MOST ENGINEERING UNDERGRADS ATTEND DOCTORAL INSTITUTIONS

By Daodao Wang

This graphic shows undergraduate enrollment in engineering according to colleges’ Carnegie Classification¹. Doctoral institutions dominate the percentage chart by taking up to 80 percent of enrollment; Master’s institutions follow and Baccalaureate institutions are the absolute minority in this category. All special schools and schools without a classification are included in the Other category.

Source: Survey data for ASEE’s Profile of Engineering and Engineering Technology Colleges.

¹ Please refer to http://carnegieclassifications.iu.edu/ for more details about Carnegie Classification and how the classification is given.

Total Undergraduate Enrollment in Engineering by Carnegie Classification, 2017

Figure 1. Percentage by Carnegie Classification

Figure 2. Total Enrollment by Carnegie Classification

TRUMP PICK TO HEAD OSTP HAILED BY SCIENTISTS

In late July, after 19 months in office, President Trump finally named his pick to head the White Office of Science and Technology Policy. And that giant sigh of relief you may have heard coming from Washington was the science community’s pleasantly-surprised reaction. The nomination of Kelvin Droegemeier, a professor of meteorology at the University of Oklahoma for 33 years, was widely applauded by scientists. He is a much-respected expert on extreme weather and also has political experience, having spent five years on the National Science Board.

The OSTP’s remit is to provide the White House with sound science advice on policies affected by science. Topics range from economics and foreign affairs to health and medical research, the environment, and artificial intelligence, including autonomous vehicles. Its staff, which numbered 135 during the Obama administration, has shrunk to 55. And while Obama’s OSTP director, John Holdren, was also a personal advisor to Obama and met with him several times a week, there’s no guarantee that Droegemeier will have a similar relationship with Trump. Senate Democrats are expected to grill Droegemeier intensely, particularly on climate change. He has not publicly indicated his views on global warming, and Marcia McNutt, president of the National Academy of Sciences, said she’s not sure what his personal views are. But Rosina Bierbaum, an environmental policy expert at the University of Michigan who has worked with him, told Nature she is “certain he believes in mainstream climate science.”

ED. DEPT. PLANS TO REPEAL ‘GAINFUL EMPLOYMENT’ RULE

In 2011, the Obama administration’s Department of Education announced a “gainful employment” rule, aimed mainly at for-profit colleges and universities, that penalized schools whose graduates lacked reasonable job prospects. Colleges found in violation would lose their eligibility to offer students federally funded Pell grants and federal student loans. The rule also required schools to make it clear in promotional materials whether they were, or were not, meeting federal standards for job placements. A federal judge in 2012 rejected part of the rule, and the department was working on a revision when President Trump won election in 2016. A watering-down of the regulation was expected, since Education Secretary Betsy DeVos is known to favor the privatization of education and has vowed to slash regulations. But the department plans to repeal the gainful employment rule outright. In its place, the department will offer more program-level outcomes data through the College Scorecard, Inside Higher Ed reports. The Chronicle of Higher Education noted that news of the repeal came in the same week that the department also took steps to make it harder for student-loan borrowers who claim their colleges defrauded them to get federal relief from their debts. DeVos has been criticized for hiring a number of former executives and lobbyists from the for-profit sector to fill key posts in the department.

DATA CRUNCHERS’ DELIGHT

Use of online forms can enrich students’ inquiry and analysis and assist in peer assessments.

By Chris Rogers

I am co-teaching a course on experimentation methods. During one class, our junior mechanical engineers correlated a person’s height both to wingspan (fingertip to fingertip) and to birth month as a way to learn about means, standard deviations, linear fits, and even kurtosis. As one might expect, the correlation of the first two is quite good, whereas it is a little known fact that the month in which you were born does not determine your height. They predicted the skewness, found the R-squared value, and generally started the discussion around correlation and causation.

What impressed me was the speed with which we could get a personally relevant data set for the students to investigate. In the first few minutes of class, the students whipped out their phones and filled in a Google form. They then downloaded the resulting Google Sheet as a CSV file, pulled it into LabVIEW, and embarked on their analysis. In five minutes we were analyzing data. The larger the class, the better the data set. I was excited to see how quickly they started questioning the accuracy of self-reported data, looking for physical causes of correlations, and wondering about why the distribution of birth months was more bimodal than flat—was that a result of less than 50 students in the class or something else?

Online forms, such as the Google form, can be used in many ways in the engineering classroom. My colleagues use Qualtrics, Survey Monkey, Google, or other tools to get at student thinking and gauge class knowledge. How is the class progressing? What do they understand? What do they predict will happen? Is the class moving too fast? Real-time surveys like Mentimeter or Socrative allow students to vote or give an opinion on the spot for the entire class to see, allowing the teacher to get instant feedback on a question or idea. Forms that allow attachments can also promote the idea of data sharing. Instead of every team duplicating the same data set, the class as a whole develops a shared data set for analysis. One team might measure the heat loss in an aluminum fin, for instance, while other teams measure the loss in different materials. The forms act as a rudimentary data sharing scaffold, ensuring that everyone documents his or her experimental setup and standardizing the data storage format. It allows the students to think of a design of experiments (DOE) that includes the entire class rather than just a single team, providing substantially more data to analyze and compare. Again, a larger class (or multiple sections) only improves the DOE and the diversity of data sets.

Where I use forms the most, though, is in peer-to-peer assessments. Students can grade one another”s presentation, project, or journal paper, and as the instructor, I can define the rubric (or the rubric can be developed as a class). These grades can vary from a single rating (8 = good, 9 = excellent, 10 = I called my parents I was so impressed) to written assessments. Rather than grading 40 papers from students, the forms allow me to emulate the peer-review system in our journals. Students read and review three articles each, learning not only the review process but also how to critique both their own and others’ work. Students appreciate the ability to leave the typical confines of the traditional classroom (one expert who knows all) and more closely follow the assessment methods of the world outside college (where there is no one expert). When trying to assess students’ creativity, in particular, I find that the crowd-sourced assessment is always better than mine.

Forms help the instructor understand student thinking. They also catalyze critiques and allow students to share data. These are all vitally important parts of the engineering process. For these tools to be really meaningful, though, students need to be working on an authentic problem—one that does not have the right answer in the back of a book. Moreover, I have found solving authentic problems makes teaching more fun and the students always seem to appreciate it (based on the student reviews). And who knows? Maybe someday we will find that unexpected correlation, show it is causal, and then the class will get really exciting.

Chris Rogers is a professor of mechanical engineering at Tufts University.

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