Competency-Based Grading in Introductory Political Science Courses

Last night’s discussion on #fycchat was, appropriately for this time of year, about grading. I briefly mentioned, in response to a thread between Lee Skallerup and Jessica Nastral about grading anxiety, that I have experimented with a system of competency-based grading in my introductory political science courses. That generated some interest, so I’ll elaborate here.

The approach is an alternative to the traditional accumulation of points approach. That, it seems to me, assumes that the difference between an A student and a C student was knowing 20% more content. That, to me, never really made sense; it seems more like an A student should be able to do things a C student can’t. Accumulation of points reduces learning to mastery of the lowest levels of Bloom’s cognitive domain: knowledge and comprehension. Especially when I have more knowledge in my pocket than my college professors had in their heads collectively, learning should be about moving students to higher levels of the cognitive domain, teaching them to apply, analyze, synthesize, and evaluate.

The competency-based approach that I use starts from the course objectives rather than the assignments (which, if assignments are about assessing learning, every course should do). The objectives are framed in two dimensions: course content and course skills. The course skills are based on being able to use content at increasingly higher levels of Bloom’s cognitive domain. From my Introduction to Comparative Politics Syllabus:

Core Concepts. Upon successful completion of this course, the student will be able to demonstrate comprehension of and the ability to apply, analyze, and evaluate the following:

• The basic methods of reasoning and analysis in comparative politics
• The ideas through which comparative politics understands states, societies, and economies across regime types. 
• The practices and structures that differentiate democratic and non-democratic regimes. 
• The processes of political development and revolution.
• The politics and recent political history of one Arab country in the Middle East or North Africa. 

Course Competencies. Students who satisfactorily complete this course will demonstrate the following skills with regard to the core concepts studied:

1. Professionalism in the performance of their duties. 
2. Comprehension of the core concepts of the course. 
3. The ability to apply those core concepts such that they can understand, give explanations for, and develop responses to political practices, situations, and outcomes in national politics across different types of political systems. 
4. The ability to analyze, synthesize, and evaluate those core concepts, both in themselves and in practice, such that they can add original material to those concepts.

I then design assignments to assess a specific competency. Comprehension is assessed with simple, open-book reading quizzes; I’m not really testing whether the students know content off the top of their heads but whether they can understand content that they find in whatever resources they’re using. That especially makes sense in comparative politics; the odds that students will ever need to remember the legislative process in France are slim but the odds that they’ll need to look up the legislative process in a foreign country and understand what they find are more substantial. The other assignments test progressively more demanding competencies. All are graded on a satisfactory/unsatisfactory/failing basis.

Assignments. All students will complete the following assignments:

• Professionalism. Students must complete all assignments in good faith, on time, and in compliance with the ethical standards of scholarship in order to demonstrate mastery of competency 1. Submission of work that does not demonstrate a good faith effort to complete the assignment as required or that includes undocumented outside sources (whether or not in violation of academic conduct policies) in an assignment will constitute failure to demonstrate mastery of this competency. 

• Quizzes. For each unit of the course, there will be an online quiz of 10 questions. The quiz is based strictly on the readings for the course. It may be completed at any time before the date specified below, and is open-book. A satisfactory score is eight correct answers. Satisfactory completion of quizzes demonstrates mastery of competency 2. 

• Essay Exams. Students will complete two out-of-class essay exams. Each essay will require students to explain a concept studied in the course and apply that concept to explain or predict the outcome of a political case. The concept and the case will be defined in the question, and material about the case will accompany the question. A satisfactory essay will adequately explain the concept using course material and apply it to make an effective explanation of the case. A failing essay is one that does not reflect a good faith effort to complete the requirements of the assignment on time. Satisfactory completion of essays demonstrates mastery of competency 3. 

• Country Study. Students will complete one country study as part of the larger class project examining the Arab Spring. The project will require students to explain a concept studied in the course, identify a hypothesis following from the concept regarding how the Arab Spring would be expected to progress in their chosen country of expertise, and determine the extent to which the course of the Arab Spring in that country supports the hypothesis. The paper will require outside research. Satisfactory completion of the country study demonstrates mastery of competency 3. 

• Group Paper. Building on the country studies, students will, in groups, prepare a paper and class presentation developing a general theory explaining why the Arab Spring took different courses in different countries and testing that theory with respect to their countries of expertise. The paper is expected to be a single, coherent essay and not a collection of separate pieces. Students will receive a common grade for their entire group. Satisfactory completion of the group paper demonstrates mastery of competency 4. 

This translates into a straightforward grading system. Satisfactory completion of assignments allows a clear determination of achievement at each course competency. Each successively more demanding course competency is associated with a higher grade. Course grades thus indicate the ability to use the material in higher levels of the cognitive domain:

Course Grades. Grades will be assigned based on demonstrated mastery of competencies as follows: 

A. Student has demonstrated mastery of all competencies by, in addition to meeting all requirements for a B grade, receiving a satisfactory grade on the group paper and presentation. 

B. Student has demonstrated mastery of competencies 1-3 by, in addition to meeting all requirements for a C grade, receiving a satisfactory grade on both essay exams and the country study. 

C. Student has demonstrated mastery of competencies 1 and 2 by, in addition to meeting all requirements for a D grad, passing all quizzes. 

D. Student has demonstrated mastery of competency 1 and minimal mastery of competency 2 by passing six quizzes and receiving at least an unsatisfactory grade on all written assignments.

So far I’ve had positive feedback on this system, though I have no systematic evidence that students find it more useful. There is some confusion at first due to unfamiliarity, I think, but as students catch on they like the idea that their grades actually mean something concrete and don’t hinge on marginal differences in points. They’ve also said that they focus more on the big picture of both readings and assignments rather than on details that might shift their grade a few points. It does seem to me that students have done a better job of writing to the question rather than on the topic generally when I’ve used this; I don’t know if that’s because they are focused on the meeting the standard rather than maximizing points by putting everything they know about the topic on the page, but that seems a reasonable hypothesis.

Some students have said that they didn’t put in as much effort into assignments dealing with higher competencies because they’d be happy with an unsatisfactory score and a B or C in the course. But that’s fine with me. Students need to learn to prioritize their efforts, and that prioritizing comes with accepting less impressive outcomes on lower priorities. I think this helps them do that.

At the same time it makes my life easier when grading. I don’t need to think about whether an assignment is an 85 or an 88, only whether it constitutes a good-faith effort and meets the standard I defined in the syllabus. I can grade much faster that way (especially at the end of the term when students aren’t concerned with comments) and I can direct my comments to the interesting issues rather than to justifying every point not awarded. I also don’t have to worry about haggling for a couple of marginal points: if the assignment is unsatisfactory, there’s a pretty clear reason for it.

I also allow revision and resubmission of assignments. Partially that’s because every assignment would be make-or-break if I didn’t, but mostly because I believe that revision is the best tool for learning from an assignment. The competency-based system, with clear standards for each grade, makes that more workable. I can focus my detailed comments on the students who will actually put them to good use, discussing the assignments personally with the students who intend to revise and directing them to the standard rather than the minutiae. The revision process gives all students an incentive to take the comments seriously since they can make a major improvement in their grades on that assignment rather than making a marginal improvement or waiting for the next assignment and trying to generalize comments from previous work that they may not have really understood.

One thing that I think has to be done to make this work is to have rigorous standards. This could very much lend itself to grade inflation if your standard for an A is something that you think everyone should meet. I haven’t tried this in an upper-level course yet, but this would be especially so there. I could even be talked into dropping everything down a grade for courses at that level: comprehension alone is a D, application is necessary for a C, analysis and synthesis gets a B, and a serious critical evaluation of ideas is necessary for an A. That said, I think the points approach doesn’t avoid this problem; it only hides it behind the idea that each point not awarded is a deduction from 100% due to some problem, making mere satisfaction rather than excellence the standard.

Of course I only have locally-sourced, artisanal data anecdotal evidence that this works. I’d love to see others’ experiences with anything like this, and especially some actual research on it (though that presents a nightmare of a control problem, to be sure). I have heard of it being used in some other disciplines, primarily ones where there are relatively clear professional competencies such as education, accounting, or nursing. But I think this has good potential in the social sciences and humanities as well. Let me know if you attempt something like this.

Injustice In, Injustice Out: Social Privilege in the Creation of Data

This is an except from my upcoming paper "From Open Data to Data Justice," which I'll present at the Midwest Political Science Association Conference in Chicago on April 13. Please join me if you're there.

Update: The full paper is available at both the MPSA conference program link above (which will only be available to members after the conference) and my SSRN archive, which is publicly available and will have the most up-to-date version of the paper.

Data is not reality. It is rather a construct, an operationalization of an actor’s concept and reality, interpreting between the physical world and the intellectual structures by which actors understand that world. GIS shapefies, for instance, operationalize a relationship between physically existing land and legally existing property; interaction with a census taker operationalizes a relationship between bodies and citizens. A key implication of the constructivist understanding of data is that, for all of the celebration of (and weeping and gnashing of teeth over) the purported ubiquity of data collection and data as the “detritus” of human life in contemporary affluent societies, data does not, in fact, simply happen. The constructed nature of data makes it quite possible for injustices to be embedded in the data itself. Whether by design or as unintended consequences, the process of constructing data builds social values and patterns of privilege into the data. Where those values and privileges are unjust, the injustice is then a characteristic of the data itself; no amount of openness can remedy such injustices, just as no amount of statistical processing can undo inaccuracies in the original data. “Garbage in, garbage out” is a central concept in data ethics.

Datized moments occur most often in the interaction of an individual with a bureaucratic organization such as the state or a business. But people and groups differ in their propensity to interact with such organizations. This difference provides an important point by which privilege can enter into data. Data over-represents some, and where those over-representations parallel existing structures of social privilege, it over-represents those already privileged and under-represents those less likely to be part of data producing interactions.

Interactions with the state are rife with disparities that reflect social privilege. One well-studied example is the undercount of the decennial United States Census. Since the problem of undercounting was first quantified in the mid-Twentieth Century, black and Hispanic households have been undercounted at higher rates than non-black households. The causes of this undercount are myriad:

Households are not missed in the census because they are black or Hispanic. They are missed where the Census Bureau’s address file has errors; where the household is made up of unrelated persons; where household members are seldom at home; where there is a low sense of civic responsibility and perhaps an active distrust of the government; where occupants have lived but a short time and will move again; where English is not spoken; where community ties are not strong. (Prewitt 2010, 245)

Two commonalities in these explanations are striking: the extent to which these causes are barriers to interaction with census takers, and the extent to which they are correlated with racial and class privilege. The latter causes the undercount to disproportionately affect disadvantaged groups (hence, Prewitt argues, the focus on race in debates over census methodology between 1980 and 2000), while the former prevents those groups from being represented accurately in Census data. Similar problems exist in collecting any data on groups such as the homeless. Groups might also be disproportionately willing to participate in some interactions over others, such as differences in thresholds for reporting building code violations between the affluent and poor.

Such privileges are not confined to interactions with the state. Residential segregation especially is often tied to forms of institutional discrimination that would influence how often individuals interact with bureaucracies. Zenk et al. (2005) found that low-income, predominantly African American neighborhoods in Detroit were, on average, 1.1 miles further from a supermarket than predominantly white neighborhoods with similar incomes, with consequently increased dependence on smaller food stores such as convenience stores or groceries. Similarly, Cohen-Cole (2011) argues that consumer credit discrimination based on the racial composition of applicants’ neighborhoods is linked to increased use of payday loans. In both cases, the use of less bureaucratized businesses by groups already suffering from discrimination in the form of de facto residential segregation (either as the legacy of formal segregation or because of ongoing discrimination) results in data that is statistically biased against such populations and reinforces whites’ privileged position. Businesses can analyze the needs of the (disproportionately white) customers with whom they interact and adapt accordingly; benefits thus accrue to the beneficiaries of social privilege.

Transforming information about a datized moment into data is equally problematic. Only some of the information about that moment will be datized. What information that will be is not a natural consequence of the interaction but a design choice on the part of the data architects that reflects their purposes, resources, and values. An institutional survey director noted to me that survey data at the institution is subject to state open records laws and sometimes requested by the public and state legislators. As a result, the survey director encouraged the practice of not collecting data that the institution would not be comfortable making public.# In this case the concern was privacy, but this reasoning is at least as likely when more self-interested motives are present. Regardless of the motivation, though, such decisions are value-laden; thus the data built on such decisions will embody those values and transmit them in the process of using the resulting data.

Less conscious assumptions such as those part of worldviews shaped by social privilege will also shape such decisions, and likely be less amenable to challenge to the extent of their invisibility to lack of diversity among the data collectors. Higher education “net price calculators” are a case in point. Such tools, which the federal government requires all institutions receiving Title IV aid to produce, are designed to help students and their families estimate the likely cost of attending an institution given the prevalence of “high-tuition, high-aid” business models. This assumes that the net price is what is important to students. But Sara Goldrick-Rab  argues that the gap in applications to elite colleges between high-achieving, high-income and high-achieving, low-income students reported by Hoxby and Avery is rooted in “sticker shock” at the high gross price of such institutions among low-income families in spite of the institutions’ often much lower net prices. Their disregard of net price is in part a lack of information, but more significantly a consequence of such families’ lack of trust in institutions generally and substantially higher risk to such families if educational institutions fail to maintain the initial promises of aid, conditions that make the net price of the institutions less credible: “Being told that a college is likely to give you aid is not the same thing as getting the aid, [emphasis in original]” she writes. Such students choose to apply at less expensive (and consequently less selective) institutions as they present less risk to themselves and their families.

If Goldrick-Rab is correct, the credibility that the middle class finds in state and social institutions that have generally protected their interests should be seen as underlying the decision to collect and report average aid amounts that do not vary my income: middle class families can credibly take average aid as typical of people like them; low-income families cannot. One might expect the same to be true of first-generation students. With family members unfamiliar with the operations of universities, they will often be unaware of issues such as net price or even understand the financial aid process at all. Yet this background knowledge, like the credibility of a measure, is assumed in the selection of data to be collected. Those privileged with such knowledge find their privileges reinforced by this data; those who are not so privileged are further disadvantaged when they cannot see the data as meaningful.

Adding to this the question of how that information is stored increases the complexity of the issue. Key features in the problematic Bhoomi experience with open data were not only the selection of only certain types of documentation for inclusion in the land title data but also the decision to store the resulting data in a relational database system. These aspects of the system design effectively precluded informal knowledge from being part of the open data system; such knowledge, which was the basis of the existing land claims of Dalits, cannot be queried by the systems used to . The two features both inform and reinforce each other: excluding narratives and other unstructured data obviates the need for systems that can handle unstructured data such as those using text-analytics or Unstructured Information Management Architecture (UIMA), while the choice of a relational database precludes the use of narrative information. Donovan (2012) cites this as an instance of James Scott’s (1998) “seeing like a state” in which the local government sought to simplify society by making it legible. The open data system incorporated this value in its choice of what to datize about the moment in which land was transferred. This incorporated a value structure into the data, one that is clearly not neutral in the competition for power.

Because of the myriad ways that social privilege can become embedded in data sets, open data cannot be expected to universally promote justice. It can just as easily marginalize groups that are not part of the data: people whose lack of privilege excludes them from the kinds of interactions that produce data and makes their viewpoints invisible to those who collect data. Opening datasets composed of such data simply propagates the injustices that came into the data as it was collected. Whatever steps are taken to promote fairness in using data that is at its root unjust, the result will almost inevitably be unjust as well. Data is very much a case of “Injustice in, injustice out.”

Higher Ed and the State of the Union, Part I: "The Soaring Cost of Higher Education"

In last night's State of the Union Address, higher education policy was, shall we say, not a featured player. A whole 177 words were devoted to the subject, taking up about 90 seconds in a speech almost exactly one hour. Here they are, in their entirety (though if you can read more than two words per second, you might just skip to the transcript below):

Now, even with better high schools, most young people will need some higher education. It’s a simple fact: the more education you have, the more likely you are to have a job and work your way into the middle class. But today, skyrocketing costs price way too many young people out of a higher education, or saddle them with unsustainable debt.

Through tax credits, grants, and better loans, we have made college more affordable for millions of students and families over the last few years. But taxpayers cannot continue to subsidize the soaring cost of higher education. Colleges must do their part to keep costs down, and it’s our job to make sure they do. Tonight, I ask Congress to change the Higher Education Act, so that affordability and value are included in determining which colleges receive certain types of federal aid. And tomorrow, my Administration will release a new “College Scorecard” that parents and students can use to compare schools based on a simple criteria: where you can get the most bang for your educational buck.

The ideas presented there were fleshed out somewhat by a document released by the White House accompanying the speech. In it, the President proposes changes especially in accreditation: 

The President will call on Congress to consider value, affordability, and student outcomes in making determinations about which colleges and universities receive access to federal student aid, either by incorporating measures of value and affordability into the existing accreditation system; or by establishing a new, alternative system of accreditation that would provide pathways for higher education models and colleges to receive federal student aid based on performance and results.

Some education policy folks have been ecstatic about the proposals. Kevin Carey, director of the Education Policy Program at the New America Foundation, called this "the biggest change to federal higher education policy since at least the Higher Education Amendments of 1972" because of how it could reshape the relationship between regional accreditation and financial aid.

But don't count me among them. Nothing in the speech last night makes me particularly happy. Over the next few days I'll tackle the three parts of the policy proposal: the perception that the costs of higher education are out of control, the idea that the ability to offer aid should be rooted in value and affordability, and the College Scorecard.

"Skyrocketing Costs"

Faithful readers of this blog (thanks, Mom) have heard my rants thoughts on this before. The "skyrocketing costs" trope of contemporary higher education policy continues to confuse the cost of educating students with the cost charged to students. The fact is that expenditures by state universities are relatively stable. At UVU, as I detailed the first time I hashed this out, real cost per student has actually declined since the 1990s. The tuition increases are making up for drastic cuts in funding from state legislatures.Colleges are already doing their part and more; UVU's tuition increases have actually been much less than the lost state funding. The costs of education thus aren't increasing, but they are being shifted from state taxpayers to students, and as the increased cost of attendance increases financial aid awards, to federal taxpayers.

State legislators a playing a very rational game. As long as the state taxpayers don't connect both federal spending increases to tuition increases and then tuition increases to state funding cuts, and as long as both the public and the media blame university administrations for those increases, legislators can take credit for cutting taxes while avoiding blame for increasing costs to students and their families.

There really isn't much more fat to cut. Exorbitant administrator salaries? Find someone in the private sector willing to manage the 300 employees that a dean might have for a few ticks below $100,000 per year. Coddled benefits? That security, and my belief in education, is why I work for less than half of a private-sector business intelligence analyst; if you want me to take private-sector benefits I'm going to expect a private-sector salary.

Make faculty work year-round? "Summer vacation" is, for higher education, simply free labor: You don't get paid, but you get fired if you don't publish and summer is the only time you can. Make them teach more than "12 hours per week"? You're confusing credit hours with labor hours; it usually takes me two hours outside of class to prepare for a one-hour lecture/discussion. More adjuncts? We're already above half of our student credit hours. MOOCs? You won't get more graduates with course completion rates of 5%.

What more do you want us to do, Mr. President?

The Next Installment: "Value and Affordability"

Opening Access to AIR's Research Publications

Many of you will have heard, by now, of the death of Aaron Swartz. He was, by all accounts, a technically brilliant man passionate about not just the technical but also the social aspects of the Internet. A firm believer in openness on the Internet, his prosecution was for an act of civil disobedience toward the American intellectual property regime: mass downloading of academic research from JSTOR. Swartz' death is thus leading many to demand that the journals through which we communicate abandon paywalls and adopt open-access policies.

Count me among them: Research in Higher Education, the research journal of the Association for Institutional Research, should adopt an open access model.

Data Mining Ethics at the RMAIR Conference

I had the pleasure of presenting my paper on ethics and data mining at the Rocky Mountain Association for Institutional Research Conference today. First off, my thanks go out to the conference organizers for putting on an excellent conference. And then my thanks go to all of the people who had kind words and/or challenging questions about it.

The paper looks at the ethical side of a growing force in institutional research and higher education management. Data mining and predictive analytics are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

I'll be posting highlights of the paper in blog-sized chunks over the next week or two. For those who can't wait, the full paper is posted at SSRN with the rest of my papers, and the PowerPoint presentation is available through Google Docs (update: it turns out Google Drive doesn't support animations) YouTube If I get really ambitious I'll record the narrations from to the slides and you can get, essentially, the whole presentation.

Getting Students to Understand Workload

Last week, Jill Rooney published an excellent post on questions students should never ask. She duly noted the general principle that there is no such thing as a stupid question, a principle that, having worked at Disneyland while an undergraduate, I know to be false: "What time is the nine o'clock parade?" is a stupid question. The rest, however, is excellent, mainly because the questions boil down to questions that students should ask themselves.

Today she followed that up with an equally useful post on questions students should ask. It includes a question that I pose to all of my students at the beginning of the semester:

@jillrooney2 Single best question: do they have 135 hours to put into this course over the next semester? So few understand that.
— Jeffrey Alan Johnson (@the_other_jeff) August 27, 2012

Students frequently don't quite get the time required for a course; the refrain of "this isn't the only class I'm taking" is all too common. To help them understand that, I work out a detailed time budget for the course, using the three-to-one rule of thumb. That's 135 hours total for a standard three-credit course at my institution. For my Introduction to Comparative Politics course this fall, the budget looks like this:

	Number	Time Each	Total Time
Class Meetings	28	1.5	42
Final Exam Meeting	1	2	2
Chapters	9	1	9
Additional Readings	9	0.5	4.5
Lynch Reading	1	12	12
Current Events	16	1	16
Quizzes	9	0.5	4.5
Exams	2	2	4
Country Study	1	20	20
Group Paper	1	20	20
Office Hours	2	0.5	1

That forms the basis for a section on my syllabus:

Workload

According to the accreditation standards that validate your degree as a legitimate one, to receive three semester credit hours requires 135 hours of study, including not more than 45 hours in class. In this course, study hours are budgeted as follows: Class meetings (44 hours), Readings (41.5 hours), Assignments (48.5 hours), Office Hour Consultations (1 hour).

When I go over the syllabus, I make clear that 135 hours is a non-negotiable standard, and students should expect to do poorly if they can't put in the required time. If their time commitments don't permit that, I suggest that they should consider cutting back on either their commitments or their expectations for performance.

Since I started doing that (back when I was a full-time faculty member rather than a bean counter—but they're such glorious beans that tell us so much!) I found that  I receive fewer complaints about the workload, and when I did they were specific things that I needed to respond to, e.g., "We can't get this assignment done in the time you think it will take us." That's especially good to me, as I tend to think that most students who complain about seemingly trivial things usually either have a legitimate concern but can't articulate it in relation to legitimate standards, or they have unrealistic expectations. This solves both of those concerns.

Ted Striker, African Gods, and Charles Peirce.

A friend reminded me of this, from my dissertation. I thought I'd share.

The problem at the heart of The Gods Must be Crazy is that the interpretants of the pilot and the Bushman are so fundamentally different that the bottle ceases to exist in a way that is remotely recognizable to someone from, literally in this case, a “Coca-cola” culture.  How, then, can the interpretant be real?  In fact, it is the reality of interpretant that creates the situation in the first place.  The fall of the bottle from the airplane demonstrates this.  We can see several interpretants at work for the pilot: soft drinks, garbage, the emptiness of the Kalahari.  If these are different—if the soft drink is not fully consumed, if the pilot thinks in terms of recycling rather than waste, if the pilot recognizes that the Kalahari is not so empty that the bottle could not possible hit anyone—the bottle is no longer something to be thrown out of the window.

The same is true for the Bushman.  In this case, we see the gods, the various activities for which the bottle can be used, communal order, and the mythological geography of the clan creating the bottle as it is used.  The Bushman’s belief in the existence of anthropomorphic gods who live in the sky leads him to believe that anything falling from the sky must have been given to him by the gods.  The belief that these gods have human-like emotions and motivations leads first to the sense that the bottle is a gift when it is brought into relationship with the everyday activities and then that it is a trick when it upsets social order within the clan.  The clan’s mythological geography—the belief in the “end of the Earth”—sparks the odyssey to permanently dispose of the bottle.  Like sign and object, interpretant displays its reality causally.  It causes something to be different than it might have been.  In so doing, it links the sign-object to other signs and objects.

But interpretant does more than this; it also links sign and object to each other.  When we ask the meaning of the sign “bottle” or “television” we can answer in two ways.  One is with description.  We explain what a bottle looks like, feels like, sounds like, and perhaps tastes and smells like.  We thus make reference to object.  Similarly, when we ask what an object is, we also answer in two ways that correspond to the question of a sign.  Analogous to the descriptive reference to the object is the nominative reference to sign: that object is a bottle or a television.  But neither of these ways fully satisfies us, much like the running joke in the film Airplane!:

“I have a message from headquarters.”
“Headquarters?  What is it?”
“It’s a big building where generals meet, but that’s not important right now.”

The joke is repeated several times: getting to a hospital (“a big building with patients in it”), helping out in the cockpit (“the small room at the front of the plane where the pilots sit”).  It is funny because the response doesn’t answer what the character is asking about—what any person would mean when they ask the question.  The character wants to know about the practices that relate to the sign, not about the object to which the sign corresponds.  They want to know not what objects the signs “headquarters,” “hospital,” and “cockpit” refer to, but what practices went on in these places that are relevant at the moment.  The character is asking about Thirdness.  Ted Striker wants to know what happened at headquarters, or what kind of help is needed in the cockpit; Captain Oveur wants to know why the passengers need to be brought to the hospital.  We can thus describe both sign and object in terms of a common interpretant, which unifies the sign and object.

This is the key function of interpretant.  In unifying sign and object through practice, interpretant unifies the natural and social aspects of reality in a single entity.  Peirceian semiotics, while idealist in that it posits the reality of ideal constructs, does not simply degenerate into an anything goes philosophy, one where the content of one’s own mind has no bounds but those of imagination, and in which unicorns and wormholes can be said to exist in exactly the same sense as a the computer on which I am typing.   The ideal is inexorably linked to the material in the sign-object-interpretant relationship.  Yet Peirce also shows that there can be no object without a sign, even if the sign is “that thing we just found.”  The reality of the object is linked just as inexorably to sign through interpretant.   Interpretant—which we should think of as sign and object in practice—thus serves to link the natural (object) and the social (the signs that we create for these objects) into a single fabric of reality.