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Automated Formation of Peer-learning Cohorts Using Computer-based Assessment Data: A Double-blind Study within a Software Engineering Course

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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

Electrical and Computer Division Technical Session 5

Tagged Division

Electrical and Computer

Page Count

17

Permanent URL

https://peer.asee.org/29841

Download Count

95

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Paper Authors

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Ronald F. DeMara P.E. University of Central Florida Orcid 16x16 orcid.org/0000-0001-6864-7255

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Ronald F. DeMara is a Professor of Electrical and Computer Engineering at the University of Central Florida where he has been a faculty member since 1992. His educational research interests focus on classroom and laboratory instructional technology, and the digitization of STEM assessments. He has completed roughly 225 technical and educational publications, 43 funded projects as PI/Co-PI, and established two research laboratories. He serves as the founding Director of the Evaluation and Proficiency Center (EPC) at UCF and is the recipient of UCF’s university-level Scholarship of Teaching and Learning Award, Teaching Initiative Program Award, Research Initiative Award, Excellence in Undergraduate Teaching Award, Advisor of the Year, Distinguished Research Lecturer, Marchioli Collective Impact Award, and is an iSTEM Fellow. He received the Joseph M. Bidenbach Outstanding Engineering Educator Award from IEEE in 2008.

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Damla Turgut University of Central Florida

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Damla Turgut is an Associate Professor at the Department of Computer Science at University of Central Florida. She received her BS, MS, and PhD degrees from the Computer Science and Engineering Department of University of Texas at Arlington. Her research interests include wireless ad hoc, sensor, underwater and vehicular networks, as well as considerations of privacy in the Internet of Things. She is also interested in applying big data techniques for improving STEM education for women and minorities as well as the digitization of STEM assessments. She is PI and Co-PI for NSF-funded REU and RET programs respectively. She co-led iSTEM Fellows program at UCF during 2016-2017 AY. Her recent honors and awards include 2017 University Excellence in Professional Service Award, 2017 Teaching Incentive Program Award and being featured in the UCF Women Making History series in March 2015. She was co-recipient of the Best Paper Award at the IEEE ICC 2013. Dr. Turgut serves as a member of the editorial board and of the technical program committee of ACM and IEEE journals and international conferences. She is a member of IEEE, ACM, and the Upsilon Pi Epsilon honorary society.

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Edwin Nassiff University of Central Florida

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Edwin Nassiff is an Adjunct Professor in the Computer Science department at the University of Central Florida (UCF) where he has been an Adjunct since 2012. He has taught both graduate and undergraduate level courses including software engineering, cloud computing, security, and data base management. He has held senior level positions as a CIO, CTO, and Technical Director with the US Government and Lockheed Martin Corporation. He has mentored for the UCF I-Corps, an organization assisting scientific researchers transform their research ideas into commercial entities. His research interests include enterprise architecture, data base modeling, computer security, and software and systems engineering.

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Salih Safa Bacanli University of Central Florida

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Salih Safa Bacanli is PhD student at Department of Computer Science, University of Central Florida (UCF). He received his MS degree in Computer Science from UCF and BS degree in Computer Engineering from Bilkent University, Turkey. His research interests include computer engineering education, opportunistic networking routing, wireless sensor network routing and security. He is member of Upsilon Pi Epsilon honorary society,ASEE and Order or Engineer.

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Neda Hajiakhoond Bidoki University of Central Florida

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Neda Hajiakhoond Bidoki is a Ph.D student at the Department of Computer Science at University of Central Florida.
Her research interests includes wireless network networks, mobility models, machine learning, data analysis as well as considerations of privacy in the Internet of Things. She received her M.Sc. in Network Engineering from Amirkabir University of Technology and her B.Sc. in Information Technology from Sharif University of Technology.

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Jun Xu University of Central Florida

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Jun Xu is pursuing the PhD degree in Computer Science from the Department of Computer Science at the University of Central Florida. He received his MS degree in Electrical Engineering from Beijing University of Posts and Telecommunications, China. His research interests include mobility models, agent path planning, and machine learning techniques applied to both large-scale autonomous and learning analytics systems.

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Abstract

An approach is developed to integrate the complementary benefits of digitized assessments and peer learning. The research hypothesis is that each student’s assessment data at the fine-grained resolution of correct/incorrect question choice selections can be utilized to partition learners into effective peer learning cohorts. A low overhead approach is explored along with its associated tool, referred to as Automated Peer Learning Cohorts (Auto-PLC). The objective of Auto-PLC is to increase scalability to deliver peer-based learning. First, digitized formative assessments are delivered in a computer-based testing center. This enables automated grading, which frees-up the instructor’s and teaching assistants’ workloads to become reallocated to recitation sessions for higher-gain learning activities, such as peer-based remediation sessions. Second, within the recitations held following each formative quiz, students are afforded an opportunity to complete a remedial assignment. Here the auto-graded results of formative assessment submissions have undergone Auto-PLC’s statistical clustering routines using Excel macros and Python scripts to partition the class into four-person peer learning cohorts having mutually-complimentary knowledge gaps and skill efficacies. Within each peer learning cohort, students solve together an assigned remedial problem during the recitation session. Thus, students who have already acquired a particular skill become paired together with those students who are still acquiring that same skill, and vice versa. This also aids scalability to large enrollments within ECE and CS courses by maximizing opportunities for students to teach each other the material which they still need to learn.

The motivation, design, and outcomes for Auto-PLC are presented within the required undergraduate course Object-Oriented Software Development at a large state university. To evaluate effectiveness, a double-blind IRB-approved study has been conducted in (redacted course number) with 206 students. All enrolled participated identically, except for their assignment to either randomly-formed or intelligently-clustered remediation groups. At the end of the semester, all students completed an identical final exam to provide a basis by which to compare their relative achievements. The data collected expounds upon the details of Auto-PLC’s impact towards achievement on a topic-specific basis. Additionally, learners’ perceptions about participation in automatically-formed peer learning cohorts are discussed.

DeMara, R. F., & Turgut, D., & Nassiff, E., & Bacanli, S. S., & Hajiakhoond Bidoki, N., & Xu, J. (2018, June), Automated Formation of Peer-learning Cohorts Using Computer-based Assessment Data: A Double-blind Study within a Software Engineering Course Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29841

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