Best Practices Involving Teamwork in the Classroom: Results From

266 IEEE TRANSACTIONS ON EDUCATION, VOL. 50, NO. 3, AUGUST 2007

Best Practices Involving Teamwork in the Classroom:

Results From a Survey of 6435 Engineering

Student Respondents

Barbara A. Oakley, Senior Member, IEEE, Darrin M. Hanna, Member, IEEE, Zenon Kuzmyn, and Richard M. Felder

Abstract—A teamwork survey was conducted at Oakland Uni-

versity, Rochester, MI, in 533 engineering and computer science

courses over a two-year period. Of the 6435 student respondents,

4349 (68%) reported working in teams. Relative to the students

who only worked individually, the students who worked in teams

were signiﬁcantly more likely to agree that the course had achieved

its stated learning objectives (

). Regression analysis

showed that roughly one-quarter of the variance in belief about

whether the objectives were met could be explained by four fac-

tors: 1) student satisfaction with the team experience; 2) the pres-

ence of instructor guidance related to teamwork; 3) the presence

of slackers on teams; and 4) team size. Pearson product–moment

correlations revealed statistically signiﬁcant associations between

agreement that the course objectives had been fulﬁlled and the

use of student teams and between satisfaction with teams and the

occurrence of instructor guidance on teamwork skills. These and

other results suggest that assigning work to student teams can lead

to learning beneﬁts and student satisfaction, provided that the in-

structor pays attention to how the teams and the assignments are

set up.

Index Terms—Correlation, evaluation, instructor, satisfaction,

student, team, teamwork.

I. INTRODUCTION

ESEARCH studies on teamwork are often geared towards

making teams more effective or determining whether

teamwork is effective in helping students learn [1]–[4]. Only a

few relatively small-scale studies have attempted to determine

which aspects of teamwork appear to be most important or

useful [5]–[9]. This study is the ﬁrst in a series of studies

designed to identify optimal conditions for teamwork in an

academic setting. Data correlations obtained over a two-year

period have been used to form hypotheses and design experi-

ments for further study.

II. B

ACKGROUND

Oakland University is a public university in Rochester, MI.

In fall 2005, the university had 17 339 students, 1664 of whom

were enrolled in engineering and computer science programs.

Manuscript received August 29, 2006; revised May 1, 2007.

B. A. Oakley and D. M. Hanna are with the School of Engineering and

Computer Science, Oakland University, Rochester, MI 48309 USA (e-mail:

[email protected]).

Z. Kuzmyn is with Zenon Et Al Ltd., Ann Arbor, MI 48104 USA.

R. M. Felder is with North Carolina State University, Raleigh, NC 27695

USA.

Digital Object Identiﬁer 10.1109/TE.2007.901982

In the engineering student population, 66% were undergrad-

uate. Of these undergraduates, 83% were male and 17% female.

Whites formed 73% of the undergraduate population, African

Americans 8.3%, Native Americans 0.4%, Asians 6%, His-

panics 2%, international students 2%, with the remaining 7%

not listed. Whites formed 52% of the undergraduate population,

African Americans 2%, Native Americans 0.2%, Asians 10.6%,

Hispanics 1%, international students 21%, with the remaining

4% not listed. The average ACT score of incoming freshmen

was 21.5, and the average high school grade point average was

3.2. Oakland University is a largely commuter campus—only

10% of the overall student body lives in dormitories.

Oakland’s School of Engineering and Computer Science ad-

ministers a course-end survey asking the students to rate their

teacher’s effectiveness and how well they feel they have accom-

plished the course objectives, among other items. This survey

includes the items shown in Table I. Although ﬁlling out the

survey at the end of each course is voluntary, all students are

strongly encouraged to participate, and approximately 60% do

so. The survey has been online for the past ﬁve years.

Two years ago, seven questions related to teamwork (Table II)

were added to the survey. The two-year period of the study en-

compassed the 14-week fall and winter semesters and the seven-

week spring and summer semesters of both 2004 and 2005. Al-

together, 533 courses, ranging from freshman to graduate level,

were evaluated using the augmented questionnaire. In the end,

6435 responses to the teamwork questions were received. Be-

cause many students enroll in three to four courses during a

semester, some engineering students may have responded to the

survey several times in each term, once for each of their courses.

Self-enumerated surveys are widely accepted in social sci-

ence research, with applications that include population census

collection and quality of life surveys in medicine. Challenges in

using self-enumerated surveys include ensuring that the com-

pleted surveys are representative of the target population, ob-

taining surveys that contain no missing data, ensuring effective

response rates, and reducing the sample bias that can occur when

surveys are voluntary.

The nature of Oakland’s survey system mitigates many of

these common challenges. The survey was designed for a large

population, and the questions and choices were brief, with min-

imal ambiguity to avoid bias caused by question interpretation.

Moreover, because the surveys were computerized, all surveys

submitted contained valid categorical responses. Access to the

OAKLEY et al.: BEST PRACTICES INVOLVING TEAMWORK IN THE CLASSROOM 267

TABLE I

ENERAL

EVALUATION

QUESTIONS (

, )

In actuality, each instructor supplies between a half-dozen and a dozen objectives for his or her course, for

example: “Be able to explain the Law of Conservation of Energy in terms that a high school student could

understand,” or “Use Ohm’s Law to solve for voltage, current, or resistance in various parts of an electrical

circuit.” Each student rates the degree to which he or she felt he or she achieved each objective. The average

rating for all objectives forms the synthesized question 5 shown above.

TABLE II

EAMWORK

SURVEY QUESTIONS

survey was given only to students who have enrolled in an en-

gineering course. All of these students have access to a com-

puter and the Internet at school, and most have access at home.

Instructors are strongly encouraged to require their students to

complete the surveys, or to provide motivation in the form of

extra credit. Students are also directly encouraged to complete

the surveys, and are given the means to evaluate an instructor

even if the instructor does not provide access to the evaluation

system. All of these steps contributed signiﬁcantly to the 60%

student response rate, far larger than the 10%–30% response rate

that characterizes most voluntary surveys. A 60% return rate

is in fact usually considered by statisticians sufﬁcient to make

sample bias negligible.

III. S

TUDY DESIGN

The online evaluation system includes a broad range of vari-

ables that may have an impact on teamwork effectiveness. By

default, the students must identify the course level (freshman,

sophomore, etc.), the department offering the course (mechan-

ical, electrical, computer, etc.), and the term in which the course

was taught (fall, winter, spring, or summer). Beyond these de-

fault classiﬁcation variables, students were also able to report

factors such as the level of instructor guidance related to teams;

whether the instructor, as opposed to the students themselves,

had selected the members of the team; and the ability to sanc-

tion uncooperative team members. Using the online system,

the students also gave quantitative ratings of the instructors

and course outcomes from responses to the questions listed in

Table I, as well as quantitative ratings of satisfaction with teams

from question 2 of Table II. These classiﬁcations became the

variables used in this study. Section IV-A below provides an

overview of the raw data analysis from the questions in Tables I

and II.

Pearson’s product–moment correlation was used to identify

similarities among patterns of responses to different items. Sec-

tion IV-B describes the cross-correlation of all survey-derived

outcome measures.

268 IEEE TRANSACTIONS ON EDUCATION, VOL. 50, NO. 3, AUGUST 2007

TABLE III

RINCIPAL

SURVEY RESULTS

Of primary interest were four outcome variables: student

satisfaction with teams, students’ belief that they had fulﬁlled

the course objectives, student satisfaction with the course as

an overall learning experience, and student satisfaction with

their instructor. Analysis of variance (ANOVA) was used to

determine signiﬁcant differentiating factors with regards to

students’ satisfaction with teams (Table II, question 2). These

results are presented in Section IV-C.

The question regarding the students’ satisfaction with teams

had a Likert ﬁve-point scaled response ranging from zero to

four. For each response to the other questions listed in Tables I

and II (e.g., “None,”“Some,” and “A Lot” in question 3 about

the degree of instructor guidance), the mean response to the

question about satisfaction with teams was computed. For the

“Instructor Guidance” question, for example, the means were

2.73 (no guidance), 3.06 (some guidance), and 3.48 (a lot of

guidance).

As part of the ANOVA,

-Tests with pooled variance were

used to evaluate whether differences in the mean response to the

satisfaction with teams question were signiﬁcantly different be-

tween the response groups. To ensure that variance pooling was

justiﬁed, Levene’s Test for Equal Variances was performed. The

null hypothesis was: there is no difference in the mean satisfac-

tion between the different groups and the alternative hypothesis

was: the means were different.A

-value of 0.001 or less led

to rejection of the null hypothesis and indicated that the factor

under consideration affected the degree to which students were

satisﬁed with teams. In the case of the instructor guidance ques-

tion, for example, the

-value was less than 0.001, indicating

that the level of instructor guidance affected the degree of stu-

dent satisfaction with teams. Unlike Pearson’s product–moment

correlation, which was used to identify similarities among re-

sponses to different items, ANOVA pointed out factors such as

instructor guidance that reliably differentiated between lower

and higher student team satisfaction ratings.

An ANOVA was also used to study the relationship between

students’ satisfaction with teams and their belief that the course

objectives were met. This analysis is shown in Section IV-D.

For this part of the study, students who did not work in teams

were placed into a “No Teams” group and students who worked

in teams were subdivided into two groups: “Low Satisfaction”

if they responded with a rating of 2.5 or below to the question

about their satisfaction with teams, and “High Satisfaction” if

their response was above 2.5. Scheffe’s test was used to evaluate

whether the mean responses to the question about the course

objectives differed signiﬁcantly among these three groups.

Regression analyses were then performed to determine the

principal factors accounting for the response variances for

the questions about fulﬁllment of course objectives, rating

the course as an overall learning experience, and rating

the instructor. The results are presented and discussed in

Section IV-E.

All analyses were performed using software for Statistical

Processing for Social Science (SPSS) made by SPSS, Inc.,

Chicago, IL [10].

IV. R

ESULTS

A. Raw Data

The principal results of the survey are summarized in

Table III. There were no statistically signiﬁcant differences

between the results obtained from each of the four disciplines

(electrical, mechanical, computer, and systems), which sug-

gests that these ﬁndings may be broadly applicable to other

engineering disciplines. The term “slacker” in the last three

items refers to a student who was rated by the survey respondent

as making an insigniﬁcant contribution to the team effort—“not

pulling his/her weight.”

B. Pearson Correlations

Table IV provides the cross-correlation of all survey-de-

rived outcome measures. For teamwork-related questions,

the results were based only on responses from students who

reported working in teams, while for other questions all student

responses were included. Because of the large number of

respondents, even relatively low correlation coefﬁcients were

statistically signiﬁcant. The signiﬁcant correlations were further

classiﬁed as “strong” (

), “moderate” ( ),

or “weak” (

Several correlations suggest that students who had good team

experiences were apt to give the course higher ratings than stu-

dents whose team experiences were negative. Ratings of satis-

OAKLEY et al.: BEST PRACTICES INVOLVING TEAMWORK IN THE CLASSROOM 269

TABLE IV

EARSON

CORRELATIONS (

)(A

GGREGATED BY

CLASS

LEVEL)

faction with teams were weakly correlated with belief that the

course objectives had been met (

), ratings of the course

as a learning experience (

), and ratings of the instructor

(

). The strongest observed correlation with team sat-

isfaction was the moderate negative one with the presence of at

least one slacker on the team (

). Weak positive cor-

relations were found between satisfaction with teams and the

provision of instructor guidance on teamwork (

) and

the abilities to exclude slackers from authorship on assignments

(

) and to ﬁre slackers ( ). The latter two cor-

relations, while statistically signiﬁcant, are too small to justify

drawing any conclusion about those effects.

The weak correlations between team satisfaction and

instructor-formed teams (

) and between in-

structor-selected teams and the reported presence of slackers

(

) suggest that students on self-selected teams

are likely to have a better experience than students on in-

structor-formed teams. The issue of instructor-formed teams

versus self-selected teams is complex, however, and that con-

clusion would be unwarranted. The arguments for instructor

formation have to do primarily with the student product quality

and skill development that result from forming teams with

diversity in abilities; indeed, there was a very weak but positive

correlation (

) between instructor formation and the

students’ ratings of the course as a learning experience. The

lower frequency of reported slackers on self-selected teams

probably reﬂects a decreased tendency to give low ratings to

one’s friends.

None of the proponents of instructor formation suggest that

instructor formation will make students happier, and the fact that

it did not do so in this study is not particularly surprising. If there

is a lesson to be learned from the correlations, it is that instruc-

tors who form teams themselves might anticipate more inter-

personal conﬂicts within teams than self-selected teams might

experience, and should take care to provide the teams with guid-

ance on how to avoid conﬂict and deal with conﬂict construc-

tively when it arises [11], [12].

Although the seminal study “The Initial Knowledge State

of College Physics Students,” by Halloun and Hestenes [13]

showed that the knowledge attained in a course was independent

of the instructor, the data set used in the present study showed

very strong correlations between overall rating of the course as

a learning experience, overall rating of completion of course

objectives, and the rating of the teacher. Thus, regardless of

how well the students might actually have learned the material,

the students’ perceptions of how well they learned appeared to

be strongly related to the instructor’s perceived teaching skills.

This ﬁnding may have important implications for retention [14].

C. Student Satisfaction With Teams

Of the 4305 students who reported on their degree of overall

satisfaction with their team, 49% were very satisﬁed (

), 27% somewhat satisﬁed, 12% neutral, 7% somewhat dis-

satisﬁed, and 4% very dissatisﬁed (

). The average

level of satisfaction increased monotonically with the level of

the course, ranging from a low of 2.93 on a four-point scale

for freshman courses to a high of 3.29 for second-year graduate

courses (Table V).

ANOVA showed that the following characteristics of team-

work implementation had signiﬁcant effects on satisfaction with

teams:

• course level

, , ;

• level of instructor guidance on teamwork

, ;

• ability to exclude slackers from authorship of assignments

, , ;

• ability to ﬁre slackers

, ,

;

• number of slackers on the team

, ,

The means of the “satisfaction with teams” question of the

groups described above are provided in the tables given below.

The level of instructor guidance was strongly related to team

satisfaction, as shown in Table VI. Signiﬁcant differences in

270 IEEE TRANSACTIONS ON EDUCATION, VOL. 50, NO. 3, AUGUST 2007

TABLE V

ATISFACTION

WITH TEAMS

(

)

BY COURSE

LEVEL (

)

TABLE VI

ATISFACTION

WITH TEAMS BY

AMOUNT OF

INSTRUCTOR

GUIDANCE

(

)

TABLE VII

ATISFACTION

WITH TEAMS BY

ABILITY TO

EXCLUDE SLACKERS

FROM

AUTHORSHIP (

)

TABLE VIII

ATISFACTION WITH TEAMS BY ABILITY TO FIRE SLACKERS ( )

TABLE IX

ATISFACTION WITH

TEAMS (ON A SCALE OF

0 TO 4)

BY NUMBER OF

“SLACKERS” ON TEAM

( )

student satisfaction were also observed by giving the students

the option of leaving slackers’ names off completed assign-

ments (Table VII) and ﬁring them (Table VIII). The presence

of slackers had a clear connection with satisfaction, with the

average satisfaction ranging from 3.4 among students with no

reported slackers on their teams to less than 2.0 for students re-

porting two or more slackers (Table IX).

D. Meeting Course Objectives

The relationship between students’ perceptions that the

course objectives had been met and their satisfaction with

TABLE X

TUDENTS’ S

ELF-ASSESSMENT OF MEETING

COURSE OBJECTIVES BY

LEVEL OF

SATISFACTION WITH

TEAMS

teams is shown in Table X. According to the results from

Sheffe’s test, the difference between those students who were

highly satisﬁed with their teams and those who were not in

a team was signiﬁcant (

). Students who had low

satisfaction with their teams had similar mean scores as those

without teams.

Table XI shows a regression analysis of the students’ percep-

tions of meeting the course objectives. The results suggest that

students were more likely to perceive a course as being effective

if they were satisﬁed with their team experience, they got guid-

ance on teamwork from their instructor, they had no slackers on

their team, and their team included at least three or four people

rather than consisting of only a pair.

E. Student Satisfaction With the Course and Instructor

Tables XII and XIII show regressions on the dependent vari-

ables “Overall rating as a teacher” and “Overall rating of the

course as a learning experience,” respectively. In each case,

three key factors emerged.

Again, students’ perception of the course and the instructor

increased with their satisfaction with the team experience,

the level of instructor guidance on teamwork, the absence of

slackers (course rating), and the provision of measures to deal

with slackers (instructor rating).

V. S

UMMARY AND CONCLUSION

There are compelling reasons for assigning students to work

in teams on homework and projects. Several well-known edu-

cational theories support the idea that students learn most effec-

tively through interactions with others, most notably a social in-

terdependence theory that derives from the work of Lewin and

others [15]–[17] and was extended into classroom practice by

David and Roger Johnson [1], [18], and a social constructivist

theory generally attributed to Vygotsky [19]. Smith et al. [18]

observed that hundreds of empirical research studies and several

meta-analyses of the research (e.g., [2]–[4]) have compared the

relative efﬁcacy of cooperative, competitive, and individualistic

learning, with the overwhelming body of evidence indicating

that cooperative learning leads to signiﬁcant gains in academic

success, quality of interactions with both classmates and faculty

members, and attitudes toward the college experience.

This is not to say that cooperative learning is without

problems. Initial resistance to team-based approaches from

individual students is quite common and can be discouraging

to faculty members who do not expect it and are not equipped

with strategies to defuse it [20]. Moreover, students are not

born knowing how to work effectively in teams, and if a ﬂawed

or poorly implemented team-based instructional model is used,

dysfunctional teams and conﬂicts among team members can

OAKLEY et al.: BEST PRACTICES INVOLVING TEAMWORK IN THE CLASSROOM 271

TABLE XI

EGRESSION

ANALYSIS OF

STUDENT

PERCEPTIONS THAT THE

COURSE

OBJECTIVES

HAD BEEN MET

TABLE XII

EGRESSION—PREDICTING/EXPLAINING “OVERALL RATING AS A TEACHER”

TABLE XIII

EGRESSION—PREDICTING/EXPLAINING “OVERALL

RATING:C

OURSE AS A

LEARNING

EXPERIENCE”

lead to an unsatisfactory experience for instructors and stu-

dents alike [11], [12]. The results of this study help shed light

on things an instructor can do to minimize the likelihood of

problematic team situations.

The data support the following inferences.

•

The use of teams for assignments in courses at Oakland

University is widespread. More than half of the engineering

classes at Oakland appear to have some mandatory team-

work element, and 68% of student respondents indicated

that they used teams in their classes. These percentages

reﬂect the widespread interest in teamwork by Oakland’s

engineering faculty after a small original core group of

instructors proved teamwork’s effectiveness and trained

others in the techniques (Table III).

• Working in teams was positively associated with students’

self-assessed quality of learning. Students’ perception of

the degree to which they fulﬁlled the course objectives was

positively correlated with whether teamwork was required

in the course (Table IV).

• The ability to omit the names of uncooperative team mem-

bers from assignments and to ﬁre them as a last resort had

the strongest association with student satisfaction of all

the factors directly under the instructor’s control. If these

options are offered, the procedures that the students must

follow to exercise them should be carefully spelled out at

the beginning of the course [11], [12] (Table IV).

• Guidance from the instructor on effective teamwork had

a signiﬁcant effect on promoting student satisfaction with

their team experience. An excellent sourcebook for such

guidance is Smith [18], and other suggestions are given by

Felder and Brent [11] and Oakley et al. [12] (Table IV).

• From the standpoint of student satisfaction, team assign-

ments worked very well. Nearly 90% of the students sur-

veyed were either satisﬁed or neutral about their work on

teams, and nearly half were very satisﬁed. Student satisfac-

tion with teams increased as students advanced in the cur-

riculum, which may relate to student maturation and/or the

loss of some less mature or less motivated students through

attrition (Table V).

• Guidance from the instructor on how to work effectively

in teams appeared to make a substantial difference in stu-

dent satisfaction (Tables IV and VI). This ﬁnding clearly

relates to the correlation between student satisfaction with

their teams and student rating of the effectiveness of the in-

structor. Effective instructors are, in fact, more likely than

ineffective ones to provide team-building guidance.

VI. R

ECOMMENDATIONS FOR FURTHER STUDY

Several ideas for continuing research are suggested by the

results of this study. Following are several questions that might

proﬁtably be explored.

• Relative to self-selected teams, instructor-formed teams

appear to be more likely to experience interpersonal con-

ﬂict and somewhat more likely to learn more from their

team experience. What conditions inﬂuence the nature and

extent of the tradeoff? Can suitable instructor guidance as-

sure that instructor formation of teams is always desirable?

What kind of guidance? Would self-selection be more or

less appropriate for students with experience in teams?

• What makes slackers behave as they do? What instructor

actions and teammate actions (including formal peer rat-

ings that affect individual grades for team assignments) are

effective at changing their behavior? Is being left off an as-

signment or being ﬁred likely to change the behavior in

subsequent team experiences?

• Does team size affect student satisfaction with teams?

What are the effects of team size on the incidence of

slackers and other sources of interpersonal conﬂict?

272 IEEE TRANSACTIONS ON EDUCATION, VOL. 50, NO. 3, AUGUST 2007

• How does the performance on individual tests of members

of smoothly functioning teams compare with the perfor-

mance of members of teams with slackers?

CKNOWLEDGMENT

The authors would like to thank Dr. M. Hwalek, President of

SPEC and Associates, a research evaluation and design com-

pany, for her review and valuable input.

EFERENCES

[1] D. W. Johnson, R. T. Johnson, and K. A. Smith, Active Learning: Co-

operation in the College Classroom, 2nd ed. Edina, MN: Interaction,

1998.

[2] C. Birmingham and M. McCord, “Group process research: Implica-

tions for using learning groups,” in Team-Based Learning: A Transfor-

mative Use of Small Groups, L. K. Michaelsen, A. B. Knight, and L.

D. Fink, Eds. Sterling, VA: Stylus, 2002, pp. 77–97.

[3] L. Springer, M. E. Stanne, and S. Donovan, Effects of Small-Group

Learning on Undergraduates in Science, Mathematics, Engineering,

and Technology: A Meta-Analysis. Madison, WI: National Institute

for Science Education, 1997.

[4] P. T. Terenzini, A. F. Cabrera, C. L. Colbeck, J. M. Parente, and S.

A. Bjorkland, “Collaborative learning vs. lecture discussion: Students’

reported learning gains,” J. Eng. Educ., vol. 90, pp. 123–130, 2001.

[5] S. B. Fiechtner and E. A. Davis, “Why some groups fail: A survey of

students’ experiences with learning groups,” Org. Behav. Teach. Rev.,

vol. 9, pp. 75–88, 1985.

[6] A. Obaya, “Getting cooperative learning,” Sci. Educ. Int., vol. 10, pp.

25–27, 1999.

[7] D. R. Bacon, K. A. Stewart, and W. S. Silver, “Lessons from the best

and worst student team experiences: How a teacher can make the dif-

ference,” J. Manage. Educ., vol. 23, pp. 467–488, 1999.

[8] B. Oakley, “It takes two to tango: How ’Good’ students enable prob-

lematic behavior in teams,” J. Student Center. Learn., vol. 1, pp. 19

–27, 2002.

[9] D. A. Riordan, D. L. Street , and B. M. Roof, Group Learning: Applica-

tions in Higher Education. Harrisonburg, VA: Institute for Research

in Higher Education, 1997.

[10] R. Ho, Handbook of Univariate and Multivariate Data Analysis and

Onterpretation with SPSS. Boca Raton, FL: Chapman & Hall, 2006.

[11] R. M. Felder and R. Brent, “Effective strategies for cooperative

learning,” J. Coop. Collab. College Teach., vol. 10, pp. 69–75, 2001.

[12] B. Oakley, R. M. Felder, R. Brent, and I. Elhajj, “Turning student

groups into effective teams,” J. Student Center. Learn., vol. 2, pp.

9–34, 2003.

[13] I. A. Halloun and D. Hestenes, “The initial knowledge state of college

physics students,” Amer. J. Phys., vol. 53, pp. 1043–1050, 1985.

[14] E. Seymour and N. M. Hewitt, Talking About Leaving: Why Under-

graduates Leave the Sciences. Boulder, CO: Westview, 1997.

[15] K. Lewin, Field Theory in Social Science. New York: Harper, 1951.

[16] K. Lewin, Resolving Social Conﬂicts. New York: Harper, 1948.

[17] D. Cartwright and A. Zander, Group Dynamics: Research and

Theory. Evanston, IL: Row, Peterson & Co., 1953.

[18] K. Smith, S. D. Sheppard, D. W. Johnson, and R. T. Johnson, “Peda-

gogies of engagement: Classroom-based practices,” J. Eng. Educ., vol.

94, pp. 87–101, 2005.

[19] L. S. Vygotsky, Mind in Society. Cambridge, MA: Harvard Univ.

Press, 1978.

[20] R. M. Felder and R. Brent, “Navigating the bumpy road to student-

centered instruction,” College Teach., vol. 44, pp. 443–47, 1996.

Barbara A. Oakley (M’98–SM’02) received the B.A. degree in Slavic lan-

guages and literature and the B.S. degree in electrical engineering from the Uni-

versity of Washington, Seattle, in 1973 and 1986, respectively, and the Ph.D. de-

gree in systems engineering from Oakland University, Rochester, MI, in 1998.

She is currently an Associate Professor of Engineering in the Department of

Electrical and Systems Engineering, Oakland University. Her research interests

include the effects of electromagnetic ﬁelds on cells. Her most recent book,

Evil Genes: Why Rome Fell, Hitler Rose, Enron Failed, and My Sister Stole My

Mother’s Boyfriend (Amherst, NY: Prometheus Books, 2007), is a psycholog-

ical study of people who have trouble working in teams.

Darrin M. Hanna (S’97–M’98) received the B.S. degree (with top honors) in

computer engineering and mathematics and the Ph.D. degree in systems engi-

neering from Oakland University, Rochester, MI, in 1999 and 2003, respectively.

He is currently an Assistant Professor in the Department of Computer Science

and Engineering, Oakland University. As a sophomore at Oakland University, he

started a company, Technology Integration Group Services, Inc., specializing in

technical infrastructure, intelligent application development, and wireless sys-

tems. The company has continued to grow internationally, opening additional

ofﬁces in London, U.K., in 2002. His research interests include bioMEMS, mi-

croprocessor-less architectures for implementing hardware directly from high-

level source code, and pattern recognition techniques for embedded systems.

Zenon Kuzmyn received the B.A. degree in psychology (with high distinction)

from Wayne State University, Detroit, MI, in 1979, and the M.S.W. degree in

clinical practice and Ph.D. degree in educational psychology from the University

of Michigan, Ann Arbor.

After working brieﬂy at Michigan’s Institute for Social Research, he entered

the private sector as a Consultant to the motor trade. He continues to work as a

private Researcher and Consultant to large manufacturing enterprises as well as

small agencies that specialize in consumer research and training.

Richard M. Felder received the B.Ch.E degree from the City College of New

York and the Ph.D. degree in chemical engineering from Princeton University,

Princeton, NJ, in 1962 and 1966, respectively.

He worked as a Postdoctoral Fellow at the Atomic Energy Research Establish-

ment, Harwell, U.K., and as a Chemical Engineer at Brookhaven National Lab-

oratory before joining the chemical engineering faculty at North Carolina State

University (NCSU), Raleigh, in 1969. He is currently the Hoechst Celanese Pro-

fessor Emeritus of Chemical Engineering at NCSU. His professional interests

include effective pedagogical methods and faculty development in engineering

and the sciences. A list of publications he has authored can be viewed online at

http://www.ncsu.edu/felder-public.