Partnering Strategies for Paired Formative Assessment in Programming

Shanon Marie Reckinger; Bryce E. Hughes

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Computing & Information Technology: Curriculum and Assessment
Tagged Division: Computing and Information Technology
Page Count: 11
DOI: 10.18260/1-2--30858
Permanent URL: https://strategy.asee.org/30858
Download Count: 461

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Paper Authors

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Shanon Marie Reckinger Stanford University

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Shanon Reckinger was a faculty in the department of Mechanical and Industrial Engineering at Montana State University from 2015-2017. Before her position at MSU, she was a Clare Boothe Luce Professor at Fairfield University in the department of Mechanical Engineering for four years. She received her PhD in Mechanical Engineering at the University of Colorado Boulder in August of 2011. Her research interests include ocean modeling, computational fluid dynamics, fluid dynamics, and numerical methods. Shanon has taught courses in thermodynamics, numerical methods (graduate), fluid dynamics, gas dynamics (graduate), computational fluid dynamics (undergrad/graduate), fundamentals of engineering, mathematical analysis in MATLAB. Currently, Shanon is enrolled in the Computer Science Education program at Stanford University.

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Bryce E. Hughes Montana State University orcid.org/0000-0001-9414-394X

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Bryce E. Hughes is an Assistant Professor in Adult and Higher Education at Montana State University, and holds a Ph.D. in Higher Education and Organizational Change from the University of California, Los Angeles, as well as an M.A. in Student Development Administration from Seattle University and a B.S. in General Engineering from Gonzaga University. His research interests include teaching and learning in engineering, STEM education policy, and diversity and equity in STEM.

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Abstract

In this paper, we present new partnering strategies to pair students for formative assessment in a programming course. These strategies, collaborative and cooperative learning, are two forms of active learning. Both forms have been widely tested in previous classroom experiments and show evidence of effectiveness. This study focused the application on how formative assessment (i.e. a weekly quiz) is administered, rather than on learning activities in general. A collaborative quiz is one based on the idea of paired programming, a technique that has become well-received in computer science. This type of quiz requires the pair of students to program together, using one computer to turn in one final solution. The second method, a cooperative quiz, allows students to collaborate as much or as little as they prefer. For a cooperative quiz, students must each use their own computer to write and submit their own solution, but any collaboration with their partner is allowed. To account for student preferences for active or independent learning, these strategies were also guided by how students are paired. To do this, the Felder-Silverman learning style inventory was used to categorize students along the model’s active-reflective dimension as reflective, active, or “in-between” (neutral) learners. Students were partnered throughout the semester with a variety of partners with attention to mixing and matching their preferred learning styles to determine any effects of partnering.

This study took place across three sections of a sophomore-level programming class in a mid-size, public university in the western United States. The sample included approximately 100 primarily second-year mechanical engineering undergraduate students. The course is a required programming course using MATLAB. A weekly quiz was administered across each of the sections differently. This was done to test the relationship between strategy and assessment performance. Therefore, each week there was a section of students taking a collaborative, cooperative, and independent quiz. All sections were given the opportunity to take the same total number of each quiz type and all sections were taught by the same instructor. A standardized rubric was used to score student performance and compare across sections, across quiz styles, and across learning styles.

The research question guiding this paper is: how does the quiz style and/or partner learning style preference affect student performance on formative assessment? Both quantitative and qualitative methods were used to address this question. Statistical analysis was used to determine the average differences in quiz performance based on quiz style, mixed or matched learning style partnering, and the combination of the above. Several quiz sessions were videotaped, and this data was used to discern the interpersonal dynamics of different partnering conditions. This paper will focus on those results. The results from the statistical analysis demonstrates that working with a partner improved student performance over individual assessment. Video observations confirmed the statistical results, and provide more detail regarding the interpersonal dynamics of each type of student pairing. Additionally, we will present student narrative feedback.

Citation
Format

Reckinger, S. M., & Hughes, B. E. (2018, June), Partnering Strategies for Paired Formative Assessment in Programming Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30858

TY - CPAPER
AB - In this paper, we present new partnering strategies to pair students for formative assessment in a programming course. These strategies, collaborative and cooperative learning, are two forms of active learning. Both forms have been widely tested in previous classroom experiments and show evidence of effectiveness. This study focused the application on how formative assessment (i.e. a weekly quiz) is administered, rather than on learning activities in general. A collaborative quiz is one based on the idea of paired programming, a technique that has become well-received in computer science. This type of quiz requires the pair of students to program together, using one computer to turn in one final solution. The second method, a cooperative quiz, allows students to collaborate as much or as little as they prefer. For a cooperative quiz, students must each use their own computer to write and submit their own solution, but any collaboration with their partner is allowed. To account for student preferences for active or independent learning, these strategies were also guided by how students are paired. To do this, the Felder-Silverman learning style inventory was used to categorize students along the model’s active-reflective dimension as reflective, active, or “in-between” (neutral) learners. Students were partnered throughout the semester with a variety of partners with attention to mixing and matching their preferred learning styles to determine any effects of partnering.

This study took place across three sections of a sophomore-level programming class in a mid-size, public university in the western United States. The sample included approximately 100 primarily second-year mechanical engineering undergraduate students. The course is a required programming course using MATLAB. A weekly quiz was administered across each of the sections differently. This was done to test the relationship between strategy and assessment performance. Therefore, each week there was a section of students taking a collaborative, cooperative, and independent quiz. All sections were given the opportunity to take the same total number of each quiz type and all sections were taught by the same instructor. A standardized rubric was used to score student performance and compare across sections, across quiz styles, and across learning styles.

The research question guiding this paper is: how does the quiz style and/or partner learning style preference affect student performance on formative assessment? Both quantitative and qualitative methods were used to address this question. Statistical analysis was used to determine the average differences in quiz performance based on quiz style, mixed or matched learning style partnering, and the combination of the above. Several quiz sessions were videotaped, and this data was used to discern the interpersonal dynamics of different partnering conditions. This paper will focus on those results. The results from the statistical analysis demonstrates that working with a partner improved student performance over individual assessment. Video observations confirmed the statistical results, and provide more detail regarding the interpersonal dynamics of each type of student pairing. Additionally, we will present student narrative feedback.
AU - Shanon Marie Reckinger
AU - Bryce E. Hughes
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Partnering Strategies for Paired Formative Assessment in Programming
UR - https://strategy.asee.org/30858
DO - 10.18260/1-2--30858
ER -