Selecting Effective Examples to Train Students for Peer Review of Open‐Ended Problem Solutions

Background Students conducting peer review on authentic artifacts require training. In the training studied here, individual students reviewed (score and provide feedback on) a randomly selected prototypical solution to a problem. Afterwards, they are shown a side-by-side comparison of their review and an expert’s review, along with prompts to reflect on the differences and similarities. Individuals were then assigned a peer team’s solution to review. Purpose This paper explores how the characteristics of five different prototypical solutions used in training (and their associated expert evaluations) impacted students’ abilities to score peer teams’ solutions. Design/Method An expert rater scored the prototypical solutions and 147 student teams’ solutions that were peer reviewed using an eight item rubric. Differences between the scores assigned by the expert and a student to a prototypical solution and an actual team solution were used to compute a measure of the student’s improvement as a peer reviewer from training to actual peer review. ANOVA testing with Tukey’s post-hoc analysis was done to identify statistical differences in improvement based on the prototypical solutions students saw during the training phase. Results Statistically significant differences were found in the amount of error a student made during peer review between high and low quality prototypical solutions seen by students during training. Specifically, a lower quality training solution (and associated expert evaluation) resulted in more accurate scoring during peer review. Conclusions While students typically ask to see exemplars of “good solutions”, this research suggests that there is likely greater value, for the purpose of preparing students to score peers’ solutions, in students seeing a low-quality solution and its corresponding expert review

First Year Engineering Students’ Identification of Models in Engineering

Background To succeed in engineering careers, students must be able to create and apply models to certain problems. The different types of models include physical, mathematical, computational, graphical, and financial, which are used both in academics, research, and industry. However, many students struggle to define, create, and apply relevant models in their engineering courses. Purpose (Research Questions) The research questions investigated in this study are: (1) What types of models do engineering students identify before and after completing a first-year engineering course? (2) How do students’ responses compare across different courses (a graphical communications course - EGR 120 and a programming course - EGR 115), and sections? Design/Methods The data used for this study were collected in two introductory first-year engineering courses offered during Fall 2019, EGR 115 and EGR 120. Students’ responses to a survey about modeling were qualitatively analyzed. The survey was given at the beginning and the end of the courses. The data analyzed consisted of 560 pre and post surveys for EGR 115 and 384 pre and post surveys for EGR 120. Results Once the analysis is complete, we are hoping to find that the students can better define and apply models in their engineering courses after they have completed the EGR 115 and/or EGR 120 courses

Infographic: Career Readiness Challenge (CRC) Summary

This infographic summarizes the final report from a study titled, Gamified Online Platform to Support Student-Athlete Career Readiness, funded by the NCAA Innovations in Research and Practice Grant Program. The purpose of the study was to create a gamified online career readiness program to enhance attitudes towards career planning and support the translation of professional competencies. Over a span of seven weeks, 116 student-athletes from Embry-Riddle Aeronautical University (ERAU) - Daytona Beach participated in the study. The student-athletes competed in three different sports. Among ERAU student-athletes, the highest contribution of professional competencies involved: a) leadership, b) communication and c) collaboration. The final report and slides are publicly available via http://www.ncaa.org/about/resources/research/career-readiness-challeng