Conference Session

Technical Session 4: Modulus Topics 1

Collection

2019 ASEE Annual Conference & Exposition

Authors

Ashwin Satyanarayana, New York City College of Technology; Karen Goodlad, New York City College of Technology, CUNY; Jennifer Sears, New York City College of Technology, CUNY; Philip Kreniske, Columbia University, The HIV Center; Mery F. Diaz, New York City College of Technology; Sandra Cheng, New York City College of Technology

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

a PhD in Computer Science from SUNY, with particular emphasis on Data Mining and Big data analytics. He is an author or co-author of over 25 peer reviewed journal and conference publications and co-authored a textbook – ”Essential As- pects 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 Chair Elect of the ASEE (American Society of Engineering Education) Mid-Atlantic Conference. He also serves as an NSF (National Science Foundation) panelist.Prof. Karen Goodlad, New York City College of Technology, CUNY Karen Goodlad is an Assistant Professor specializing in

Conference Session

Technical Session 9:Topics related to STEM

Collection

2019 ASEE Annual Conference & Exposition

Authors

Ronald F. DeMara P.E., University of Central Florida; Tian Tian, University of Central Florida; Shadi Sheikhfaal, University of Central Florida; Wendy Howard, University of Central Florida

Tagged Divisions

Computers in Education

instructionaltechnologies with alternative modes of delivery embracing active learning [8] and otherpathways identified herein.At the other extreme, Massive Open Online Courses (MOOCs) exclusively utilize onlinedelivery methods with a high reliance on self-paced learning via an asynchronous deliverymechanism and often at the expense of reduced engagement [9]. Strengths of MOOCs includevery high instructor productivity, which can reach thousands of students and some peer-assessment is feasible albeit via asynchronous discussion mechanisms [10]. Challenges ofMOOCs for teaching STEM include reduced retention [11], few opportunities for activeengagement, and challenges with assessment arising from the lack of authentication whereinonline-only grading may be difficult

Conference Session

Technical Session 1: Issues Impacting Students Learning How to Program

Collection

2019 ASEE Annual Conference & Exposition

Authors

A.T.M. Golam Bari, University of South Florida; Alessio Gaspar, University of South Florida; R. Paul Wiegand, University of Central Florida, School of Modeling, Simulation, & Training; Dmytro Vitel; Kok Cheng Tan; Stephen John Kozakoff, University of South Florida

Tagged Divisions

Computers in Education

. URL http://dl.acm.org/citation.cfm?id=1151869.1151890. [2] Barbara J Ericson, Lauren E Margulieux, and Jochen Rick. Solving parsons problems versus fixing and writing code. In Proceedings of the 17th Koli Calling Conference on Computing Education Research, pages 20–29. ACM, 2017. [3] Juha Helminen, Petri Ihantola, Ville Karavirta, and Lauri Malmi. How do students solve parsons programming problems?: An analysis of interaction traces. In Proceedings of the Ninth Annual International Conference on International Computing Education Research, ICER ’12, pages 119–126, New York, NY, USA, 2012. ACM. ISBN 978-1-4503-1604-0. doi: 10.1145/2361276.2361300. URL http://doi.acm.org/10.1145/2361276.2361300. [4] Ville Karavirta, Juha

Conference Session

Technical Session 13: Digital Learning

Collection

2019 ASEE Annual Conference & Exposition

Authors

Hieu-Trung Le, George Mason University; Aditya Johri, George Mason University; Aqdas Malik, George Mason University

Tagged Divisions

Computers in Education

pieces ofinformation. The benefits of using Twitter data for this type of analysis is that the whole dataset can be used, rather than havingto select a small sample from the dataset.In this study, descriptive analysis will look at information and metrics in three main areas of the dataset: tweets, users, and URLs.The outcome of this analysis will provide a picture into the data and provide metrics about the tweets. Analyzing the tweets, thestudy will look at word counts, hashtags that are used, how tweets are produced over time, and the overall statistics of the tweetsthemselves. For the users, the study looks at who write the tweets, and who response to it. In addition, it identify the key playersand characteristics that makes them important in