Krauss, R. W., & Baine, N. A. (2024, June), Data Analysis for First-Year Experience Redesign  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47107

\bibitem{asee_peer_47107}
  Krauss, R. W., \& Baine, N. A. (2024, June), \emph{Data Analysis for First-Year Experience Redesign}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://peer.asee.org/47107

Ryan W. Krauss and Nicholas A. Baine.  "Data Analysis for First-Year Experience Redesign".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47107 Internet.  30 Jul, 2024

\bibitem{asee_peer_47107}
  Ryan W. Krauss and Nicholas A. Baine.
  "Data Analysis for First-Year Experience Redesign".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47107 Internet.  30 Jul, 2024

@INPROCEEDINGS{asee_peer_47107
author = "Ryan W. Krauss and Nicholas A. Baine"
title = "Data Analysis for First-Year Experience Redesign"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47107}
}

TY  - CPAPER
AB  - Grand Valley State University recently completed a redesign of its first-year engineering experience, which launched in the Fall of 2020. Prior to 2020, the first-year experience consisted of two three-credit courses where each course blended together several topics from different engineering disciplines. The first course focused on building skills such as engineering graphical communication and applied programming. The second course focused on the engineering design process, built upon the skills from the first course, and included a variety of other introductory topics in engineering. This second course also served as a cornerstone design course and concluded with a student robotics competition. 

Unfortunately, this course sequence acted as a barrier to students, resulting in a low success rate and poor transferability. The main contributing factor was found to be the blending together of topics from different disciplines. Students encountering difficulties in any one of the engineering skills included in a course were at a high risk of failing and were often required to retake the course to continue in engineering regardless of performance in the other content within the course. The blend of dissimilar topics also was found to be the cause of poor transferability, as courses at other institutions were unlikely to contain the same mix of topics.

To solve this problem, the new first-year experience is composed of five mini-courses that are one- to two-credit hours each.  The redesign had three primary goals:  increasing student success, reducing the number of credit hours that needed to be repeated, and increasing the transferability of courses. Based on several years of institutional data, this paper addresses the question of whether or not the redesign met these goals. While data collection on the effectiveness of the new design has been hindered by the pandemic, this paper presents a first attempt at data-based analysis on the effectiveness of the redesign.  

AU  - Ryan W. Krauss
AU  - Nicholas A. Baine
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Data Analysis for First-Year Experience Redesign
UR  - https://peer.asee.org/47107
ER  -