Engineering students designing a statistical procedure for quantifying variability

Hjalmarson, Margret

doi:10.1016/j.jmathb.2007.06.001

Cited by 7 publications

(12 citation statements)

References 20 publications

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“…The Just-in-Time (JIT) Manufacturing MEA was selected as the problem around which GTA feedback was investigated in this study. This MEA had been implemented a number of times in the course (Zawojewski, Diefes-Dux, & Bowman, 2008) and had been the focus of a number of studies (e.g., Carnes, Cardella, & Diefes-Dux, 2010;Diefes-Dux, Zawojewski, & Hjalmarson, 2010;Hjalmarson, 2007). Therefore, student responses to this MEA were well understood, and the formative assessment system for this problem was well developed.…”

Section: Teaching Assistants As Instructorsmentioning

confidence: 99%

“…Typical first responses include computing the mean late time for each company, but the data set is designed to prompt an intellectual need (Harel, 2007) on the part of students to account for greater Journal of Engineering Education 101 (April 2012) 2 complexity. The means for the companies in the given data set are almost equal in order to provoke students to find another statistic to compute to describe the variability in the data set (Carnes, Cardella, & Diefes-Dux, 2010;Hjalmarson, 2007). Further, the problem context and data, when considered together, are designed to prompt students to go beyond the use of standard deviation to a consideration of distribution because, for example, having a low mean and low standard deviation does not necessarily imply that a distribution is skewed toward on-time deliveries.…”

Section: Teaching Assistants As Instructorsmentioning

confidence: 99%

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A Framework for Analyzing Feedback in a Formative Assessment System for Mathematical Modeling Problems

Diefes‐Dux

Zawojewski

Hjalmarson

et al. 2012

J of Engineering Edu

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Background Providing formative feedback on student responses to complex, open‐ended problems is challenging as solutions vary in content and quality. Instructors need to interpret student work and give feedback with high potential to guide students to close the gap between actual and reference level performance. To understand instructor feedback and its impact, a framework for analyzing instructor feedback is necessary. Purpose (Hypothesis) The purposes of this study were to develop a framework to analyze instructor feedback on team responses to open‐ended mathematical modeling problems and to demonstrate the use of the framework in investigating formative assessment systems. Design/Method To develop the framework, formative feedback from graduate teaching assistants on first‐year engineering student team responses to the Just‐In Time Manufacturing Model‐Eliciting Activity (MEA) was analyzed. The framework in conjunction with a four dimensional MEA Rubric was then used to identify patterns in feedback. Results Feedback has both form and substance. Four forms of feedback were identified: comments not intended to prompt change; and questions, open suggestions, and direct suggestions intended to prompt change. The substance was tied to the criteria used to evaluate MEA responses. Results of applying the framework yielded two claims: (1) the rubric criteria influence the form and intent of feedback, and (2) the perceived quality of student work also impacts the form and intent of feedback. Conclusion The framework that emerged from this work is a useful tool for investigating instructor feedback in this context. Further, the framework is believed to be extendable to other learning contexts.

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Section: Teaching Assistants As Instructorsmentioning

confidence: 99%

Section: Teaching Assistants As Instructorsmentioning

confidence: 99%

A Framework for Analyzing Feedback in a Formative Assessment System for Mathematical Modeling Problems

Diefes‐Dux

Zawojewski

Hjalmarson

et al. 2012

J of Engineering Edu

Self Cite

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“…In some cases, tiebreaker statistics were used if the first statistic calculated did not differentiate sufficiently between the images. Use of the mean and the standard deviation was also typical in another statistics-focused MEA 3 . The complex part of the task is not finding the statistics (most teams used Excel® or MatLab®) but in first deciding what to calculate and they interpreting the findings in light of the context.…”

Section: Student Strategiesmentioning

confidence: 99%

Special Session: Model Eliciting Activities: A Construct For Better Understanding Student Knowledge And Skills

Moore¹,

Self²,

Miller³

et al.

2010 Annual Conference &Amp; Exposition Proceedings

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is a former high school mathematics teacher and her research interests are centered on the integration of STEM concepts through contextual problem solving in the mathematics and engineering classroom. She has been developing curricular tools and researching professional development and student learning in this area. Before coming to the University of Minnesota, Dr. Moore received her Ph.D. from the

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“…The JIT MEA, the focus of this study, requires student teams to use their knowledge of mathematics and statistics to develop a procedure (mathematical model) to rank shipping companies in order of most likely to least likely to be able to meet a company"s delivery timing needs 4 . The motivation for developing the procedure is established by using a realistic context in which D. Dalton Technologies (DDT), a manufacturer of advanced piezoceramics and custommade ultrasonic transducers, is unsatisfied with their current shipping service.…”

Section: B the Just-in-time (Jit) Manufacturing Meamentioning

confidence: 99%

“…For DRAFT 1, student teams are required to establish a procedure to rank a number of alternative shipping companies using a small subset of a larger historical data set. The JIT MEA has been the subject of ongoing research into the development of MEAs in general, as well as the iterative development of tools for assessment of student work [3][4][5][6] .…”

Section: B the Just-in-time (Jit) Manufacturing Meamentioning

confidence: 99%

Evaluating Student Responses in Open-Ended Problems Involving Iterative Solution Development in Model-Eliciting Activities

Carnes

Diefes‐Dux

Cardella

2011 ASEE Annual Conference &Amp; Exposition Proceedings

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she worked with the Center for Engineering Learning and Teaching (CELT) and the LIFE Center (Learning in Informal and Formal Environments). She was a CASEE Postdoctoral Engineering Education Researcher at the Center for Design Research at Stanford before beginning her appointment at Purdue. Her research interests include: learning in informal and out-of-school time settings, pre-college engineering education, design thinking, mathematical thinking, and assessment research.

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Engineering students designing a statistical procedure for quantifying variability

Cited by 7 publications

References 20 publications

A Framework for Analyzing Feedback in a Formative Assessment System for Mathematical Modeling Problems

A Framework for Analyzing Feedback in a Formative Assessment System for Mathematical Modeling Problems

Special Session: Model Eliciting Activities: A Construct For Better Understanding Student Knowledge And Skills

Evaluating Student Responses in Open-Ended Problems Involving Iterative Solution Development in Model-Eliciting Activities

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