Characterizing Computational Adaptive Expertise

2020 · 20 citations

• Computer Science
• Computer Science

Our research is exploring the role that computational and analytical abilities play in innovation, in the context of engineering design education. We are applying the learning framework of adaptive expertise to focus our work and guide the research. The model of adaptive expertise has been presented as a way of thinking about how to prepare learners to flexibly respond to new learning situations, which is precisely what students are expected to do in the context of developing design solutions. We focus on "computational adaptive expertise," which we abbreviate CADEX, since a major portion of an engineering curriculum focuses on developing analytical and computational knowledge. Yet, students often struggle with applying or transferring computational knowledge in the context of design. The current paper presents an overview of adaptive expertise and relates this concept specifically to engineering design education. In addition, the paper presents an overview of the research plan we are presently using to study CADEX in the context of a senior level biomedical engineering design course.

Similarities and Differences in Undergraduate Biomedical Engineering Curricula in the United States

2020 · 13 citations

• Computer Science
• Computer Science
• Mathematics education

Abstract Similarities and Differences in Undergraduate Biomedical Engineering Curricula in the United StatesEmployers, textbook publishers, and existing and emerging educational programs in biomedicalengineering and bioengineering continue to be interested in the degree to which theundergraduate curricula of degree granting programs are similar for undergraduates in thesefields, and what the similarities are.Several years ago, the VaNTH Engineering Research Center in Bioengineering EducationalTechnologies compiled information about required courses at 40 of the 42 ABET-accreditedprograms, as well as 31 programs that were not accredited at that time. While these data from2007 have been presented in several forums, there is as yet no complete publication on this topic.In the interest of providing data that can be used by different constituencies, as well as a snapshotof the curriculum at a particular point to which changes can be compared, the data from thatproject are presented here in full. The results from the 2007 sample concerned courses beyondfreshman math, physics and chemistry, which tend to be common across engineering majors, tofocus on the courses required specifically for the biomedical engineering degree. We found thatmechanics, physiology and design were the subjects required most frequently, at 90% or more ofthe accredited programs. Other subjects required by 75% or more of the accredited programswere other areas of biology, circuit analysis, computing, statistics, materials, andinstrumentation. Several more topics were required by more than half of the programs. Therewas more variation in the amount of curricular time devoted to different subjects than in thetopics that were required. In comparing accredited and non-accredited programs, mechanics,thermodynamics, and materials were required more frequently at accredited programs, whilecomputing and organic chemistry were required by a larger percentage of the non-accreditedprograms. Normalizing all programs to a credit-hour basis showed that beyond required courses,the median number of credit hours left for specialization or elective courses was 12, and this didnot differ between accredited and non-accredited programs. Overall these results showed a highdegree of similarity in the required courses across all biomedical engineering programs. Whileresources are not available to obtain a complete 2013 data set for comparison to the 2007 data,changes in curriculum will be presented for a selected sample of institutions in order to assesstrends.

Biomedical Engineering Key Content Survey – The 1 St Step In A Delphi Study To Determine The Core Undergraduate Bme Curriculum

2020 · 16 citations

• Computer Science
• Computer Science
• Medical education

Using Technology To Promote Active Learning In Biomedical Engineering

2020 · 1 citations

• Computer Science
• Computer Science
• Artificial Intelligence

This paper evaluates previous uses of Personal Response Systems (PRS) and the pedagogical rationale associated to the different uses. We illustrate the use of PRS systems in two different courses: Systems Physiology and Thermodynamics. We describe the motivation to use PRS as well as the pedagogical methods associated with PRS use in the courses. The main goal of the study is to evaluate the relationships between students' use of PRS and learning outcomes. We used two measures to evaluate students' use of the PRS system. A Response index was calculated as the percentage of questions answered. A second index, Correct Response index was calculated by dividing the number of correct answers by the number of questions attempted. Learning outcomes were assessed using exam grades and final course grade. Students' perceptions relative to PRS use in the course were measured using a questionnaire. We found a positive and significant relationship between PRS Response index and course performance for both courses. We conclude by comparing and evaluating the differences found in the results from both courses.