Libre, N. (2022, August), Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41702

\bibitem{asee_peer_41702}
  Libre, N. (2022, August), \emph{Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41702

Nicolas Libre.  "Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41702 Internet.  21 May, 2024

\bibitem{asee_peer_41702}
  Nicolas Libre.
  "Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41702 Internet.  21 May, 2024

@INPROCEEDINGS{asee_peer_41702
author = "Nicolas Libre"
title = "Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41702}
number = {10.18260/1-2--41702}
}

TY  - CPAPER
AB  - The ability to predict student performance and identify those who are “academically at-risk”
creates opportunities to improve educational outcomes and enhance retention. This research
aimed at developing and implementing a predictive model to generate an alert of potential
student failure in engineering mechanics courses. A prediction model is developed based on
regression analysis and is used to predict students' final course grades based on their activities
early in the semester. In general, a predictive model needs a refined and standardized data set in
order to achieve meaningful results. The data set used for the grade prediction includes students'
performance in the formative and summative assessments during the first four weeks of the
semester as well as their class participation and homework completion. The training data was
collected in two semesters where the course was offered in-person in large sections with more
than 100 students per section; then the model was tested and validated in small and large sections
in four consecutive semesters, two of which involved online teaching mode. The analysis of all
collected data shows that the accuracy of the model is 88.0%. In particular, the model
successfully predicted students who were in danger of failure. The accuracy of prediction was
acceptable in both in-person and online teaching modes. The ultimate goal of developing and
implementing the model is utilizing a reliable tool that is capable of predicting student
performance with an acceptable accuracy to identify “academically at-risk” students. The
intervention process is beyond the scope of this paper and will be discussed in a separate
publication. The results of this study will provide all parties involved in student success (e.g.
instructors, advisors, counselors, etc.) with a tool to identify at-risk students early in the semester
enabling proactive intervention approaches before it is too late, to increase student success and
retention.
AU  - Nicolas Libre
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics
UR  - https://peer.asee.org/41702
DO  - 10.18260/1-2--41702
ER  -