What Do We Know About Our Entering Students And How Does It Impact Upon Performance?
Author(s) -
Staci Provezis,
Mary BesterfieldSacre,
Larry J. Shuman,
Siripen Larpkiattaworn,
Obinna Muogboh,
Dan Budny,
Harvey Wolfe
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--11829
Subject(s) - curriculum , mathematics education , session (web analytics) , rank (graph theory) , variety (cybernetics) , academic year , medical education , advanced placement , psychology , computer science , mathematics , pedagogy , medicine , artificial intelligence , world wide web , combinatorics
Besides their average SAT scores and possibly their high school rank, engineering faculty and administrators typically know little about their entering freshmen. This limited knowledge hinders both placement in and content of first year courses. For example, how many entering students took calculus in high school? For how many semesters? What grades did they earn? How does their math background correlate with their college boards or college placement testing? How much have they retained? What was their performance in first semester math courses? How many students have had three or more years of foreign languages? Do they continue that in college? Does that motivate them to study abroad? Where there is a common freshman year, how many students select a different major program than the one they initially were interested in at the time of application? By having the answers to these and similar questions, faculty and administration may be able to change curriculum, advising and support services to better support student learning and success. At the University of Pittsburgh School of Engineering we have been collecting such information about our incoming students’ attitudes, math knowledge, academic history, first year academic performance, retention, and general background through a variety of survey instruments, inventories and placement examinations. These data are maintained in a large database, which allows us to easily extract specific information for assessment or monitoring purposes. This enables us to answer questions about our students and use the information to make more informed curriculum and policy decisions. We present a number of examples here including information on entering student attributes, language and math background, departmental choice and performance.
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