As part of the Cerritos College Faculty Inquiry project (SPECC), participating faculty began a process of thinking deeply about their teaching practices within a selected developmental class. Throughout the semester, participating faculty researchers were mentored in a sequence of guided reflections by faculty outside their disciplines and experienced with SoTL. The mentors assisted the faculty researchers in both thinking through the process and articulating their thoughts in writing. The mentors worked in pairs, each pair being assigned either the math faculty researchers or the English faculty researchers. The guided reflections addressed faculty interpretation and understanding about the:

• Description of Course, including a description of prerequisite knowledge, course content, students, and their satisfaction and frustration with the selected developmental course;
• Teaching Methods, including a description of their class organization, assessments, assignments, and intended outcomes;
• Analysis of Student Learning, including examples and their analysis of three levels of student performance; and
• Planned Changes, including a description of what the faculty member plans to do differently as a result of their analysis of student performance.

Responses to each faculty researcher’s reflections were posted to an electronic portfolio. Faculty reflections and responses are available here.

A Carnegie Perspectives repost

By Pat Hutchings and Lee Shulman
It’s hard to find a campus in these days of number crunching and accountability that doesn’t have some kind of office of institutional research. These offices vary a lot, with large research universities supporting a staff of a dozen or more, and small colleges sometimes relying on a person—or half a person—to get the job done. But what exactly is the job? Traditionally, institutional research has been treated as a kind of company audit, sitting outside the organization’s inner workings but keeping track of important trends and facts—about enrollment patterns, student credit hours, graduation rates, peer institutions, and so forth—requested by both internal and external constituencies.

But imagine a different way of thinking about institutional research as a capacity to work closely with faculty to explore questions about what students are actually learning. Such a shift would mean asking much tougher, more central questions: What do our students know, and what can they do? What do they understand deeply? What kinds of human beings are they becoming—intellectually, morally, in terms of civic responsibility? How does our teaching shape their experience as learners, and how might it do so more effectively?

As part of a Carnegie Foundation project focused on pre-collegiate, developmental education in community colleges (in partnership with The William and Flora Hewlett Foundation, we are working with 11 institutions in California), we recently brought together a group of institutional research directors and faculty to talk about the kinds and sources of data that are needed to improve teaching and learning for the many students who are unprepared to enter college-level courses and who often fail on the long road through one remedial course after another. On the one hand, institutional research is an underfunded, undervalued function on many two-year campuses, and we heard from those who work in IR offices about the frustration of spending scarce time and resources generating information that faculty never see. At the same time, we heard from faculty who wish that the kinds of evidence that are most important for making changes at the classroom level could be made more readily available, and be more valued, at “the top.” But we also heard about some encouraging efforts to bridge these gaps.

At Los Medanos College, for example, getting better information to guide improvement has been part of a shift of focus from “the underprepared student” to “the prepared institution.” The college’s Developmental Education Committee works with staff from the Office of Institutional Research to develop a research agenda that yields data faculty members can use to monitor improvements in student learning. Recently, the Committee asked the IR office to study the relative success rates in elementary algebra of students who had different levels of preparation—requiring data much more specific than what is usually provided by the IR office for program review. “We gathered this data over a two-year period and discovered significant differences in success rates based on type of preparation,” Myra Snell, a professor of mathematics, told the group. “This information was instrumental in several changes: We established a prerequisite for elementary algebra, changed scheduling patterns in the math department, and are now experimenting with different modes of instruction for basic skills curriculum.”

City College of San Francisco—a much different, much larger institution—has developed a Web-based Decision Support System. The DSS contains data from 1998 through the present on student enrollment, student demand for classes, departmental productivity, student success as measured by grades, course completion, degrees and certificates, and student characteristics, all of which are available in response to queries from faculty and staff. Thus, an instructor of pre-collegiate English might use the system to find out if different student groups—by race or age—are particularly at risk in a key sequence of courses in which he or she is teaching. The department might use the system to see how changes in teaching and curriculum are reflected, or not, in patterns of student success over time. Importantly, we heard from CCSF institutional research staff about the need to work directly with faculty—one-on-one, in small groups, and by departments—to help them envision ways to use the information; the promise, that is, lies not only in supplying good information but in cultivating a demand for it. A study of the DSS system found that the increased availability of data has produced a shift in how individuals imagine their role in using information for decision making.

The Carnegie project meeting generated enthusiasm for further bridge-building, as well. As more and more faculty embrace the scholarship of teaching and learning and begin gathering evidence about their students’ learning, it’s exciting to think about how rich, qualitative classroom-level information can be captured and integrated into larger data systems that others on the campus can access and build on. What may be needed is not an information superhighway but a friendlier set of neighborhood paths and backstreets that take people where they need to go as educators. This, in turn, may require a different way of organizing the work of institutional research—and resources to support its more central role.

To readers who do not work on a campus, all of this may sound like inside baseball. It’s not. Questions about who talks to whom, and about what kinds of information are institutionally valued and available, are central to an institution’s capacity to improve. And while the availability of data is never a sufficient condition for improvement, it is certainly a necessary one. Community colleges—with their “can do” attitudes, and their willingness to experiment—may well have things to teach the rest of higher education about the best ways to think about the evidence needed for improvement.

A Carnegie Perspectives repost

By Molly Breen

Every year hundreds of thousands of students begin their higher education in community colleges. Of course, these institutions also bring in large numbers of new faculty. For both groups, students and faculty alike, there are plenty of challenges to go around.

Imagine yourself in the shoes of a newly hired instructor at a community college. If you’re lucky, you’ve landed a full-time position, but more likely you’re working as an adjunct, teaching on one campus in the morning and another in the afternoon. You put in years writing an English thesis on, say, spiritual autobiography in the 18th century, or a math thesis on primal decomposition in modules and lattice modules, only to find yourself teaching basic literacy or numeracy skills in a class three levels below the first course in the transfer sequence. You don’t object to teaching students basic skills; in fact, you find it fascinating. You’ve just never had so much as a day of training on the subject. So what do you do?

Faculty members at California community colleges have been asking that question in large numbers lately, spurred on by numerous reports—from the Academic Senate, from the Hewlett Foundation, from the Chancellor’s office—that all point to the urgency around basic skills education. They have asked it of themselves, certainly, in private moments of bafflement or frustration, but as part of the Carnegie project Strengthening Pre-collegiate Education in Community Colleges (SPECC), they’ve also asked it of each other, transforming the question from “What do I do?” to “What do we do?”

Their work together has led to a number of improvements in teaching basic skills, including the innovative pairing of classes through learning communities and experiments with high-intensity teaching formats, particularly in math. But their initial questions have also led to further, sharper questions: Why do so many of my students earning a C or higher wind up dropping the class? What makes word problems so difficult for so many math students? How much of the homework that I assign do my students actually read? What is going on in my student’s head when he tackles a new equation? Is what I’m doing even working?

The faculty at work on the 11 SPECC campuses have tackled these questions through a variety of methods: observing each others’ classes; creating common finals and assessment methods; devising pre- and post- tests as a way of pinning down desired student learning outcomes; videotaping student “think-alouds” in mathematics; adapting metacognitive or “intentional” reading strategies to math and ESL classrooms, and many more.

Beyond sharing the results of these pedagogical experiments with each other, some faculty have taken the extra step of documenting their work on the web. These websites are rich with data. In one, the instructor posts the results of her department’s common algebra final and reflects on her students’ performance. Another site includes a video of four beginning ESL students, with four native languages between them, working together to unpack a poem in English. Indeed, as well as affording teachers the chance to cringe at their wardrobe choices on the day of filming, video allows instructors to capture student learning in all its compelling complexity, from a single student explaining where he gets stuck on a word problem to an entire class speculating on why an anonymous student from a previous semester had dropped out and what lessons they can take from that experience to increase their own chances of success.

These multimedia sites have been collected in the SPECC Windows on Learning Gallery. The sites can be used in a variety of ways: as archives of teaching and research materials; as hands-on resources for teachers who can download materials and study their implementation in an actual classroom; and as tools for professional development. A number of faculty presented their sites at the annual Strengthening Student Success conference held in San Jose, California in October, among other venues, and have used their sites to forge connections with community college instructors across the country doing similar research and exploring similar formats for making their work visible. An especially nice feature of these sites is that they preserve the trace of both teaching and inquiry, so that the complicated process of properly identifying a problem of learning; designing an intervention to address it; and evaluating the success of the intervention becomes clear.

Through this kind of documentation and exchange questions about teaching that once might have lead merely to migraines—or to a growing sense of isolation and disillusionment—lead to discussion, research, experimentation, data collection and further inquiry. All of these are processes that can be recorded and shared, and it is this act of recording, of making teaching visible, that creates a crucial difference between the sort of teaching that Carnegie President Lee Shulman has described as “evaporating at room temperature” and a more durable alternative. The more visible teaching becomes, and the more durable its best practices, the better for students.

And the better, certainly, for that new hire tackling the risks and rewards of teaching basic skills for the first time.

A Carnegie Perspectives repost

Michael C. Burke (College of San Mateo; Visiting Scholar, Carnegie Foundation)

In Mathematics and Democracy, Lynn Arthur Steen describes quantitative literacy as “a habit of mind, an approach to problems that employs and enhances both statistics and mathematics.” What characterizes this habit of mind, this way of thinking? Why is it important? How can it be taught?

These are questions much on my mind as a college mathematics teacher, but I believe they matter far beyond my discipline. Quantitative literacy, the ability to discriminate between good and bad data, the disposition to use quantitative information to think through complex problems-these are capacities that educators across fields should be helping students develop. I’d like to lead you to this conclusion through an extended example.

Princeton University economics professor Paul Krugman recently began a blog on The New York Times website. In his first post, Krugman wrote, “I’ll be using this space to present the kind of information I can’t provide on the printed page-especially charts and tables, which are crucial to the way I think about most of the issues I write about.” Krugman then introduces a graph that presents a picture of income distribution in the country by displaying the share of total income earned by the richest 10 percent of Americans.

Income distribution chart courtesy of Paul Krugman

On the basis of this graph, Krugman posits that the “middle class America” period from 1950 to 1970 produced a society “without extremes of wealth or poverty, a society of broadly shared prosperity,” and that this period is, in fact, an aberration. The years since 1970, as Krugman’s graph clearly shows, have been marked by a gradual return to the America that existed before what he calls The Great Compression in the World War II years, an earlier America characterized as the Gilded Age.

How did this shift occur? How did this country decide to return to a Gilded Age in which extremes of wealth and poverty were the norm? The answer, of course, is that we made no such conscious decision. Instead, we made numerous small decisions, without any articulated vision or plan. We did not see what we were doing, and could not therefore really “decide,” because we did not know how to look. There was public discussion, certainly, but it was fragmented, fractious, and marked by a conspicuous lack of grounding in the underlying reality of the America of the late twentieth century and the trends at work that led to the America of today.

In contrast, Krugman’s use of a graph illustrates an especially powerful way to look at and think about our world. The graph, he says, is “central to how I think about the big picture, the underlying story of what is really going on in this country.” Using the tools of economics, he shows us that things that are otherwise difficult to see or understand can sometimes become dramatically apparent when we look at the right graph, table or chart. These visual representations of data are indispensable tools for understanding, and they can often clarify what is obscured by the sound and fury of public debate.

Caveats are in order here, of course. Krugman uses the “right graph,” but it is also possible to construct the “wrong graph.” Graphs, like words, can be used to mislead. There is also a great deal of subtlety involved. As Edward Tufte elegantly illustrated in The Visual Display of Quantitative Information, it can be difficult to envision what the right graph for a given situation should look like. Which graph will illuminate rather than obscure, clarify rather than confuse?

But my larger point here is that the content of our thoughts and the depth of our understanding are dependent on the tools we bring to the task. What we think is intertwined with how we think. And the ability to think in terms of quantitative data, in terms of tables and graphs, is indispensable for understanding our modern world. This should be part of what we teach all our students-not just students in selected courses or selected majors.

With that aim in mind, I would propose that we begin by redesigning our freshman and sophomore writing programs in order to place a significant emphasis on working with quantitative data, and on the visual representation of that data. We write, after all, to figure out what we think. And we ask our students to write so that they will learn how to think.

I can imagine that many who oversee our writing programs would not be eager to implement such a program. After all, it is perhaps asking them to teach our students to think in ways that they themselves do not think. That’s a tall order, indeed. Of course, the responsibility for rethinking the way we teach writing on our campuses should be shared. Mathematics faculty, in particular, should take the lead here, but others who view the world through a quantitative lens-statisticians, economists, physicists, biologists, even some psychologists-should contribute as well. It is well past time for those of us with a quantitative cast of mind to become involved in a serious way with the writing programs on campus. For the majority of our students, this is where the action is, and accordingly, this should be one of the places where we concentrate our efforts.

We need to come to terms with some basic questions. Since the ability to think quantitatively is, in fact, essential to understanding today’s world and to acting effectively and wisely as a citizen, we have an obligation to ask: are we teaching these skills? Do we routinely require students to build their arguments on an analysis of the data relevant to an issue? Do we require them to create their own tables and graphs to support their arguments? Are we teaching our students how to get beyond the rhetoric surrounding important issues, how to see the underlying trends at work, and how to cut through the distractions of the often loud, heated debate?

If the answer to these questions is no, then we have work to do.

Yu-Chung Chang (Pasadena), “No Longer Lost in Translation: How Yu-Chung Helps Her Students Understand (and Love) Word Problems”

Yu-Chung says:

I started a faculty Inquiry Group (FIG) to investigate why so many math faculty find Intermediate Algebra onerous to teach.

The FIG discovered that…

1. Word problems are hard: Students avoid doing them and teachers struggle with teaching them

2. Too much to cover and too much overlapping review with Beginning Algebra

3. New concepts presented in the last chapters are rushed through and inadequately covered

4. It’s difficult to find time to show students real-word applicability.

Faculty inquiry provides instructors with an opportunity to come together on a regular basis to reflect, discuss, write, and research ways to help them learn how to help their students succeed.

Adapted from Yu-Chung Chang (Pasadena), “No Longer Lost in Translation: How Yu-Chung Helps Her Students Understand (and Love) Word Problems”

From Windows on Learning: Laura Graff, Dustin Culhan, and Felix Marhuenda-Donate, “Outlining Mathematics: Transforming Student Groaning into Student Learning”

I have always thought a large problem in math and science education is reading. Students are never taught how to read technical textbooks. I knew that somewhere along the way I had mastered this skill, but still could not identify precisely what the skill was. I attended [a workshop on reading offered through the Strategic Learning Initiative (WestEd)]….

During the week I learned that I am an “expert reader” in mathematics. This means I have the knowledge and history that allows me to read math. I was taught to participate in meta-cognitive exercises, exercises that forced me to think about my thinking while I read. I became supersaturated with tools and ideas. I was also cynical, for while all the ideas were great, I could not see incorporating them into my already full curriculum.

How would I incorporate the ideas from Reading Apprenticeship without compromising my class and homework time? I decided to assign the outlines to “at risk” students – those with scores below 75 percent.

I was at first amazed that the idea of outlines was considered innovative. However, as we used inquiry and analyzed student outcomes, we were amazed at the positive results. When we videotaped students this summer talking about the effect the outlines had on them, it was one of those huge “paydays,” where you realize you have made a difference in students’ lives and learning.

Adapted from Laura Graff, Dustin Culhan, and Felix Marhuenda-Donate, “Outlining Mathematics: Transforming Student Groaning into Student Learning”