Dacunto, P. (2016, June), Academic "Predestination": Does It Exist?  Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26488

\bibitem{asee_peer_26488}
  Dacunto, P. (2016, June), \emph{Academic "Predestination": Does It Exist?}
  Paper presented at 2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana.
  10.18260/p.26488

Phil Dacunto P.E..  "Academic "Predestination": Does It Exist?".  2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, 2016, June.  ASEE Conferences, 2016.  https://peer.asee.org/26488 Internet.  12 Dec, 2024

\bibitem{asee_peer_26488}
  Phil Dacunto P.E..
  "Academic "Predestination": Does It Exist?".
  \emph{2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana, 2016, June}.
  ASEE Conferences, 2016.
  https://peer.asee.org/26488 Internet.  12 Dec, 2024

@INPROCEEDINGS{asee_peer_26488
author = "Phil Dacunto P.E."
title = "Academic "Predestination": Does It Exist?"
booktitle = "2016 ASEE Annual Conference \& Exposition"
year = "2016"
month = "June"
address = "New Orleans, Louisiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/26488}
number = {10.18260/p.26488}
}

TY  - CPAPER
AB  - 	It often seems that instructors can predict who the best performers in a particular course will be by looking at their grades coming into the course.  Those with the best grades coming into the course, the “good” students, usually seem to end up on top.  However, does that relationship actually exist, or is it just a perception?  Likewise, can we predict student performance in an engineering program based on their grades in certain classes (or the core curriculum as a whole) prior to entering the program?  This paper seeks to answer those questions by analyzing grade data from several courses, and one engineering program.  The course grade and program performance data came from 91 environmental engineering majors at an undergraduate teaching institution in classes graduating over a six-year period.  Results of linear regression analysis of final course grades or program grade point averages (GPAs) against GPAs of the same students coming into the course or program are reported.  In addition, the relationship between particular courses or sets of courses (for example, math and science courses only) taken previously and overall GPA in the major is explored.  While all relationships were significant (p < 0.005), several were more useful in predicting future performance.  A particularly strong relationship was found between an environmental chemistry course and overall performance in the environmental engineering major (R2 = 0.77); the relationships between overall performance in the major with 1st term GPA or 1st term math and chemistry grades were useful as well (R2 = 0.48 and 0.52, respectively).  Finally, 40% of students with 1st term GPAs less than 2.0 did not complete the full ABET curriculum, whereas all with 1st-term GPAs greater than 2.0 did.  Understanding these relationships is important because by identifying students who may be at risk of performing poorly prior to or at the beginning of a particular course or engineering program, instructors and advisers can be ready to offer early assistance or objective evidence of how students with similar entering grades performed.

AU  - Phil Dacunto P.E.
CY  - New Orleans, Louisiana
DA  - 2016/06/26
PB  - ASEE Conferences
TI  - Academic "Predestination": Does It Exist?
UR  - https://peer.asee.org/26488
DO  - 10.18260/p.26488
ER  -