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Common Assessment of Two Related Courses to Reduce Grading Bias and Improve Readiness of the Students for Corporate Environments

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2017 Mid-Atlantic Section Fall Conference


Penn State University - Berks Campus - Reading, Pennsylvania

Publication Date

October 6, 2017

Start Date

October 6, 2017

End Date

October 7, 2017

Conference Session

Mid Atlantic Papers

Tagged Topic

Mid-Atlantic Section Fall Conference

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Paper Authors


Ashwin Satyanarayana New York City College of Technology

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Dr. Ashwin Satyanarayana is currently an Assistant Professor with the Department of Computer Systems Technology, New York City College of Technology (CUNY). Prior to this, Dr. Satyanarayana was a Research Scientist at Microsoft in Seattle from 2006 to 2012, where he worked on several Big Data problems including Query Reformulation on Microsoft’s search engine Bing. He holds a PhD in Computer Science (Data Mining) from SUNY, with particular emphasis on Data Mining and Big data analytics. He is an author or co-author of over 20 peer reviewed journal and conference publications and co-authored a textbook – “Essential Aspects of Physical Design and Implementation of Relational Databases.” He has four patents in the area of Search Engine research. He is also a recipient of the Math Olympiad Award, and is currently serving as Vice Chair of the ASEE (American Society of Engineering Education) Mid-Atlantic Conference. He also serves as an NSF (National Science Foundation) panelist.

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Janusz Kusyk PhD New York City College of Technology, CUNY.

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Dr. Janusz Kusyk received BS and MA degrees from Brooklyn College of the City University of New York. He was awarded PhD degree in Computer Science at the Graduate Center, CUNY in 2012. Currently he is an assistant professor in Department of Computer Systems Technology of New York City College of Technology (City Tech), CUNY. Prior to joining City Tech, he was a Patent Examiner in US Patent and Trademark Office, where he analyzed and evaluated patent applications in areas of information security, cryptography, and communication networks. His interests include applications of game theory and biologically inspired algorithms to communication networks and cyber security, especially with applications to MANETs, sensor networks, and distributed robotics tasks.

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Hong Li New York City College of Technology

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Hong Li is an Associate Professor and Chairperson of the Computer Systems Technology Department at New York City College of Technology, CUNY. She received her Ph.D. in Mathematics. Her focus are working with faculty constantly to keep curriculum updated to respond to the growth of computer technology; researching in project-based learning with digital generation; and promoting the retention of female students. Her research interests include artificial neural networks and applications in system identification and forecasting. She has worked on projects that have applied neural networks in highway rainfall drainage problems, the estimation of crude oil saturation and non-invasive glucose sensing problems.

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This paper presents an approach to assess students attending two related computing courses. To demonstrate our approach, we evaluated students taking either Fundamental Networking or Fundamental Database courses. Towards the end of the semester, students taking both courses were asked to individually finish a common term project resembling a scenario in the corporate environment. One of the objectives of this assignment was to let students recognize benefits of mastering different but related areas of study. Another goal was to teach them that various computer technology courses are interconnected and that a computer specialist can use skills learned in one area to better understand concepts of the other one. The students were evaluated based on their abilities to combine knowledge from the subject they studied with researched information about a related area in computer technologies. Each student’s project was assessed by two professors and the results were analyzed to better prepare future interdisciplinary assignments while eliminating potential grading bias. This type of assessment methodology could benefit students, by introducing them to advantages coming from broader knowledge, and educators, by letting them develop cross-disciplinary assignments that are resilient to instructor’s grading bias while stimulating students interests.

Satyanarayana, A., & Kusyk, J., & Li, H. (2017, October), Common Assessment of Two Related Courses to Reduce Grading Bias and Improve Readiness of the Students for Corporate Environments Paper presented at 2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania. 10.18260/1-2--29370

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