Essential Questions

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For over five years, the Association of American Universities (AAU), representing the 62 leading research universities in the United States and Canada, has been engaged in

an initiative to improve the quality of undergraduate teaching and learning in science, technology, engineering, and mathematics (STEM) fields at its member institutions. The overall objective is to influence the culture of STEM departments at AAU universities so that faculty members are encouraged to use teaching practices proven to be effective in engaging students in STEM education and in helping students learn. (See https://www.aau.edu/education-service/undergraduate-education/undergraduate- stem-education- initiative.)

Products from this initiative that should be of help to every mathematics department seeking to improve instructional practice are now available online. These include a framework for improving undergraduate STEM education with examples of programs at AAU universities that address each of the elements of the framework.

Figure: Cover of the AAU Essential Questions & Data Sources Report.

This past summer, they released their report on Essential Questions & Data Sources for Continuous Improvement of Undergraduate Teaching and Learning. Data sources include institutional data and tools for its visualization, observation protocols, rubrics, frameworks, student learning assessments, and surveys. The essential questions are separated into questions for institutional leadership as well as at the college, departmental, and instructor levels.

Because I believe that departmental leadership is the critical juncture for effective improvement, I will focus the remainder of this column on the questions addressed to departmental leaders and comment on what we have learned from the MAA’s studies of calculus instruction. By departmental leadership, I mean not just chairs and associate chairs, but all of those who shape the department’s direction. Change does not happen without a chair who is committed to improving the teaching and learning within the department, but it cannot be maintained without the support of a core of senior faculty.

Do all of the courses in the department have articulated learning goals, and are these made clear to students? What process exists to ensure that individual course learning goals connect to learning goals for the program, major, and department?

One of the clearest findings from the MAA calculus studies is that coordination of multiple section classes is essential. A prerequisite for effective coordination is a shared sense of what each course is seeking to accomplish.

What are the demographics of students in the department? What are the progression/retention/completion rates for students in the department or major broken out by relevant demographic categories? How do these compare with other departments and what steps are being taken to improve these rates?

Most departments I have visited have a sense that they are not doing as much as they could or should for students from traditionally underrepresented groups. This is not just a question of race, ethnicity, or gender, but also for students who are first generation, of lower socio-economic status, or from under-resourced schools whether they be inner city or rural. A department cannot know what is working for which populations if it is not tracking success rates by student demographics.

What actions has the department chair taken to encourage instructors to take advantage of both on-campus and off-campus (e.g., through relevant disciplinary societies) resources and professional development related to pedagogy? How many instructors have taken advantage of these resources and what notable improvements have occurred as the result?

The CBMS 2015 survey and other sources have documented that improvements in instructional pedagogy, support services, and course options almost always result from efforts initiated by individual faculty members. This question probes what the department is doing to nurture these faculty.

What resources are available to instructors in the department for encouraging all students to succeed, and what steps have been taken to ensure all instructors take advantage of these resources?

We know that faculty expectations of student ability play a huge role in how well students do, and faculty attitudes toward support services shape how students think about using these resources. The department as a whole must work to ensure the effectiveness of these services and then actively support their use, not as remediation but as a source of support and enrichment.

To what extent do departmental instructors have access to learning spaces that support evidence-based pedagogy? What training in the use of those facilities is available to instructors in the department?

The physical layout of classrooms and access to appropriate technology is critical for implementing effective pedagogies. This means tables where students can work together; sufficient space for instructors to walk around, answer questions, and observe how students are progressing; and sufficient board space for student groups to share their work. It does not have to be high tech classroom, but computer projection that is easily visible by all students is essential.

What is the department chair’s and distinguished faculty members’ support of evidence- based pedagogy? How well-known is this support to instructors and students?

This returns to the issue of nurturing those faculty who are positioned to initiate effective improvements. They need to know that if they are going to sink time and energy into improving teaching and learning within the department, then they will have the support not just of the chair whose term is limited but also of a core group of senior faculty who can ensure that support continues.

What are the biggest barriers to evidence-based pedagogy for instructors in the department and how is the chair working to address them? How often does the chair discuss these issues with the dean or other institutional leaders?

This addresses the chair’s critical role as the bridge between enthusiastic faculty, eager with ideas, and the college or university administrators with concerns to improve instruction and with access to resources that can support change. It is a position that requires insight and discernment on the part of the chair: to understand the priorities of the dean or provost and to comprehend the nature and potential of the initiative that faculty members are proposing. What will it take to implement a particular change? How can it be sold to the dean? What worries of the dean can be matched to ideas from the faculty?

How are all faculty who participate in annual/merit, promotion, and tenure evaluations educated about the meaningful inclusion of measures of teaching excellence in those processes? How closely does the chair review the outcomes of those processes to ensure teaching is indeed meaningfully included?

Finally, there is this elephant standing in the background of every effort to improve teaching and learning: How will it effect promotion and tenure? In my early years at Penn State, I was told that the dean of science was concerned about any faculty member that received high praise for teaching, because that might be a sign that they were neglecting their research. Even in my later years there, I found it necessary to discourage untenured faculty from sinking too much time into educational efforts. Unfortunately, the bifurcation of the faculty that I wrote about in October, separating tenure line faculty from contract faculty, only exacerbates this problem. With the option to “drop down” to a non-tenure line, the pressure to publish and receive research grants is all the greater.

Women in the Profession

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In last month’s column, I described the loss of tenure positions and their replacement with other full-time faculty appointments. This month, I will focus on how this has affected women earning PhDs in the mathematical sciences, also drawing on the Annual Survey of new PhDs, made available through AMS.

The first observation is that, while the number of tenured and tenure-eligible female faculty has increased by a third since 1995, most of the employment gains have been in other-full-time positions, which have more than tripled (Figure 1).

The first observation is that, while the number of tenured and tenure-eligible female faculty has increased by a third since 1995, most of the employment gains have been in other-full-time positions, which have more than tripled (Figure 1).

Figure 1. The number of women employed in U.S. departments of mathematics,
applied mathematics, or statistics. T & TE = tenure or tenure-eligible. Other full-time includes post-docs.
Source: CBMS Surveys for 1995, 200, 2005, 2010, 2015.

This is particularly noticeable in PhD-granting mathematics departments, where a woman employed full-time is far less likely than a man to be in a tenure or tenure-eligible position (Figures 2 & 3). In 2015, 80% of the men employed full-time in a PhD-granting department were in tenure or tenure-eligible positions, this fraction having dropped from 91% in 1995. For women, the percentage fell from 65% in 2015 to 44% in 2015.

Figure 2. The number of women employed in PhD-granting U.S. departments of mathematics, applied mathematics, or statistics.
T & TE = tenure or tenure-eligible. Other full-time includes post-docs.
Source: CBMS Surveys for 1995, 200, 2005, 2010, 2015.

Figure 3. The number of men employed in PhD-granting U.S. departments of mathematics, applied mathematics, or statistics. T & TE = tenure or tenure-eligible. Other full-time includes post-docs.
Source: CBMS Surveys for 1995, 200, 2005, 2010, 2015.

Despite the appearance that women are making substantial gains in tenure and tenure-eligible positions in PhD-granting departments, the fact is that they have only grown from 9% of those faculty in 1995 to 16% in 2015. In comparison, in Masters-granting departments the percentage of women in tenure and tenure-eligible positions rose from 18% in 1995 to 29% in 2015. At undergraduate colleges, it rose from 26% in 1995 to 32% in 2015. Over the same two decades, women rose from 22% of the PhDs awarded by mathematics departments to 26%.

If we look at all PhDs awarded to women in the mathematical sciences, now including departments of statistics or applied mathematics, the situation looks better, rising to 31% in 2015 (Figure 4), with women earning 33% of the PhDs in applied mathematics and 46% of those degrees in statistics.

Figure 4. Women as a percentage of new PhDs in the mathematical sciences in the U.S. by type of department.
 Source: The Joint Data Committee’s Annual Survey available at AMS.org, 1995 through 2015.

CBMS does not collect the data that would enable us to make comparable statements about the type of employment gained by mathematicians from other underrepresented groups and the numbers are so small it is not clear how meaningful they would be, but it does appear that efforts to broaden the diversity of mathematics departments is being stymied by the trend to replace tenure-line positions with contract positions. At least for women, their expanding representation in mathematics faculty is happening primarily in those contract positions.

The Loss of Tenure Positions: Threats to the Profession

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The preliminary tables from the CBMS 2015 surveys of U.S. departments of mathematics or statistics are now available from the CBMS homepage at CBMSweb.org or by clicking HERE. I am using this month’s column to highlight one of the most dramatic developments: the loss of tenured and tenure-eligible faculty (Figure 1). At the end of this article, I reflect on the implications for our profession.

Figure 1. Number of faculty in mathematics departments.
T  & TE = tenured or tenure-eligible, other full-time includes post-docs.

The year 2015 saw the fewest tenured or tenure-eligible faculty, 15,270, since 1995, a drop of two thousand positions since 2005. Where they have gone is no mystery. The number of other full-time faculty, including post-docs, has tripled over the past two decades, from 2140 in 1995 to 6427 in 2015.

The break-down by type of institution—according to the highest degree offered by the mathematics department: PhD, Master’s, or Bachelor’s—is interesting. PhD-granting universities have seen remarkably constant numbers of tenure positions, Master’s universities have seen the greatest loss, and undergraduate colleges saw a spike around 2005 and have now returned to the number of positions in 1995. The growth in other full-time positions has been most dramatic at the PhD-granting universities, from 758 in 1995 to 2336 in 2015 (Figures 2–4).

Figure 2. Distribution of faculty in PhD-granting mathematics departments.

Figure 3. Distribution of faculty in Master’s-granting mathematics departments.

Figure 4. Distribution of faculty in Bachelor’s-granting mathematics departments.

It is not that we now have fewer students to teach. Since 2005, the number of students studying mathematics in four-year under undergraduate programs has grown from 1.6 to over 2.2 million, an increase of 38% (Figure 5). If we add in the statistics courses taught within mathematics departments, the number of students enrolled each fall has jumped from 1.79 to 2.53 million, almost three-quarters of a million additional students. This dramatic growth holds even when we restrict to students at the level of calculus instruction and above, where the past decade has seen an increase of 262,000 students (Figure 6). To meet this increased demand while dropping two thousand tenure positions, we have added over three thousand other full-time faculty and one thousand part-time faculty.

Table 5. Undergraduate enrollment in mathematics in four-year programs. Calculus level includes sophomore-level differential equations, linear algebra, and discrete mathematics. Advanced is any math course beyond calculus level. These do not include statistics.

Table 6. Undergraduate enrollment at calculus level and above.

Not surprisingly, this means that undergraduate courses are now much less likely to be taught by a tenured or tenure-eligible faculty member. Figures 7 and 8 show what has happened at the PhD- granting universities. The 2015 survey was the first time that mainstream Calculus I and Calculus II were less likely to be taught by tenure line faculty than by other full-time faculty.

Table 7. T &TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus I.
After 2000, it is the percentage of sections.

Table 8. T & TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus II.
After 2000, it is the percentage of sections.

The trends are similar at Master’s universities and Bachelor’s colleges, though not as dramatic (Figures 9–12, following the Reflection).

Reflection. The CBMS data confirm what I have seen in departments across the country, especially in PhD- and Masters-granting departments. More and more of the undergraduate instruction is now the responsibility of contract faculty. In our research universities, it is becoming unusual for a tenured faculty member to teach any undergraduate courses. The unfortunate consequence is that the teacher-scholar, the ideal when I entered the profession, is fast disappearing. Those who are most active in mathematical research receive few teaching responsibilities. The remainder are saddled with heavy teaching loads that leave little time for research.

The reality of this bifurcation of the profession hit home in a recent network analysis of faculty interaction around issues of teaching, undertaken by the MAA’s Progress through Calculus project at a large public university. We found that tenure line faculty only interact with other tenure line faculty, contract faculty only with other contract faculty, with just a few individuals to provide a bridge. In effect, it has become two departments, one for undergraduate teaching and the other for research and the preparation of graduate students.

The teaching faculty are now manifestly second-class members of the profession: earning less money and receiving fewer benefits, carrying heavier prescribed duties, often lacking input in departmental decision-making, and living with the reality that, even with a renewable contract, long-term prospects are uncertain. It is no wonder so many of them have chosen to unionize.

There also are disturbing implications for the research faculty. Unlike Engineering or many of the other sciences, tenured mathematics faculty members seldom receive research grants that cover the full cost of their employment. Our public research universities have justified the size of their departments of mathematics by the large load of service teaching these departments must provide. Administrators are already questioning the wisdom of supporting a large corps of mathematics researchers who contribute ever less to the activities that pay the university’s bills.

We cannot turn back the clock, but there are mechanisms that can mitigate the dangers: involving contract faculty in departmental committees and decision making, involving tenure line faculty in observing and supporting those who carry the brunt of the teaching responsibilities, and ensuring that everyone is respected. There was one simple action that I observed at the Colorado School of Mines, a PhD-granting department. On the bulletin board that posts pictures of the faculty, contract faculty were not segregated from tenure line faculty. All members of the faculty were together in alphabetical order. What a radical idea.

Table 9. T & TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus I.
After 2000, it is the percentage of sections.

Table 10. T & TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus II.
After 2000, it is the percentage of sections.

Table 11. T & TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus I.
After 2000, it is the percentage of sections.

Table 12. T & TE = tenured or tenure-eligible, other full-time includes post-docs.
For 1995 and 2000, % is percentage of total students taking Calculus II.
After 2000, it is the percentage of sections.

**Editorial note: Figures 1-4 were updated on October 27, 2017.

Mathematics as Peacock Feathers

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Mathematics occupies a privileged position in our educational system, generally equated with English language facility—reading and writing—for emphasis within the K-12 curriculum, in curriculum reform efforts such as Common Core, in admissions testing with SAT and ACT, and in college graduation requirements. Why? An important recent article by Daniel Douglas and Paul Attewell, “School Mathematics as Gatekeeper,”[1] draws on data from the Education Longitudinal Study of 2002 (ELS:2002) [2] to explore this question.

A common response is that in today’s technologically driven society, mathematical knowledge is more essential than ever. Yet, as the authors document, the fact is that few workers, even in those jobs that require a bachelor’s degree, use mathematics at or above the level of Algebra II on a regular basis.

Of course, no one argues that actually factoring a quadratic or finding a derivative are essential skills for today’s workplace. Instead it is the habits of mind that learning mathematics instills that are considered so important. Douglas and Attewell look at the other side of this connection. It has been extremely difficult to demonstrate that mathematics instruction does lead to the development of logical thinking and effective problem solving, but society does recognize those who are successful in mathematics as talented individuals who are primed for success. The authors explore the role of mathematical achievement as a signal that a prospective student or employee is going to succeed, just as a peacock’s feathers signal a male capable of fathering strong offspring (Figure 1).

Figure 1. From Bob Orlin’s “The Peacock Tail Theory of AP® Calculus.”

mathwithbaddrawings.com/2016/07/20/the-peacock-tail-theory-of-ap-calculus/

Signals are important. Those who are believed capable of succeeding are more likely to get the support and encouragement they need to succeed. Douglas and Attewell were able to draw on ELS:2002, a ten-year longitudinal study of survey data and transcripts of 15,000 U.S. students, to test whether mathematical achievement in high school has such a signaling effect. Able to control for the common variables associated with success: general academic performance in high school, motivation, effort, academic involvement, gender, race/ethnicity, socio-economic status (SES), and parental education, they took as their null hypothesis that mathematical achievement—especially having studied precalculus or calculus in high school—would add nothing to the chances of being admitted to and graduating from a four-year college program.

That null hypothesis was firmly rejected with a p-value less than 0.001. Controlling for all of those other factors, taking trigonometry or precalculus as the last high school math class was associated with increased odds of attending a four-year college, close to two times those of students whose last mathematics class was Algebra II. The odds of attending a selective college were doubled. Calculus in high school is an even stronger signal, associated with the increased odds of attending a four-year college by a factor of two and a half, and attending a selective college by a factor of three. Again controlling for all of these other variables, completing any of these courses nearly doubled the odds of earning a bachelor’s degree.

In the other direction and still controlling for all other factors, terminating high school mathematics at Algebra I was associated with far lower odds of attending a four-year college—by a factor of one half. The odds of earning a bachelor’s degree among students completing only Algebra I were about a quarter of that for students for whom Algebra II was the highest mathematics course taken in high school.

Reporting marginal effects, the authors note that students taking precalculus as the last high school mathematics course were 12 percentage points more likely to attend a four-year college than those for whom Algebra II was the last class. A precalculus class also raised the likelihood of attending a selective college by 12 percent, and of earning a bachelor’s degree by nine percent. Similarly, taking calculus in high school boosted the likelihood even further: 16 percent for four-year colleges, 18 percent for a selective college, and 10 percent for earning a bachelor's degree.

Perhaps surprising is the fact that this signaling effect is strongest for students of high SES. Using a composite score of mathematical ability as measured by the ELS:2002 standardized test in mathematics and the highest mathematics course taken in high school, students scoring one standard deviation above the mean increased their likelihood of attending a selective college by 12 percent. For students with high SES, it increased by 25 percent. It is important to note that while these findings are statistically significant associations, they should not be interpreted as statements of causality.

Conclusions

The authors emphasize the irony of the very strong signal sent by advanced work in high school mathematics given how small a role it plays in actual workforce needs. It is my personal belief that the strong signaling effect of mathematical achievement points to something real, an analytic ability that goes beyond the other talents for which this study controlled: general academic performance in high school, motivation, effort, and academic involvement, but that the signal has been amplified beyond reason. This has important implications.

The common perception that calculus on a high school transcript helps a student get into a selective college is supported by these data. It also appears to improve the chances of completing a bachelor’s degree. Given that this effect is strongest for students of high SES, those with parents who are best positioned to push to accelerate their sons and daughters, the trend to bring ever more students into calculus at an ever earlier point in their high school careers is rational. Rational does not mean desirable, or even necessarily appropriate, but it does mean that trying to counter the growth of high school calculus will require more than recommendations and policy statements. If misapplied acceleration can do harm, as many of us believe, we need convincing evidence of this.

The work of Douglas and Attewell should also inform the debate over requiring Algebra II in high school. Those who oppose this as a requirement for all students point out that few will need the skills taught in this course; this perspective is highlighted by the study authors, though they do not believe mathematics requirements should be summarily dismissed. The problem is the self-reinforcing signal sent by not having Algebra II on one’s transcript. Their work also points to the importance of making precalculus and calculus available to all students who are prepared to study them. Lack of access in high school does more than postpone the opportunity for their study; the evidence suggests that it actually damages chances of post-secondary success.

References

[1] Douglas, D. and Attewell, P. (2017). School mathematics as gatekeeper. The Sociological Quarterly. www.tandfonline.com/doi/abs/10.1080/00380253.2017.1354733

[2] National Center for Education Statistics (NCES). nces.ed.gov/surveys/els2002/

Changing Demographics

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In the United States, the mathematically intensive disciplines—engineering, the mathematical sciences, and the physical sciences—have traditionally been dominated by White males. It is common knowledge that the U.S. population is changing. Data from the National Center for Education Statistics (NCES) of the Department of Education  show that while 73% of high school graduates in 1995 were White, by 2015 that had decreased to 55%, on track to drop below 50% by 2025, in just eight years (Figure 0.1). These and all data in this article are taken from the NCES Digests of Education Statistics, 1990 through 2017, available at nces.ed.gov/programs/digest/. 

It has become a truism that if the United States is to maintain its pre-eminence in science and technology, we must ensure that traditionally underrepresented minorities share in this preparation for mathematically intensive careers. Groups like the Mathematical Association of America have programs such as the Tensor grants that encourage students from underrepresented groups to succeed in mathematics.

Figure 0.1. White non-Hispanic students as percentage of all high school graduates. Percentages after 2012 are estimates based on the number of students already in the K-12 pipeline. Note: scale starts at 40%.

While we have seen and should continue to see a modest increase in the percentage of Asian and Black students, most of the changing demographics are shaped by the dramatic growth in the number of Hispanic students, which grew from 9% of high school graduates in 1995 to 21% in 2015, projected to reach 27% by 2025 (Figure 0.2).

Figure 0.02  Black non-Hispanic, Hispanic, and Asian/Pacific Islander students as a percentage of all high school graduates. Percentages after 2012 are estimates based on the number of students already in the K-12.

The intent of this article is simply to exhibit the data showing how well we are including various racial, ethnic, and gender groups among the recipients of bachelor’s degrees in engineering, the mathematical sciences, and the physical sciences. In future articles, I will address some of the ways in which MAA and other organizations are addressing the issues raised by these trends. 

The bulk of this paper is taken with three appendices that show the graphs of the percentage of bachelor’s degrees earned in these three areas by each of the following demographic groups:

• Figure x.1. Women
• Figure x.2. White students, also reported by gender
• Figure x.3. Black students, also reported by gender Figure x.4.
• Hispanic students, also reported by gender
• Figure x.5. Asian students, also reported by gender
• Figure x.6. Non-resident alien students, also reported by gender where x is 1 for engineering, 2 for the mathematical sciences, and 3 for the physical sciences.

Native Americans/Alaskan Natives account for 1.1% of high school graduates, 0.3% to 0.5% in engineering and mathematics, and 0.5% to 0.8% in the physical sciences. These numbers are so small that there is tremendous year-to-year variation, and the graphs do not exhibit meaningful trends. Only in 2011 did NCES begin separating Asian from Pacific Islander and begin to allow students to self-identify as two or more races. For the sake of consistency, all of the data reported for Asian students include Pacific Islander students. In the disciplines of engineering, mathematical sciences, and physical sciences, Pacific Islanders make up between 0.1% and 0.2% of the total majors. In the first year that the choice of two or more races was allowed, about 1% of the students in each of the three disciplines so identified. This had risen to 3% by 2015. From 2011 through 2015, about 2% of high school graduates identified as two or more races.

Observations

Probably the most striking graph in this entire collection is Figure 2.6, showing the proportion of mathematics degrees going to non-resident aliens. Historically, this has been around 4%. It began to take off in 2008. By 2015, 13% of the bachelor’s degrees in the mathematical sciences were awarded to non-resident aliens. While we welcome these visitors and hope that many of them will stay, it is disturbing that so much of our mathematical talent must be imported. As shown in Figures 1.6 and 3.6, there have also been increases in the fraction of engineering and physical science degrees earned by non-resident aliens, but here the growth has not been nearly as dramatic.

A very disturbing set of graphs are given in Figures 1.3, 2.3, and 3.3, showing the proportion of degrees earned by Black students. In all three disciplines, we see a pattern of substantial growth during the 1990s, followed by a period of leveling off, followed by substantial decline. In engineering and the physical sciences, the percentage of degrees earned by Black students has dropped to levels not seen since 1993. In mathematics, the percentage of degrees awarded to Black students in 2015, 4.6%, is below that of 1990, when it was 5.0%.

The graphs showing the percentage of women in these fields, Figures 1.1, 2.1, and 3.1, are also discouraging. Engineering has always had a difficult time attracting and retaining women. By 2000, they had managed to get the proportion of degrees going to women over 20%, but it then slipped back to 18%. The good news is that the fraction of engineering degrees to women began growing again in 2010 and is now back to the 20% mark, far too low, but headed in the right direction.

In the physical sciences, there was dramatic growth in the participation of women, from 31% in 1990 to over 42% in 2002. It has been slipping since then, now back almost to 38%.
Compared to engineering and the physical sciences, the mathematical sciences have done very well, but we were at 46% in 1990 and achieved 48% in 1998. We have since slipped back to 43%. The recent trend line looks decidedly flat.

The brightest spot in these data is the substantial increase in the proportion of Hispanic students among these mathematically intensive majors (Figures 1.4, 2.4, and 3.4). Given the dramatic increase in the percentage of students of traditional college age who are Hispanic, an increase of 125% from 1995 to 2015, engineering and mathematics—with only 110% increases in the proportion of majors who are Hispanic—are not doing as well as they should. The physical sciences have seen the most dramatic increase, but starting from an extremely low base. Nevertheless, today around 9% of the majors in all three disciplinary areas are Hispanic, and strong growth continues.

Asian students have always been well represented in engineering, the mathematical sciences, and the physical sciences, currently at or above 10% of those degrees (Figures 1.5, 2.5, and 3.5).



Appendix I. Bachelor’s degrees in Engineering

Figure 1.1. Women as a percentage of all bachelor’s degrees in engineering. Note: scale starts at 10%.

Figure 1.2. White non-Hispanic students as percentage of all bachelor's degrees in engineering. 

Figure 1.3. Black non-Hispanic students as a percentage of all bachelor's degrees in engineering.

Figure 1.4. Hispanic students as a percentage of all bachelor's degrees in engineering.

Figure 1.5. Asian students as a percentage of all bachelor's degrees in engineering. 

Figure 1.6. Non-Resident Alien students as a percentage of all bachelor's degrees in engineering.

Appendix II. Bachelor’s degrees in the Mathematical Sciences

Figure 2.1. Women as a percentage of all bachelor's degrees in the mathematical sciences. Note: Scale starts at 40%.

Figure 2.2. White non-Hispanic students as a percentage of all bachelor's degrees in the mathematical sciences.

Figure 2.3. Black non-Hispanic students as a percentage of all bachelor's degrees in the mathematical sciences.

Figure 2.4. Hispanic students as a percentage of all bachelor's degrees in the mathematical sciences. 

Figure 2.5. Asian students as a percentage of all bachelor's degrees in the mathematical sciences.

Figure 2.6. Non-Resident Alien students as a percentage of all bachelor's degrees in the mathematical sciences. 

Appendix III. Bachelor’s degrees in the Physical Sciences

Figure 3.1. Women as a percentage of all bachelor's degrees in the physical sciences. Note: scale starts at 30%

Figure 3.2. White non-Hispanic students as a percentage of all bachelor's degrees in the physical sciences.

Figure 3.3. Black non-Hispanic students as a percentage of all bachelor's degrees in the physical sciences.

Figure 3.4. Hispanic students as a percentage of all bachelor's degrees in the physical sciences.

Figure 3.5. Asian students as a percentage of all Bachelor's degrees in the physical sciences. 

Figure 3.6. Non-Resident Alien students as a percentage of all bachelor's degrees in the physical sciences.

The 2015 NAEP

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On May 5, 2017, the presidents and executive directors of the member societies of CBMS received a report from Samantha Burg and Stephen Provasnik at the U.S. Department of Education’s National Center for Education Statistics (NCES) on the results in mathematics from the 2015 studies by the National Assessment of Educational Progress (NAEP) and Trends in International Mathematics and Science Study (TIMSS). The full PowerPoint of their presentation, covering both the 2015 NAEP and 2015 TIMSS, can be accessed at www.cbmsweb.org/2017/05/presentations.

Both assessments are conducted for students at grades 4, 8 and 12. NAEP is a federally mandated assessment of student achievement in the U.S. and is conducted every other year. TIMSS provides an international comparison and is run every four years for ages equivalent to grades 4 and 8. The 12th grade TIMSS is restricted to advanced mathematics students (in the U.S. those who have taken a course like AP Calculus). It was administered in 2015 for the first time in the U.S. since 1995.

The scores since 1990 for the 4 th and 8 th grade NAEP and since 2005 for the 12th grade are shown in Figures 1, 2, and 3. The distinguishing features for grades 4 and 8 are the strong growth from 1990 until 2007 and relative stagnation since then, with a small but statistically significant drop (except for the 90th percentile in grade 4) between 2013 and 2015. The 12th grade scores also show a drop since 2013 that is statistically significant at and below the 50th percentile.

This drop is a cause for concern, but not yet alarm. NCES is eagerly anticipating the 2017 NAEP results to see whether the downturn was simply a blip in what is essentially a stable state or the start of something more troubling.

Figure 1: NAEP scores for grade 4.
Source: Burg & Provasnik, 2017.

Figure 2: NAEP scores for grade 8. Source: Burg & Provasnik, 2017.

Figure 3: NAEP scores for grade 12.
Source: Burg & Provasnik, 2017.

An obvious question is whether the Common Core State Standards in Mathematics (CCSS-M) have had any effect on student scores. One hypothesis is that changing the curriculum has introduced enough confusion and uncertainty among teachers that it is having a visibly negative effect. Another hypothesis, which has some supporting evidence, is that the choices of topics for assessment may no longer be completely aligned with what is being taught.

The largest drops at Grade 4 were in the subject areas of Geometry and Data Analysis (Table 1). The NAEP Validity Studies (NVS) panel (Daro, Hughes, & Stancavage, 2015) found some misalignment between the NAEP questions and the CCSS-M curriculum. They found that 32% of the Data Analysis questions were either not covered in CCSS-M or were covered after grade 4. In Geometry, 18%, of the NAEP questions were covered after grade 4 in CCSS-M. In the other direction, only 57% of CCSS-M standards for Operations and Algebraic Thinking by grade 4 were covered by NAEP questions.

Table 1: Changes in NAEP scores, 2013 to 2015, by subscale topics.
Source: Burg & Provasnik, 2017

For grade 8, the misalignment occurs in both directions within Data Analysis. In the 8 th grade NAEP, 17% of the Data Analysis questions had not yet been covered in CCSS-M, and 59% of what is specified for statistics and probability by grade 8 in CCSS-M was not assessed by NAEP. For grade 12, there was a uniform 2-point drop across all subscales.

These observations raise interesting questions about the construction of future NAEP instruments. Because of the need for comparability from one test administration to the next, the distribution of topics has not changed. While CCSS-M is not the national curriculum that was once envisioned, the fact is that almost all states have aligned their standards with its expectations. NAEP may need to change to reflect the reality of what is taught by grades 4 and 8.

The breakdowns by race/ethnicity and gender for the overall mathematics scores in grades 4 and 8 (Table 2) show comparable increases from 1990 to 2015, and comparable declines since 2013. Black students in grade 4 saw the greatest gains since 1990, but at a score of 224 they are still well below the national average.

Table 2: Changes in NAEP Math scores for grades 4 and 8 by race/ethnicity and gender.
 Source: Burg & Provasnik, 2017.

At grade 12, the strongest gains since 2005 have been for Asian and Hispanic students (Table 3, Pacific Islanders are such a small proportion of Asian/Pacific Islander that it is not clear how their scores have changed, and the doubling of the percentage identifying as Two or More Races makes it difficult to compare the 2005 and 2015 scores). An interesting insight lies in the shift in the demographics of 12 th grade students. In ten years, the percentage of White students dropped from 66% to 55%, while the percentage of Hispanic 12 th graders rose from 13% to 22%.

Table 3: Changes in NAEP Math scores for grade 12 by race/ethnicity and gender.
 Source: Burg & Provasnik, 2017.

Next month I will be looking at the changing demographics of bachelor’s degrees earned in engineering, the mathematical sciences, and the physical sciences. In mathematics, the decline in the percentage of degrees in mathematics going to White students has been in line with the decline in their overall percentage at that age group, from 72.4% in 2005 to 59.6% in 2015 (NCES, 2005–2015). Some of this has been made up by a significant increase in mathematics degrees going to Hispanic students, from 5.7% to 8.9%, but the percentage of bachelor’s degrees in mathematics earned by Black students decreased from 6.1% to 4.7% over this decade, while Asian students remained essentially stable, 10.2% to 10.6%. Most of the shift has gone to non- resident aliens who accounted for 5.0% of the mathematics degrees in 2005, but 12.9% in 2015.

References
Burg, S. & Provasnik, S. (2017). NAEP and TIMSS Mathematics 2015. Presentation to the Conference Board of the Mathematical Sciences, May 5, 2017. Available at www.cbmsweb.org/2017/05/presentations/

Daro, P., Hughes, G.B., & Stancavage, F. (2015). Study of the alignment of the 2015 NAEP mathematics items at grades 4 and 8 to the Common Core State Standards (CCSS) for Mathematics. NAEP Validity Studies Panel report. Washington, DC: American Institutes for Research. Available at www.air.org/sites/default/files/downloads/report/Study-of- Alignment-NAEP-Mathematics- Items-common- core-Nov- 2015.pdf

National Center for Education Statistics (NCES). (2005–2015). Digest of Education Statistics. Available at nces.ed.gov/programs/digest/

Note:
In compliance with new standards from the U.S. Office of Management and Budget for collecting and reporting data on race/ethnicity, additional information was collected beginning in 2011 so that results could be reported separately for Asian students, Native Hawaiian/Other Pacific Islander students, and students identifying with two or more races. In earlier assessment years, results for Asian and Native Hawaiian/Other Pacific Islander students were combined into a single Asian/Pacific Islander category.

As of 2011, all of the students participating in NAEP are identified as one of the following seven racial/ethnic categories:

• White 
• Black (includes African American) 
• Hispanic (includes Latino) 
• Asian 
• Native Hawaiian/Other Pacific Islander 
• American Indian/Alaska Native 
• Two or more races

When comparing the results for racial/ethnic groups from 2013 to earlier assessment years, results for Asian and Native Hawaiian/Other Pacific Islander students were combined into a single Asian/Pacific Islander category for all previous assessment years.

Re-imagining the Calculus Curriculum, II

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Last month, in "Re-imagining the Calculus Curriculum," I, I introduced Project DIRACC (Developing and Investigating a Rigorous Approach to Conceptual Calculus), developed by Pat Thompson, Mark Ashbrook, and Fabio Milner at Arizona State University. References to the theory underpinning this approach are given at the end of this column. This month’s column will expand on some details of this curriculum.

One of the first common student misconceptions that Project DIRACC tackles is that variables are simply stand-ins for unknown quantities. The authors begin the meat of his course in Chapter 3 with an explanation of the distinction between variable, constant, and parameter, pointing out how context-specific the designations as either variable or parameter can be. One of the distinctive features of this project is the thoughtful use of technology, in this case enabling students to play with the effect of varying a variable with a variety of choices of parameter (see patthompson.net/ThompsonCalc/section_3_1.html).

This leads to relationships between variables (how volume varies with height), and then functions as a special class of relationships between variables, one in which “any value of one variable determines exactly one value of the other.” The point is that the f in f (x) has meaning. It is the name of the relationship. This enables the authors to tackle the misconception that f (x) is simply a lengthy way of expressing the variable y.

While acknowledging that f(x) can represent a second variable, they emphasize that it is shorthand for “the value of the relationship f when applied to a value of x.” This point is driven home by an example of the usefulness of functional notation. If d(x) relates a moment in time, x measured in years, to the distance between the Earth and the Moon at that time, then d(x) – d(x–5) enables us to express the change in distance over the five years before time x, while d(x+5) – d(x) expresses the change in distance over the succeeding five years.

The authors also make the important distinction between functions defined conceptually—the distance between Earth and Moon at a given time—and those defined computationally, such as V(u) = u(13.76 – 2u)(16.42 – 2u). They then proceed to devote considerable effort to describing the structure of functions as they are built from sums, products, quotients, compositions, and inverses. This includes clarifying the distinction between the independent variable and the argument of a function. Thus for f (x/3 + 5) the independent variable is x, but the function argument is x/3 + 5, an important step toward understanding composition of functions.

While function structure should be part of precalculus, the importance of including this material has been revealed in exploring student difficulties with differentiation. Given a complicated computational rule that defines a function, students often have difficulty parsing this rule and thus determining the choice and order of the techniques of differentiation they need to use.

Rates of change are now introduced in Chapter 4. The authors distinguish between ∆x, the parameter that describes the length of a small subinterval of the domain, and the changes in x and y represented by the differentials dx and dy. These are variables that within the given subinterval are always connected by a linear relationship.

A nice illustration of how this works is given with a photograph of a truck traveling through an intersection (Figure 1).

Figure 1. A photo of truck taken with a shutter setting of 1/1000 sec.

Taken at a shutter speed of 1/1000th of a second, it appears to freeze the truck. But if you zoom in on the tail light (Figure 2, see Section 4.3 for a video of the zoom), the streaks reveal that the truck was moving.

Figure 2. A closer look at the truck's tail light shows small streaks.
The truck moved slightly while the camera's shutter was open.

One can even estimate the length of the streaks to approximate the velocity of the truck. Over 1/1000th of a second, it is doubtful that the truck’s velocity changed very much. The picture of the truck was taken at a “moment” in time, but that moment stretched over 0.001 seconds. The point is that this period of time is short enough that the truck’s velocity measured as change in distance over change in time is “essentially constant.” If y is position and x is time, then over this interval of length ∆x = 0.001 seconds, we can treat the variable dy as a constant times dx. It is this constant that is used to define the rate of change at a moment,

We say that a function has a rate of change at the moment x₀ if, over a suitably small interval of its independent variable containing x₀, the function’s value changes at essentially a constant rate with respect to its independent variable.

Significantly, even as the authors are defining the rate of change at a moment, they emphasize that “all motion, and hence all variation, is blurry.”

Note that there is no mention of limits, a means of defining the derivative that is often more confusing than enlightening (see the 2014 Launchings columns from July, August, and September).

After further discussion and exploration of rate of change functions, the authors now move in Chapter 5 to Accumulation Functions, building up total changes from rates of change that are essentially constant on very small intervals. These give rise to what are anachronistically referred to as left-hand Riemann sums. Students use technology to explore the increasing accuracy as ∆x gets smaller. The effect of the choice of starting value is noted, and the definite integral with a variable upper limit now appears. It is important that the first time students see a definite integral it has a variable upper limit.

In Chapter 6, the inverse problem, going from knowledge of an exact expression of the accumulation function to the discovery of the corresponding rate of change function, is now explored, leading to the Fundamental Theorem of Integral Calculus in the form: The derivative with respect to x of the definite integral from a to x of a rate of change function is equal to that rate of change function evaluated at x. Techniques and applications of differentiation follow as the semester concludes.

The great strength and promise of this approach is that the traditional content of the first semester of calculus is only slightly tweaked, especially since it is increasingly common for university Calculus I courses to avoid or significantly downplay limits. But the curriculum has been totally reshaped to address common student difficulties and misconceptions. This route into calculus has the added advantage—though perhaps a disadvantage in the eyes of some students—that those who have been through a procedurally oriented course are unlikely to recognize this as an accelerated repetition of what they have already studied. It will challenge them to rethink what they believe calculus to be.

References

Thompson, P.W. and Silverman, J. (2008). The concept of accumulation in calculus. In M.P. Carlson & C. Rasmussen (Eds.), Making the connection: Research and teaching in undergraduate mathematics (MAA Notes Vol. 73, pp. 43–52). Washington, DC: Mathematical Association of America.

Thompson, P.W., Byerley, C. and Hatfield, N. (2013). A conceptual approach to calculus made possible by technology. Computers in the Schools. 30:124–147.

Thompson, P.W. and Dreyfus, T. (2016). A coherent approach to the Fundamental Theorem of Calculus using differentials. In R. Göller. R. Biehler & R. Hochsmuth (Eds.), Proceedings of the Conference on Didactics of Mathematics in Higher Education as a Scientific Discipline (pp. 355–359 ) Hannover, Germany: KHDM.