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Examining Choice in Self-directed Tiered Homework Assignments in College-Level Engineering Courses

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

NSF Grantees Poster Session I

Tagged Topic

NSF Grantees Poster Session

Page Count

7

DOI

10.18260/p.26791

Permanent URL

https://strategy.asee.org/26791

Download Count

179

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Paper Authors

biography

Vasiliki Ikonomidou George Mason University

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Vasiliki N. Ikonomidou earned the Diploma in Engineering (MSc) degree and the PhD in Electrical Engineering from the Aristotle University of Thessaloniki, Greece, in 1997 and 2002 respectively. Her PhD thesis focused on the development of excitation techniques for magnetic resonance tagging. In May 2003 she joined the intramural program of the National Institute of Neurological Disorders and Stroke (NINDS) of the NIH (Bethesda, MD), as a Visiting Fellow in the Laboratory for Functional and Molecular Imaging, working in the development of high-contrast anatomical MRI techniques. In May 2006, she joined the Neuroimmunology Branch of NINDS, where she worked on brain imaging, using MRI and PET, in patients with Multiple Sclerosis. Since August 2009, she has been with George Mason University, where she is an Assistant Professor of Bioengineering. Her research interests are in the fields of video analytics for stress detection, MRI image processing, and differentiated learning techniques for engineering education. Dr Ikonomidou has co-authored 22 papers in peer-reviewed journals, and is a member of IEEE.

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Anastasia P Samaras George Mason University, VA USA

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ANASTASIA P. SAMARAS is Professor of Education in the College of Education and Human Development at George Mason University, USA. She is an educational researcher and pedagogical scholar with signature work in self-study research methodology including co-editor of Polyvocal Professional Learning through Self-Study Research (2015) and author of Self-Study Teacher Research (2011) and lead editor of Learning Communities In Practice (2008). She is recipient of the Dissertation Research Award, University of Virginia, the Outstanding Scholar Award, University of Maryland, a Fulbright Scholar, and a Visiting Self-study Scholar. She served as chair of S-STEP from 2013-2015 and is a current Co-PI of two National Science Foundation (NSF) funded grants: Designing Teaching: Scaling up the SIMPLE Design Framework for Interactive Teaching Development and a research initiation grant: Student-directed differentiated learning in college-level engineering education. Her research centers on facilitating and studying her role in faculty development self-study collaboratives.

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biography

Vikas Kotari George Mason University

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Vikas Kotari is a PhD candidate in the electrical and computer engineering department at George Mason University, Fairfax, VA. He earned his M.S in electrical engineering from George Mason University in Dec 2009. His PhD thesis focuses on characterization and longitudinal tracking of multiple sclerosis (MS) lesions in brain magnetic resonance images. Vikas uses image registration, subtraction imaging, and segmentation algorithms among other image processing and image analysis techniques to characterize and track MS lesions changes. Vikas has interned at Genentech, South San Francisco. During this time he has developed an image analysis algorithm to segment in-vivo volumetric micro-computed tomography mice data. In addition, Vikas has developed a multispectral fusion based detection technique for Virchow-Robin spaces. His research interests include medical image processing, texture analysis for evaluating accuracy of registration, linear and non-linear image registration techniques and, image segmentation algorithms for segmenting brain magnetic resonance data.

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Abstract

The concept of differentiated instruction (DI), i.e., adjusting material delivery according to the readiness of each student, has gained substantial interest in K-12 education. Current literature suggests that it is an effective option for designing instruction in classrooms where there is significant difference in students’ academic readiness levels. Evidence suggests that different readiness levels are a reality in college classrooms, and with it come different student needs in order to succeed. However, there has been significantly less interest in the application of differentiated learning techniques at the college level and which this study begins to address. This may be due to the level of commitment required for DI, where the burden of design of individualized mini-curricula for each student falls on the instructor, and may not be realistic for engineering college faculty. Additionally, college students are adults, and adult learning theory suggests that as such they should be responsible for their own learning. Based on these notions, our wider question was whether we could provide a design where the student could make a choice for a learning pathway adapted to their own needs, based on both their interests and readiness levels. One key pedagogical intervention tested in the study was a tiered homework assignment. In our design, these homework assignments consisted of problem menus organized in three categories, corresponding to the levels of apply, analyze and creating (termed “design”) in Bloom’s taxonomy. Each problem was assigned a number of points, and students were asked to choose problems in order to earn a certain number of points. A student could not complete the number of points required for passing by solving problems in the apply or analyze sections alone. The challenge was to encourage and support students’ extended reach towards the “creating” or design level. Four such assignments were presented over the course of a semester; each such homework was preceded by a conventional assignment that was graded for effort only, aiming at providing formative feedback to the student prior to the tiered assignment. Additionally, each tiered assignment was accompanied by a brief, open-ended questionnaire aiming at understanding how students chose problems to solve in this context. This allowed the instructor to assess students’ understanding and conceptions of the material as well as their misconceptions. Data for this study were collected during the Spring 2015 semester. 44 students provided written informed consent to have their course data analyzed for this study with 40 students completing the class; 4 dropped the class for reasons unrelated to the study. Student responses from the questionnaires accompanying the homeworks indicated that there are four major factors that drive such a choice: (1) Confidence that they can answer the question correctly was the strongest, with answer rates ranging from 80% to 86% over the four assignments. (2) A choice of questions based on how they would help them learn, or prepare for an exam, was the second cited motivation factor, which was mentioned in around 25% of responses in all assignments but the first one. (3) Time constrains, usually coupled with a rejection of the more complicated design questions, ranged from 6% to 17%, with concerns increasing as the semester progressed. (4) Looking for a challenge, which we initially assumed would motivate students to choose design problems, started at 16% and dropped to 10% towards the end of the semester. Interestingly, despite our expectation that this group would consist of the highest performing students, this was not the case: while some of the highest performing students were consistently in that group, that group also included students who exhibited overall lower performance levels in the course. Similarly, a lot of the higher performing students avoided the more demanding design problems. Concerns over a potential adverse effect on the final course grade and time demands seemed to be driving these choices. In this study, we present a detailed discussion on how these factors vary over time, and how they correlate with student choices and performance in the assignments. Overall, the intervention was well-received, even though thought of as time-consuming by the students, as indicated by a separate homework assessment questionnaire. Even though this is not a case-control study, both students’ success levels and ratings of the course improved compared to the previous year and is worthy of further study. Further data acquisition and analysis is currently underway which includes designing and enacting a video library for further supports for students’ learning.

Ikonomidou, V., & Samaras, A. P., & Kotari, V. (2016, June), Examining Choice in Self-directed Tiered Homework Assignments in College-Level Engineering Courses Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26791

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