The questions we ask and the way in which we ask them can make all the difference in how successful we are in meetings, in collaborations, and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? In this paper, we develop a theory for asking great questions that elicit information useful for accomplishing the tasks of a collaborative project and also strengthen the statistician-domain expert relationship. We deconstruct asking great questions into three parts: the question, the answer, and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question, follow the question with behaviors and actions consistent with the prefaced words including actions such as listening, paraphrasing, and summarizing; and model a collaborative relationship via the asking of a great question. We provide practical guidelines for learning these skills so that statisticians can improve their statistical collaboration skills and thus increase their impact to help address societal challenges.
more »
« less
Beyond Content, Understanding What Makes Test Questions Most Challenging
When students answer test questions incorrectly, we often assume they don't understand the content; instead, they may struggle with certain cognitive skills or with how questions are asked. Our goal was to look beyond content to understand what makes assessment questions most challenging. On the basis of more than 76,000 answers to multiple-choice questions in a large, introductory biology course, we examined three question components—cognitive skills, procedural knowledge, and question forms—and their interactions. We found that the most challenging questions require the students to organize information and make meaning from it—skills that are essential in science. For example, some of the most challenging questions are presented as unstructured word problems and require interpretation; to answer correctly, the students must identify and extract the important information and construct their understanding from it. Our results highlight the importance of teaching students to organize and make meaning from the content we teach.
more » « less- PAR ID:
- 10402027
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- BioScience
- Volume:
- 73
- Issue:
- 3
- ISSN:
- 0006-3568
- Format(s):
- Medium: X Size: p. 229-235
- Size(s):
- p. 229-235
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges.
-
Context Engineering design skills are essential for engineering students to succeed in their careers. Engineering design is a skill that is in high demand in the current job market and should be prioritized in education. Purpose While design has been acknowledged as a cognitive skill in research, there exists limited literature addressing the cognitive foundations of design thinking. Hence, engineering educators must understand the engineering design process, as well as the different ways students approach design problem-solving and the potential reason behind these differences. To understand how people solve design problems, we need to consider how their minds work and the strategies they use. Spatial ability stands out as a cognitive factor that is crucial for designers and holds significance in well-established theories and models of intelligence. However, to date, research exploring the impact of spatial ability on design thinking and its influence on problem-scoping behaviors remains limited. This paper examines how engineering students’ spatial skills influence how they define the scope of open-ended design problems. The central research question that guides this paper is “How do design problem-scoping behaviors differ for engineering students based on their spatial scores?”. Methods The researchers used a mixed methods research approach to answer their research question, collecting qualitative and quantitative data in two phases. One hundred twenty-seven undergraduate engineering students completed four tests that measure spatial reasoning skills in the quantitative phase and 101 students returned to finish the three design tasks in the second phase. This paper will examine the performance of students with low spatial and high spatial skills on one of the completed design tasks. Outcomes From the study, it was clear that spatial skills have an impact on the design-scoping behaviors of the undergraduate engineering students. It was inferred that high spatial skill visualizers emphasized the technical details of the design problem whereas low spatial skill visualizers emphasized the context of the design problem during their problem-scoping behavior. A Mann-Whitney test revealed there was a statistically significant difference in detail- and context-focused segments between the high and low spatial visualizer groups. Conclusion This research study confirms that a relationship exists between spatial and design skills. The study also found that undergraduate engineering students with different levels of spatial skills had different approaches to scoping design problems.more » « less
-
Context Engineering design skills are essential for engineering students to succeed in their careers. Engineering design is a skill that is in high demand in the current job market and should be prioritized in education. Purpose While design has been acknowledged as a cognitive skill in research, there exists limited literature addressing the cognitive foundations of design thinking. Hence, engineering educators must understand the engineering design process, as well as the different ways students approach design problem-solving and the potential reason behind these differences. To understand how people solve design problems, we need to consider how their minds work and the strategies they use. Spatial ability stands out as a cognitive factor that is crucial for designers and holds significance in well-established theories and models of intelligence. However, to date, research exploring the impact of spatial ability on design thinking and its influence on problem-scoping behaviors remains limited. This paper examines how engineering students’ spatial skills influence how they define the scope of open-ended design problems. The central research question that guides this paper is “How do design problem-scoping behaviors differ for engineering students based on their spatial scores?”. Methods The researchers used a mixed methods research approach to answer their research question, collecting qualitative and quantitative data in two phases. One hundred twenty-seven undergraduate engineering students completed four tests that measure spatial reasoning skills in the quantitative phase and 101 students returned to finish the three design tasks in the second phase. This paper will examine the performance of students with low spatial and high spatial skills on one of the completed design tasks. Outcomes From the study, it was clear that spatial skills have an impact on the design-scoping behaviors of the undergraduate engineering students. It was inferred that high spatial skill visualizers emphasized the technical details of the design problem whereas low spatial skill visualizers emphasized the context of the design problem during their problem-scoping behavior. A Mann-Whitney test revealed there was a statistically significant difference in detail- and context-focused segments between the high and low spatial visualizer groups. Conclusion This research study confirms that a relationship exists between spatial and design skills. The study also found that undergraduate engineering students with different levels of spatial skills had different approaches to scoping design problems.more » « less
-
In this project, we sought to uncover the cognitive processes and skills that are involved in completing a theoretical physics project. Theoretical physics is often portrayed as a field requiring individual genius and can seem inaccessible to undergraduate students, as well as the public. We drew upon the foundations of Cognitive Task Analysis and completed semi-structured interviews with eleven theoretical physics faculty members from several different research institutions who specialized in subfields including quantum optics, biophysics, computational astrophysics, and string theory. We analyzed the processes and skills of these physicists, focusing on an analysis of idea origin, which is typically the first cognitive process within a project, and how it was connected to collaboration and motivation. We used concept maps to organize these codes and portray the factors that influence the creation of project ideas. We found that motivation and collaboration are fundamental determinants of project ideas and their origins, which contradicts the "lone genius" stereotype. These findings on cognitive processes and skills can help us understand how to better prepare students to do theoretical physics research. Finally, the information gathered during this project may be useful for improving the public understanding of theoretical physics, dispelling the belief that the field requires "genius," and making it accessible to more students.more » « less
