Displaying all 2 results
- 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
- Computers in Education 7 - Modulus 2
- Collection
- 2021 ASEE Virtual Annual Conference Content Access
- Authors
- Rahman Adekunle; John Kofi Eshirow Jr., University of Virginia; Jacob Lam Herring, University of Virginia; Sin Lin, University of Virginia; Rider W. Foley, University of Virginia
- Tagged Topics
- Diversity
- Tagged Divisions
- Computers in Education
diversity At first, the assessment showed a disproportionate number of projects were located inurban areas and connected to specific school districts and city administrations, see Figure 2.There were only seven projects that focused on rural school districts, while 21 projects wereconnected to urban school districts. The projects grouped as both urban and rural were all state-wide initiatives where the project outcomes affected both large cities and rural regions. Taken atface value, this depicts a higher concentration of projects located in urban areas where internetconnectivity is generally more accessible. The financial geography shows that these projectstarget major US cities with concentrations in the Northeast and Mid-Atlantic, as well