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ENE 590 Presentations

Event Date: November 29, 2012
Speaker: Emily Dringenber, Joi Mondisa, Celia Pan
Speaker Affiliation: ENE doctoral students
Time: 3:30-4:20pm

A Review of Literature on Transfer of Learning Theories

Presented by Emily Dringenberg

In summary, this research was motivated by the idea that in order to truly increase the accessibility of engineering to students from low SES backgrounds, their math skills must be on par with their higher SES peers. This includes a preliminary literature review for ways to define and access the transfer of math knowledge.

Assessing Mentoring: Reflections on the Merit Scholars Workshop Program: How a Mentoring Program Affects Underrepresented Undergraduates in STEM Majors

Presented by Joi-Lynn Mondisa

This research study assesses mentoring, teamwork, and peer mentoring and evaluates how these factors are employed in the Merit Scholars Workshop Program, a math and science mentoring program for undergraduates at the University of Illinois at Urbana-Champaign. Assessing how mentoring, teamwork, and peer mentoring play roles in the Merit Scholars Workshop Program and how do program participants reflect on their experiences and build community through their experiences provides insight into informal and formal mentoring implications and how mentoring programs and relationships can be used to increase retention of undergraduates from underrepresented populations in STEM fields.

Online Course Advising: Differences in Student Response by Gender and Ethnicity

Presented by Celia Pan

Despite considerable efforts to recruit and retain women and minorities in engineering, many studies indicate that female and minority students still constitute a lower share of engineering undergraduates. One potential solution to improve student retention in engineering is academic advising. Previous research indicates that high quality academic advising is a key factor in engineering students’ academic success and retention. So far, many computer tools and systems have been invented to facilitate the advising process.  Among these tools is Course Signals, which uses real-time data pertaining to student performance and other indicators to provide up-to-date feedback to students.  Additionally, Course Signals can be used to predict the student’s probability of success in the course based on performance on assignments and exams, as well as to deliver advising interventions to promote more effective study strategies and other changes that will lead to better outcomes. Therefore in this study we explore whether Course Signals influences engineering students’ academic performance.

Learning Traits, Strategies and Experiences Critical to Success as Practicing Engineers

Presented by Daniel Ferguson, James Cawthorne, and Cory Schimpf

This research examines the question: “How do successful practicing engineers continue to learn after their „formal‟ schooling comes to a close?”