Quality Indicators for Engineering and Technology Education

In recent years the development and use of university rankings, comparisons, and/or league tables has become popular and several methodologies are now frequently used to provide a comparative ranking of universities. These rankings are often based on research and publication activity and also not uncommonly focus on indicators that can be measured rather than those that should be measured. Further, the indicators are generally examined for the university as a whole rather than for university divisions, departments or programs. Implicit also is that placement in the rankings is indicative of quality. This paper provides an overview of the methodologies used for the more popular rankings and summarizes their strengths and weaknesses. It examines the critiques of rankings and league tables to provide appropriate context. The paper then examines the issue of how a university (or a college or program) could be assessed in terms of the quality of its engineering and technology programs. It proposes a set of indicators that could be used to provide relative measures of quality, not so much for individual engineering or technology programs, but rather of the university

Special Libraries, August 1980

Volume 71, Issue 8https://scholarworks.sjsu.edu/sla_sl_1980/1006/thumbnail.jp

Volume 2015 - Issue 2 - Spring, 2015

https://scholar.rose-hulman.edu/rose_echoes/1091/thumbnail.jp

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Central Florida Future, April 22, 1998

Reception held to get to know the dean; he garage comes tumbling down; Earth Day Blowout to honor Mother Nature; Deal\u27s achievements deserving of an alumnus award.https://stars.library.ucf.edu/centralfloridafuture/2457/thumbnail.jp