experimental design of the project, participants in the experimental group will learn to use the Question Improvement Model (QIM) while the remaining participants (the control group) will not initially receive QIM training. All participants will be tested before, during, and over the course of a year for their skill in formulating questions and attitudes about questions. A crossover design will allow the control group participants to receive identical QIM training to the experimental group following assessment. The QIM training may provide a useful complement to existing graduate engineering pedagogy focusing on identifying solutions to predefined questions. Furthermore, it is anticipated that the QIM will be highly scalable and useful in a wide array of disciplines.