Understanding Teaching with Mike Rose

Note: This is a post originally written for an internal Macmillan Education blog on December 7, 2013

More and more the work we do in higher education publishing necessarily and inevitably includes professional development for the teachers who will use our books and media. The business rationale for this is simple: if teachers understand the value and support and insight our stuff brings to them and their students, they'll use it more, require it, depend upon it, and students will then be required to log into our software and services, required to do some of their learning with us, required to buy, open, study from our books. Essential books and software equals profit. Simple. But even more important our mission as educational publishers is to foster better teaching and learning because the world requires good teaching and a humane and educated people.  By all of us understanding teaching and learning, and then applying that understanding on campus from the first sales call to ongoing training and professional development for adopting professors, we enact the better natures of books and media. We become what we promise professors can be.

Mike Rose, who has three books, two as co-editor, in Bedford/St. Martin's professional resource series -- An Open Language is the one solo authored and is a collection of his essays --  has the first of three posts in the Washington Post Online that looks at the complexities of teaching here: http://t.co/btzId4s2sq. He's posting as a guest blogger in Valerie Strauss's The Answer Sheet, a blog that covers education.  Here's how Strauss sets up Mike's work:

Here is a thoughtful piece on the essence of teaching and the kind of teacher education programs we really need from Mike Rose, who is on the faculty of the UCLA Graduate School of Education and Information Studies and author of “Back to School: Why Everyone Deserves a Second Chance at Education.”  This is longer than your average blog post but well worth the time, and is the first of three pieces on teacher education by Rose.

Those who know Mike won't be surprised by the quality and kindness of thought. Here is but one excerpt  that resonates for me, in main because it simply reminds me of my wife, Barbara, a brilliant fourth grade teacher whose job teaching fourth grade is so much more complicated by all kinds of factors and needs than my occasional job teaching college writing courses.

Teaching done well is complex intellectual work, and this is so in the primary grades as well as Advanced Placement physics. Teaching begins with knowledge: of subject matter, of instructional materials and technologies, of cognitive and social development. But it’s not just that teachers know things. Teaching is using knowledge to foster the growth of others. This takes us to the heart of what teaching is, and why defining it primarily as a craft, or a knowledge profession, or any other stock category is inadequate. I’m not sure there is any other work quite like it.

The teacher sets out to explain what a protein or metaphor is, or how to balance the terms in an algebraic equation, or the sociological dynamics of prejudice, but to do so needs to be thinking about how to explain these things: what illustrations, what analogies, what alternative explanations when the first one fails? This instruction is done not only to convey particular knowledge about metaphors or algebraic equations, but also to get students to understand and think about these topics. This involves hefty cognitive activity, as any parent knows from his or her experiences of explaining things to kids, but the teacher is doing it with a room full of young people—which brings a significant performative dimension to the task.

Thus teaching is a deeply social and emotional activity. You have to know your students and be able to read them quickly, and from that reading make decisions to slow down or speed up, stay with a point or return to it later, connect one student’s comment to another's. Simultaneously, you are assessing on the fly Susie’s silence, Pedro’s slump, Janelle’s uncharacteristic aggressiveness. Students are, to varying degrees, also learning from each other, learning all kinds of things, from how to carry oneself to how to multiply mixed numbers. How teachers draw on this dynamic interaction varies depending on their personal style, the way they organize their rooms, and so on—but it is an ever-present part of the work they do.

The full piece by Mike, which goes on to look at the role of college teacher education programs and the balance of classroom preparation for teaching to on the job experience, reminds those of us who do professional development that when we work with educators we are teachers and those educators are students. And what matters so much in our work then is following good pedagogy, applying the things our books suggest. So a workshop might open with a prompt or question and then asking people to write a few minutes on it before discussion, a simple writing to learn activity that helps focus the mind on the topic, gives people a private place to think though they're in a public space, assures that there will be things to be said when one asks for responses, and helps support learner to learner discussion. But what we also need to remember is how people learn, the role of anxiety and aspiration. When we do workshops, our rooms are full of teachers who will have, to borrow from Mike, silences, slumps, aggressiveness, and more.

To understand professional development then, to understand training, we need to understand that we are educators and that what we are doing is as important and complex and demanding and necessary as the work the teachers we are supporting will go forth and do in their classrooms.

Draft --> Writing Software with Analytics for Writers

Another item originally written for folks at Macmillan Education.

Traci Gardner, who contributes to Bits, a Bedford/St. Martin's blog for teachers at http://bedfordstmartins.com/bits, tweeted a link to a ProfHacker entry by Konrad Lawson about Draft, a tool for collaborative writing, at "http://chronicle.com/blogs/profhacker/draft-for-collaborative-writing/49427."  Lawson observes,

Draft is designed for drafting and collaborative writing of text. It is not a blogging platform or a live editing environment like Google Docs, and the documents created within Draft are not designed to have their final home there. As it functions now, you write, collaborate, edit, and then export or directly publish the documents to your cloud hosting service such as Dropbox, Evernote, or Google Drive, to a social platform such as Blogger, WordPress, Tumblr, and Twitter or, if you use the Chrome or Firefox additions, directly back into a text box in another browser tab. Get a quick overview of its features here: https://draftin.com/features .

I want to draw attention here to the analytics (https://draftin.com/features#analytics) features. Especially this idea, described by Draft's creator, Nathan Kontny:

One mistake I keep seeing people make, when they publish their writing, is that they don't pay enough attention to attributes that might affect how much traction that writing will get.  They'll publish 2000 word posts, when their audience would prefer 500. Or they publish on Friday night, when no one might be paying attention and Monday morning might be a better idea. I wanted to make this type of analysis a lot easier to understand, and help people, including myself, learn what makes our writing get more attention than other writing.

This is the kind of thing -- finding ways to give writers data they can use to make decisions about their writing -- that really allows the writer to adapt; it gives them agency. That's a distinct from the kind of adaptive learning that pushes a learner to one desired end, a preset end, and where the software varies content and activities based on performance for the learner to do until they reach the end, where the software is adapting, not the learner.

Note the kinds of things Kontny's trying to help with -- audience awareness, the ability for writers to see differences in drafts. A lot of online writing tools (http://grammarly.com, for example) can give statistics on word count, or in the case of Grammarly, the number of sentence level errors made. And there's some use in that some times, but giving writers information on what in their writing finds an audience, when it's best to post, what the reading level of their writing is (Draft uses the Flesch reading level.), and other information lets a writer see things over time. This intranet does some of that, by the way; it reports (click the "Content" tab and then filter to see your own content) what of your contributions have been viewed and how many times, commented on, liked, replied to. That's standard stuff in social networks and should be standard stuff in the learning spaces we make for students. What would up the value on that would be aggregation for totals of all those items, and a view that shows trends over time.

The other feature of Draft is the ability for a writer to compare drafts side by side, whether one's own writing or writing being co-edited by fellow writers offers a powerful tool as well. Teachers struggle to get writers to address global revision, what is sometimes called higher order revision. Students tend to focus on tweaking sentences here or there. So a tool like Grammarly or Word's spell checker/grammar checker draw attention to surface level issues. But a tool like Draft shows changes. That's the kind of thing we need in the writing tools we make for students and teachers. There needs to be a drafting tool that supports version control, to encourage writers to save as or upload drafts. And a tool that not only lets writers compare drafts, but that highlights changes. And not only highlighting changes, but it should also tell writers what percentage of the draft is changed: how many new words were added, how many new sentences were created, new paragraphs, how many deletions were made. The idea would be a tool that can summarize larger revision from minor editing. And then something that aggregates that so over assignments and drafts, a writer sees trends.

Giving writers and writing teachers these views -- evidenced based insights into writing and revision processes -- gives them tools for articulating change and describing growth. A teacher can ask for a new draft and can tell the writer that she wants to see at least a 25% increase in new ideas. Peer reviewers can return to see which of their feedback ideas lead to bigger revisions by the writer, which would help reviewers see the value of their work and why it matters.

A Look at the Automated Analysis of Constructed Responses, Or Why Jared is Thin

Note: Like the post on Art Graesser's work, this entry comes from notes taken from a meeting with innovative professors doing great research on using automated response to writing, in this case, writing to learn. One of the privileges of being in publishing is meeting professors with great ideas.


So: Jared, the Subway guy, lost a lot of weight. Where did the weight go?

That's the kind of question that requires biology students to NOT simply recall key concepts, but asks them to explain a process, and unlike a multiple choice question, it does not allow the student to look at choices for hints. It asks them to construct a response that answers the question. So, where did Jared's weight go?  I won't tell you the answer, but it was the illustrative question used by to explore "How Can Automated Assessment of Constructed Responses (AACR) Provide Automatic Evaluation of Written Formative Assessment in LaunchPad?"

The presenters were (with descriptions pulled from http://create4stem.msu.edu/project/aacr:
Mark Urban-Lurain, Associate Professor and Associate Director of the Center for Engineering Education in the College of Engineering, Michigan State University directs the technology development and implementation of AACR.

John Merrill, Director of the Biological Sciences Program, Michigan State U, lead the development of the core biology curriculum, and provides disciplinary expertise in the biology portions of the work plan, coordinating with faculty who teach introductory biology courses to implement the materials in their courses.

John began by stating the essential problem: multiple choice questions are not adequate measures of what students know; writing -- in this project, short answer (from one - 40 words or so) reveals better student understanding (or misunderstanding). However, in large lecture courses, sometimes with up to four or five hundred students, even just skimming, let alone reading, sorting, scoring and getting a composite understanding of what students know doesn't work. So John and Mark embarked on a project to have students write, and then use machine analysis to not only score the writing, but sort it by categories and from those categories reveal where students have misconceptions or misunderstandings, giving instructors two things: a better guage of student learning and the means to see broad trends and thus to adjust their teaching to what students need clarification and help with. Currently AACR is an instructor tool: instructors download questions that AACR can score, sort, and report on, instructors deliver the questions to students, extract their answers as a spreadsheet, upload that spreadsheet to AACR, and the AACR team runs the answers and delivers to the instructor a report.  Cumbersome yes, but the team has won a $6 million NSF grant to both improve how AACR works and -- this is key to the mission -- create a means for faculty development, with advice provided to and from faculty communities of users on what kinds of changes to make to their teaching in response to the data AACR provides.

Attending the presentation also were James Morriss, first listed author of Biology: How Life Works (http://www.macmillanhighered.com/Catalog/product/biologyhowlifeworks-firstedition-morris) and Melissa Michael, the lead assessment author on the book. Both James and Melissa concurred on the power of writing to foster learning and to reveal better than multiple choice questions what students know and do not.  James told a story about how he sometimes use both a multiple choice question and a short answer question on the same test, and students will have wildly different takes, getting more right on MC questions but then seeing from written responses that students don't really know the subject matter or at the very least cannot articulate it on their own. An MC question might have language the triggers recall of lecture notes or textbook language, but when asked to write, and thus required to cull that out on their own, things fall apart.

So as to writing. Note the acronym AACR and the phrase "constructed responses."  While we might use the term open ended responses from our use of surveys or assessments we're used to designing, Merrill and Urban-Lurain used "constructed responses" to emphasize two things: first, students have to 'construct' a response, but the questions used are meant to be open ended, as you see in a survey with things like "other" or "tell us more." Instead the questions seek specific responses to key concepts in the course; and second, when fully considered as a concept in learning, a constructed response might not be text only (though AACR is for text only written response) but can include the use of images, artifacts, data, tables and so on.

Earlier I posted on the meeting we had w/ Art Grasser and his use of Latent Semantic Analysis. The AACR project doesn't use that approach. Instead, they automate responses by doing a word analysis -- the presence of key words in students answer, nouns -- of students answers and matching the prevalence of those words to prior student answers. The machine is trained to score student writing and to categorize answers according to core ideas in -- this demo -- biology, so that teachers can see which students are using language that correlates with understanding and which are not, and where students are not, where they are misunderstanding things. The design of the software uses an program called SPSS (http://www.ibm.com/software/analytics/spss/), statistical analytic and predictive software now owned by IBM. They chose it in part because it's designed for non specialists to use but is also robust enough for their purposes. They applied NodeXL, a program that creates associational graphs of Excel data to produce concept clusters and association graphs (So a word used a lot has a bigger ball, and a word it appears with a lot in student answers has a thicker line to that word's ball. Go to http://www.nodexlgraphgallery.org/Pages/Default.aspx to see NodeXL graph images to get a sense). The images you see at NodeXL are more complex than an example from a single question in AACR, and so the data is easier to read from the graph, but John Merill noted that even so, and even for science professors, teaching instructors how to read the graph, understand what it means about student learning, and then coming up with a response in their teaching to address what they see is necessary.

Here's my understanding of how things worked:

SPSS was fed WordNet, an open source dictionary funded by the National Science Foundation (http://wordnet.princeton.edu/). Wordnet links/associate words not just by meaning, like a thesaurus, but concepts and varied meetings, so a richer lexical matrix. Merrill and Urban-Lurain also added to the dictionary terms and meanings particular to biology, so that additional and occasional specialized language or specialized meanings of common language (such as 'mean' in statistics) that students might use in their answers was accommodated.

As student answers were added, SPSS produced a report that pulled and analyzed key works -- nouns -- in student answers and did a first pass association at suggesting categories/concepts for those words. Merrill and his team of biology professors then fine-tuned that, correcting and fine-tuning the categories and which words should be associated them. Categories were then associated with concepts key to the course.  So for example, on where Jared's weight went, 37.3% of answers were associated correctly that the reasons were cellular (metabolic rate, calorie burn, and weight leaves as CO2) and another 37% said they had to do with physiological actions (sweat, urine, feces and other means of departure). Guess which is right, or rather, where the student answers should be clustering? With the results, at a glance and computed quickly and more accurately than a professor could from reading and categorizing and counting answers by category, a teacher sees whether students have the key concepts at stake down, and with greater accuracy even than a easier to auto score multiple choice question. Very powerful stuff information.

Questions were drafted and student responses were uploaded. In the sample question we studied, 374 student responses were gathered and two things happened: one human readers applied a rubric and scored those, and then, the answers, categories and concepts were tweaked so that SPSS could give a predictive score -- note the word predictive -- that says, essentially, based on the vocabulary we see, we the software predicts a human reader, reading the full answer, would give the answer score X. Overtime that prediction matched human scorers in the 83% or higher range for high and low scores (of three levels the humans used) but matched 43% on mid-range scores (where humans also show widest variance.

The labor intensity is in question authoring, adding vocabulary (though both of these would subside if more instructors used the program and added stuff, one of the goals of the NSF grant, a kind of crowd sourcing to get more questions in. The labor comes in establishing predictive outcomes from SPSS that matches the scores or normed human graders (humans trained to apply a rubric consistently, so readers get the same, or nearly the same [depends on the rubric] score on a given sample of student constructed response. It took 374 items scored by humans, for example, to account for the range of responses and lexical variation of student work. That's a lot of norming for one question. Multiple that by just five per chapter to go with a book, and one can see a tremendously labor intensive process.

But that said, the pedagogical benefits and outcomes possible, and the ability to perhaps adapt the machine to not only score and give a report to an instructor, but to also give information directly back to students, adaptive information (so imagine a LearningCurve made of "constructed responses" instead of just multiple choice questions, and you see can see where this might go.

Right now the technology is young, despite ten years of research, and the NSF grant is only 6 months or so (out of five years) in. So there's time to see where this goes and maybe experiments the biology team can try. My own concern in the humanities, where our textbooks, which are the point of sale to pedagogy, are lower, significantly lower, and so the labor to build the questions that the current methodology uses would be something that we couldn't afford. But boy, they got a lot right and it'll be cool to see where this goes and whether biology and other science books in MHE can do experiments.

Art Graesser and MITSC

A colleague at Macmillan Education arranged a meeting in New York for editors to meet with Art Graesser, a Psychology professor at the University of Memphis who researches and designs in the Memphis Intelligent Tutoring Systems Center (MITSC), which is part of the Advanced Distributed Learning Center for Intelligent Tutoring Systems Research and Development (ADL-CITSRD), a government partnership, which is located in the FedEx Institute of Technology (FIT, and yes, there will be an acronym test at the end of this post; it is 50% of your grade.)  The purpose of the meeting was to learn about different approaches to automated writing assessment.

On the way to discussing automated assessment of writing, Art described some other projects from MITSC:

AutoTutor (http://www.memphis.edu/mitsc/capabilities/team-memphis-projects/autotutor/index.php), where, as a student works at a self-paced tutorial, two "agents," software coded to track what the student is doing (or not doing), are triggered by student actions in the tutorial. So a student might make a mistake in identifying a key idea, and the first agent, might trigger a text or audio message asking the student a question. The second agent might comment on the first agent's question or on the student response, creating a kind of learning dialog among the two agents and the student about the item under study. The Turing Test becomes Turing Tutors. Now, if this sounds bizarre, wait: the research from Art and his team shows that students who study in the tutoring software do slightly better on shallow knowledge (recall, definition, summary) of content than students who read only, but do significantly better at deeper knowledge (reasoning, synthesis, and communication) than students who read only. The acts of dialog, of drawing student attention to thinking in new ways, of answer or at least considering questions the agents posed, leads to deeper learning.

That's not surprising on the face of it, but what's powerful is the creation of software that helps a lone learner come to the kind of deeper engagement necessary for deeper learning.

And that's the nub of Art's work -- deeper learning through deeper engagement via dialog and writing.

Art and his colleagues and Memphis are also doing research with the Center for the Study of Adult Literacy (CSAL) at Georgia State University, which, like MITSC, has won grants from the Institute for Education Sciences (IES), a federal initiative that studies learning sciences. I'm linking to both CSAL and IES because they're sites worth visiting, doing a lot of useful research that we can draw on for validation and direction of editorial initiatives.

On writing, one of the first things you'll want to check out is the work MITSC has been doing with cohesion metrics of written text. To quote from the project, it involves the creation of a tool that generates "Automated Cohesion and Coherence Scores to Predict Text Readability and Facilitate Comprehension," affectionately dubbed, and this will be on the test so pay attention, Coh-Metrix. To put this in simpler terms, Coh-Metrix measure readability, only in ways more sophisticated than Lexile, Gunning-Fog, and perhaps best known (because it's built into MS Word) Flesch-Kincaid. You can get a good explanation of what Coh-Metrix measures here -- http://cohmetrix.memphis.edu/cohmetrixpr/cohmetrix3.html -- but what you might want most to do is to go to their Text Easability Assessor (TEA) --http://tea.cohmetrix.com/ -- create an account and have a TEA party* with some prose of your own.

So. On to auto assessing writing. In the discussion, Art described three broad ways to auto assess text:

1. Compare the text to an ideal and score it for how close it comes --- We can do this crudely already with one word answers for example. A student writes a word into an answer, and it matches the word (correctly spelled in our limited engine so it fully compares) we've designated as correct, the student gets full points for the question. The software used in automated assessment allows answers more sophisticated than a single, correctly written word and more nuanced scoring than right or wrong.
2. Using a cluster of answers and mapping to them. That is, instead of comparing to a single idea, a range of responses -- A, B, C, and D answers might be available and student submissions are compared for features that match somewhere in the cluster. So if the writing has features associated with an A -- vocabulary, length, and other measures -- the writing is scored an A and so on.
3. C-Rater level (C-Rater is an ETS tool that we see in use in writing courses as Criterion). Here, the software is trained to prompts, and a corpus of sample student writing in response to those prompts. The prompts are designed so that submissions will fall into the range of samples given (so a bit of 1 and 2 above happens), but in addition to using that corpus as a tool and way to do the analysis, C-Rater also uses Latent Semantic Analysis, a means of analyzing the text submitted in more sophisticated ways.

The psychology team and the biology team have done experiments with MITSC using the Latent Symantic Analysis (LSA) engine they've designed. The process went something like this:

1. A textbook was turned over to MITSC as .txt files.
2. Those files were scanned and described for the latent semantic features using the engine.
3. Wikipedia's entries on psychology were also scanned, to extend the corpus and to provide a richer semantic matrix. This creates a LSA Space, a corpus of writing that student answers to short answer questions are analyzed and scored against.
4. In an experiment, 6 questions were used and student answers were auto scored and compared to human scores. Now this process had some significant steps I won't get into, and the answers were short answer -- 20 - 100 words or so. But the results were interesting in two ways:
A. The scores on the first three test questions, the ones used to train the machine to score, matched human raters.
B. The machine was able to score on the second three questions accurately, without being trained. That is the algorithm designed on the first three test questions carried over and worked on the second set of three.

The potential here is tremendous -- the ability to deploy in all texts -- questions that draw on the text being read for accuracy and a range of answers. Imagine this in a system where questions occur as students read, engaging them as at key points to help them pause. And imagine the data that might result, where a student sees not only how they did, but what class averages are. Imagine a tutoring agent stepping in from time to time as answers come in, helping students learn deeper based on the answers. That is, with a well designed automated assessment tool, one doesn't just have to give a score alone, the score could trigger dialogic tutoring agent, or suggest a study group of fellow students to chat with, or a trigger a message to an on campus tutor.

It's not the score so much as what is done with the score to foster further learning. The key is that score comes from writing, an act that research shows deepens learning more fully than just reading, just highlighting, and just multiple choice (even in LC) answering.

My understanding is that these questions were designed for convergent thinking (see the graphic here -- Faultless Facilitation – Leveraging De Bono’s Six Thinking Hats -- on that) and not divergent.

There's more, but I'm out of time.

*I apologize

in praise of typo, errata, and error

http://www.theatlantic.com/technology/archive/2014/06/a-corrected-history-of-the-typo/373396/

The URL leads to a piece by Adrienne LaFrance,  "A Corrected History of the Typo" with the subtitle notation, "In the beginning, print was not about perfection; it was a space for collaboration."

LaFrance interviews "Adam Smyth, an English literature fellow at the University of Oxford who specializes in the instability of early modern texts," and he walks her through early printing, the role of errata, and how the relationship of early writers, printers,and readers coalesced around the expected excavation and discussions around error. LaFrance steers the discussion to Smyth with how error is treated online, where it often is erased in corrections (though wiki's most notably keep versions). Together they observe, and this just a sample:

Errata lists in the early days of printed books, then, were themselves a sort of early comment section—the place where revisions were made and ideas were exchanged. They were "confessional spaces" and "emblems of a new culture of accuracy," but errata lists were also a way of seeing books as a collaboration between reader and writer, rather than just the one-way broadcasting of a set of ideas. Which means that print, in its infancy, didn't actually lead to "better, more accurate texts," but to "the dissemination of blunders," Smyth says. It is in this way that the dawn of book printing sounds a bit like where we find ourselves today on the Internet—a fluid and collaborative space for ideas that sometimes seems to be equal parts information-rich and error-riddled. The difference in early print, though, is that errors "were not hidden away." And while screengrabs capture some evaporated Internet writing for posterity, much of what's published today simply disappears or changes with all the imperceptibly of a distant keystroke. 

As a writing teacher, I'd assign this to my students. It's useful for exploring collaboration, for thinking "about tolerating, rather than eliminating, reasonable mistakes," about the social life of information, about the transition into new technologies and how the new starts out by aping what came before it. 

But on the idea of "reasonable mistakes," it would be especially useful for weaker, less confident writers whose fear of error might make it harder to draft, and as well for a discussion of peer review and workshopping, where it might be possible to create a practice of not calling error out for correction but using it as an occasion for discussion and exploration. 

Too, the other lesson to be drawn from this is one wiki's teach as well -- reminding writers to use save as and other techniques for storing drafts, preserving versions of work with errors intact. The goal for most writers will remain on most occasions to publish (or in a course, to turn in a final draft) with as little error as possible, all the way to no errors at all. But the point is, as Paul Krebs notes in another piece I'd assign writers, that error will happen, is in fact necessary for writing to proceed, for thinking to improve, for learning to happen. 

Error is a good thing, and to hide and treat it as a shame, to shame writers who make error, is to shame writing.

How UT Austin Helps Students Graduate

Apologies for quoting heavily, but there's a lot in this article -- "Who Gets to Graduate" by Paul Tough at www.nytimes.com/2014/05/18/magazine/who-gets-to-graduate.html that applies to teaching, professional development for teachers -- whether those teachers are classroom instructors, TA's,  or tutors.

When you look at the national statistics on college graduation rates, there are two big trends that stand out right away. The first is that there are a whole lot of students who make it to college — who show up on campus and enroll in classes — but never get their degrees. More than 40 percent of American students who start at four-year colleges haven’t earned a degree after six years. If you include community-college students in the tabulation, the dropout rate is more than half, worse than any other country except Hungary.

The second trend is that whether a student graduates or not seems to depend today almost entirely on just one factor — how much money his or her parents make. To put it in blunt terms: Rich kids graduate; poor and working-class kids don’t. Or to put it more statistically: About a quarter of college freshmen born into the bottom half of the income distribution will manage to collect a bachelor’s degree by age 24, while almost 90 percent of freshmen born into families in the top income quartile will go on to finish their degree.

When you read about those gaps, you might assume that they mostly have to do with ability. Rich kids do better on the SAT, so of course they do better in college. But ability turns out to be a relatively minor factor behind this divide. If you compare college students with the same standardized-test scores who come from different family backgrounds, you find that their educational outcomes reflect their parents’ income, not their test scores. Take students like Vanessa, who do moderately well on standardized tests — scoring between 1,000 and 1,200 out of 1,600 on the SAT. If those students come from families in the top-income quartile, they have a 2 in 3 chance of graduating with a four-year degree. If they come from families in the bottom quartile, they have just a 1 in 6 chance of making it to graduation.

The full article begins with a profile of University of Texas, Austin student Vanessa Brewer, the challenges and choices she makes, putting her face and story on the kind of data above, and then shifts into a profile UT's David Laude, a chemistry professor by degree who is now a senior vice provost focusing on improving graduation.

In 1999, at the beginning of the fall semester, Laude combed through the records of every student in his freshman chemistry class and identified about 50 who possessed at least two of the “adversity indicators” common among students who failed the course in the past: low SATs, low family income, less-educated parents. He invited them all to apply to a new program, which he would later give the august-sounding name the Texas Interdisciplinary Plan, or TIP. Students in TIP were placed in their own, smaller section of Chemistry 301, taught by Laude. But rather than dumb down the curriculum for them, Laude insisted that they master exactly the same challenging material as the students in his larger section. In fact, he scheduled his two sections back to back. “I taught my 500-student chemistry class, and then I walked upstairs and I taught this 50-student chemistry class,” Laude explained. “Identical material, identical lectures, identical tests — but a 200-point difference in average SAT scores between the two sections.”

Laude was hopeful that the small classes would make a difference, but he recognized that small classes alone wouldn’t overcome that 200-point SAT gap. “We weren’t naïve enough to think they were just going to show up and start getting A’s, unless we overwhelmed them with the kind of support that would make it possible for them to be successful,” he said. So he supplemented his lectures with a variety of strategies: He offered TIP students two hours each week of extra instruction; he assigned them advisers who kept in close contact with them and intervened if the students ran into trouble or fell behind; he found upperclassmen to work with the TIP students one on one, as peer mentors. And he did everything he could, both in his lectures and outside the classroom, to convey to the TIP students a new sense of identity: They weren’t subpar students who needed help; they were part of a community of high-achieving scholars.

Tough goes on to profile one more person in some detail, David Yeager, a UT assistant professor who does work on the psychology of education and who studied how best to help students through their anxiety about college, especially students like Vanessa Brewer.

Before he arrived at U.T. in the winter of 2012, Yeager worked as a graduate student in the psychology department at Stanford, during an era when that department had become a hotbed of new thinking on the psychology of education. Leading researchers like Carol Dweck, Claude Steele and Hazel Markus were using experimental methods to delve into the experience of students from early childhood all the way through college. To the extent that the Stanford researchers shared a unifying vision, it was the belief that students were often blocked from living up to their potential by the presence of certain fears and anxieties and doubts about their ability. These feelings were especially virulent at moments of educational transition — like the freshman year of high school or the freshman year of college. And they seemed to be particularly debilitating among members of groups that felt themselves to be under some special threat or scrutiny: women in engineering programs, first-generation college students, African-Americans in the Ivy League.

The negative thoughts took different forms in each individual, of course, but they mostly gathered around two ideas. One set of thoughts was about belonging. Students in transition often experienced profound doubts about whether they really belonged — or could ever belong — in their new institution. The other was connected to ability. Many students believed in what Carol Dweck had named an entity theory of intelligence — that intelligence was a fixed quality that was impossible to improve through practice or study. And so when they experienced cues that might suggest that they weren’t smart or academically able — a bad grade on a test, for instance — they would often interpret those as a sign that they could never succeed. Doubts about belonging and doubts about ability often fed on each other, and together they created a sense of helplessness. That helplessness dissuaded students from taking any steps to change things. Why study if I can’t get smarter? Why go out and meet new friends if no one will want to talk to me anyway? Before long, the nagging doubts became self-fulfilling prophecies.

[ . . . ]

Yeager began working with a professor of social psychology named Greg Walton, who had identified principles that seemed to govern which messages, and which methods of delivering those messages, were most persuasive to students. For instance, messages worked better if they appealed to social norms; when college students are informed that most students don’t take part in binge drinking, they’re less likely to binge-drink themselves. Messages were also more effective if they were delivered in a way that allowed the recipients a sense of autonomy. If you march all the high-school juniors into the auditorium and force them to watch a play about tolerance and inclusion, they’re less likely to take the message to heart than if they feel as if they are independently seeking it out. And positive messages are more effectively absorbed when they are experienced through what Walton called “self-persuasion”: if students watch a video or read an essay with a particular message and then write their own essay or make their own video to persuade future students, they internalize the message more deeply.

The piece goes on to explore Laude's creation of a "University Leadership Network, a new scholarship program that aims to develop not academic skills but leadership skills. In order to be selected for U.L.N., incoming freshmen must not only fall below the 40-percent cutoff on the Dashboard; they must also have what the financial-aid office calls unmet financial need. In practice, this means that students in U.L.N. are almost all from families with incomes below the national median."

There's a lot of useful detail in the piece, more insights, and it's really worth reading in full.

Blogging via Moleskine: the Pedagogy of Transcription

Writing in Slate, blogger Justin Peters describes his blog-post writing process:

I’ve always enjoyed writing things by hand, but I didn’t formalize the process until I started blogging daily for Slate. Almost every morning, before the day starts and I start drowning in emails, I go to a coffee shop with a pen and a small Moleskine notebook. There, I try to conceive and write drafts of two separate posts before 10:30 a.m. Then, it’s back to my apartment, where I shed my pants, transcribe, and refine what I’ve written. (One of the nice things about writing my posts by hand is that it allows for a built-in revision process.) 

I can write in my notebook anywhere and everywhere. I will frequently bring it with me and dash off a rough draft while in transit—waiting for the subway, sitting on a bus. This is very convenient, as it allows me to be productive on the go without having to own a smartphone. (My current cellphone is at least 10 years old … but that’s a story for another time.)

This reminds me of something I saw close to thirty years ago, and I forget where, maybe 60 Minutes?, but it was a profile of Woody Allen and he described how he'd write short pieces for the New Yorker by long-hand on a legal pad before typing the piece for submission. But more to the point, Peters observation on revision recalls this bit from Craig Fehrman:

In the last 30 years, however, technology has shifted again, and our ideas about writing and revising are changing along with it. Today, most of us compose directly on our computers. Instead of generating physical page after physical page, which we can then reread and reorder, we now create a living document that, increasingly, is not printed at all until it becomes a final, published product. While this makes self-editing easier, Sullivan thinks it may paradoxically make wholesale revision, the kind that leads to radically rethinking our work, more difficult.

Fehrman's piece draws much from the work of Hannah Sullivan and her recently published The Work of Revision (http://www.hup.harvard.edu/catalog.php?isbn=9780674073128). Sullivan's main focus is on how technologies -- cheap paper, typewriters for faster drafting -- made possible the kind of revision that literary writers (Hemingway, Wolfe, Pound, Eliot, and other modernists she studies) did. But what's key is that the act of switching  -- or not switching -- composing media, from pen and paper notebook draft to online draft (or not), alters how writers revise.

In teaching writing, then, there's a virtue to be found and built on in urging writers to use varied writing tools, everything from audio notes recorded via a phone, to a series of tweets, to notes on napkins, to drafts in notebooks, to writing in discussion boards, to drafts in a word processor, or entries in a blog, and so on. Moving one's thinking on an idea from writing space to writing space, from writing tool to writing tool, from writing occasion to writing occasion lets an idea both age and blossom in the way wine does. With any luck,  no idea would be served before its time (and place and purpose and audience).

On the Death of My Wife's Cat

Ebony beloved cat of Barbara Crowley-Carbone, died, after 20 years of devotedly sleeping on her head most nights, in his owner's lap on Sunday, July 7. He is buried in our backyard, in a place that will become a small flowerbed, likely perennial bulbs that will bloom early each spring.  Ebony was predeceased by a brother, Bogart, who died of kidney failure in 2004, and a sister, Simba, the runt from their litter, who died at two weeks old, found tightly curled --and very stiff-- on a sun-streamed bedroom pillow. All three cats were as dark as Ebony's name, with small triangular Siamese faces, and, if you looked closely, tiger stripes of a deeper black.

Ebony lived to and died from old age and his final months came with weaker vision, less spring, confusion that left him wailing on occasion, and towards the very end, it seemed, the search for a quiet place to let go of living. And so we'd find him in places he never went before: a very tight spot behind a book case, behind a book bag under an old school desk, asleep on top of basket of keys that sit atop a cabinet where we store coffee and tea, and, for some reason only after a bath, he'd sleep in the litter box. But still, his favorite place to sit, was on my wife; if she sat on the couch to grade, he'd walk over her legs, across the math-paper pile on her lap, and climb to her neck, and settle, with his head under her ear, fore legs over her shoulder, and rump on her breast.

Combined with summer heat and a new-to-her cat allergy, the location wasn't the best of places, but as often as not Barbara would take a Zyrtec, shift the papers to her left and his rump a bit to the right, adjust her bra, and let him be, knowing he had little time left.  And on the not-times, when things were too hot or itchy for her, he'd wander over and sit on me. I don't like cats on me. The rule of thumb for pets in our house is that they don't belong to me. I don't feed them, hold them, groom them, pick up after them. I'd occasionally flick a string at the cat to get him to jump, or throw a toy his way, but I was just as likely to toss a pillow to get him off the bed, or a dish towel to get him down from the table. Still, as he got weaker and weirder, it got harder to push him away, and so in the last few weeks he'd come to me, searching for the same location. I'd tuck a couch pillow on my lap, lacking as I do the perch he found with my wife, so he could climb to where he wanted to be. It made reading or writing a trick, and so when he came my way, I'd often switch to a gin-and-tonic and Netflix moment as a bribe to myself to be kind to him in his dotage dolorous.

Ebony, in his dotage, allowed on the coffee table so he can look at the skinks.

Still, for the odd crying jags, the more frequent search for new quiet spaces, the increased lap time, there were moments when Ebony would be or try to be himself: his appetite stayed good until the last day or so, when he switched to water; he'd pop his head up at the window when a rabbit stopped outside of it to eat the clover that grows in our yard; if one my daughters trailed a cat toy, he'd follow after it (though they had to play the game in slow motion); and he'd come to the table for scraps, sniffing and beseeching for a nibble of pork, chicken, fish, or pop-corn if we had a fresh-popped batch.

My kids got in the habit of sneaking him food because when Bogart, his brother, was alive, if Ebony didn't finish his breakfast before Bogart had finished his own, Bogart would Bogart Ebony's food, shoving him out of dish with a head bump and a look. So Bogie was plump, Ebony thin, and my daughters in empathy got him hooked on scraps. And that habit held on to the end, an end marked by the things that come with getting old punctuated by habits from a life lived. He carried on, doing what he could on his own when could, crying honestly when he was lonely and confused,seeking time with those he was about to leave behind, and despite occasionally doing things that would be embarrassing -- sleeping in a litter box when wet of fur -- seeming never ashamed of who he was and how his days were spent.

Strange to say, for a pet that wasn't mine, whom I grudgingly acknowledged, and certainly didn't love, I do miss his presence. I think in maybe the same kind of way George Bailey discovers, in It's a Wonderful Life, that he's happy to see even the exasperating broken newel on his staircase. Don't get me wrong, I'm glad to be one pet less, and that much closer to no litter dust or stench, no pet food odor, no scratch marks, no dander, and all kinds of wonderful no mores to come (after the remaining cat moves out with my daughter if she ever can afford to move out). And so as happy as I am to have cat-things diminished, Ebony will be missed. Maybe because when he begged for a bite, the kids had a running joke they'd make, or when he did one of those dumb things cats do, like slide in panic on a new-polished floor, my wife would laugh a certain way because it was him, and that laugh, that one special one for that occasion, won't be heard again.

Students: We'll Give You Our Print Textbooks When You Pry Them From Our Cold, Dead Hands

Digital Book World did a $45 webcast yesterday on e-textbook trends. Happily, they provided a free summary today because the news isn't new and instead confirms what we knew.

While publishers are increasingly creating and selling digital materials and students increasingly have the devices on which to consume that content, only 3% of students last semester used a digital textbook as their primary course material (for a specific course). That’s down from 4% for the fall semester.

Overwhelmingly, students prefer print, according to the survey of 1,540 undergraduate college students at both four-year and two-year institutions of higher education.

When asked why, about half “prefer the look and feel of print;” nearly half say they like to highlight and take notes in the textbooks; and a third cite that they can’t re-sell digital textbooks.

So it goes.

U.S. director of Bowker Market Research, Carl Kulo, who presented his research, predicts e-textbooks will take off in the standard prediction range: 3 - 5 years, the rate of take off now for the past 15 - 20 years. Hey, don't laugh, predict this often enough and it's bound to right.

But digital sales are increasing  -- "Despite the stagnation of digital textbook adoption, some publishers are reporting that a significant portion of their revenue is 'digital'.” -- with Pearson reporting that 50% percent digital revenue.

So let's ask this: if e-textbooks aren't selling, what is? Homework systems? Tools like e-portfolios? Course design and faculty development services delivered digitally?

What purpose does a textbook serve? To dump information, advice, activities, assignments, guidelines into a single device -- for years a print device bound by covers and held by string and glue -- so that an instructor could direct students to read, do, discuss, remember, write about, lab about, and even learn about what the book covered.

Students are right -- simply taking that print thing and dumping into a PDF and delivering it on a screen does suck compared to having the print book. It's like difference between drinking a fresh gin and tonic on a sunny afternoon on the back porch overlooking a quiet beach from drinking a gin and tonic that was poured a few days ago and delivered flat and diluted for your drinking pleasure in a place where the view is of the brick wall across from you stuck-shut window in the room with no a.c.. Sure, it's still a gin and tonic, but it don't look and taste the same, and isn't improved by being in a hot room with no breeze and no view.

Now we do PDF-based e-books because they're cheap, fast, and make more money than they lose for being cheap and fast and allow us to say to the market we have lower-priced options and books that can be read on an iPhone (even though Bowker finds most students prefer laptops still), and other reasons.

e-textbooks will take off -- are taking off in fact -- when and where the word 'book' means something different than what the print thing is. Where the purposes and contents and activities once in print live a native digital life, with a digital rationale, where things are unbound from covers and rebound to learning purposes and needs. 

Unsolicitated Solicitations Solutions

From: Carbone, Nick
Sent: Tuesday, May 28, 2013 2:40 PM
To: 'Breanna Colin'
Subject: RE: Publishing Industry Prospects

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Nick Carbone
Director of Digital Teaching and Learning
ncarbone AT bedfordstmartins DOT GOES HERE com
phone: (six one seven) two seven five – one eight seven two

_____________________________________________________________________________________________________________________

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To: Carbone, Nick
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Deep Learning and Memory Implants, Oh My! MTR's 10 Breakthrough Technologies of 2013

MIT Technology Review (MTR) magazine offers an annual list of 10 technology breakthroughs. This year's list is embedded in their introduction to the full set at http://www.technologyreview.com/featuredstory/513981/introduction-to-the-10-breakthrough-technologies-of-2013/:

Our definition of a breakthrough is simple: an advance that gives people powerful new ways to use technology. It could be an intuitive design that provides a useful interface (see “Smart Watches”) or experimental devices that could allow people who have suffered brain damage to once again form memories (“Memory Implants”). Some could be key to sustainable economic growth (“Additive Manufacturing” and “Supergrids”), while others could change how we communicate (“Temporary Social Media”) or think about the unborn (“Prenatal DNA Sequencing”). Some are brilliant feats of engineering (“Baxter”). Others stem from attempts to rethink longstanding problems in their fields (“Deep Learning” and “Ultra-Efficient Solar Power”). As a whole, we intend this annual list not only to tell you which technologies you need to know about, but also to celebrate the creativity that produced them.

Each link in the introduction above leads to an article length exploration of the breakthrough. Deep Learning and Memory Implants caught my eye. Deep Learning uses computer processing power to simulate neural networks; the article focuses on Ray Kurzweil's work at Google, where they've made magnitudes of progress in speech and image recognition using deep learning. The image recognition will be key to Google Cars, to indexing images and video; the speech software does the same for sound, including human speech. Kurzweil's next area of inquiry? -- natural language understanding:

    Kurzweil isn’t focused solely on deep learning, though he says his approach to speech recognition is based on similar theories about how the brain works. He wants to model the actual meaning of words, phrases, and sentences, including ambiguities that usually trip up computers. “I have an idea in mind of a graphical way to represent the semantic meaning of language,” he says.
    That in turn will require a more comprehensive way to graph the syntax of sentences. Google is already using this kind of analysis to improve grammar in translations. Natural-language understanding will also require computers to grasp what we humans think of as common-sense meaning. For that, Kurzweil will tap into the Knowledge Graph, Google’s catalogue of some 700 million topics, locations, people, and more, plus billions of relationships among them. It was introduced last year as a way to provide searchers with answers to their queries, not just links.
    Finally, Kurzweil plans to apply deep-learning algorithms to help computers deal with the “soft boundaries and ambiguities in language.” If all that sounds daunting, it is. “Natural-language understanding is not a goal that is finished at some point, any more than search,” he says. “That’s not a project I think I’ll ever finish.”

Imagine what kind of grammar checker  that would produce. Or maybe it could create an auto-writer instead as well as writing assessment tools -- ask the computer (speech recognition, remember; no need to use a mouse) to create a report on the latest trends in and it arrives in your inbox in a day or two, freeing your assistant to do proper things, like pick up your dry-cleaning and getting your lunch. And then, if you really want to get scifi with it, imagine that the memory implant, the idea of which is to embed a chip that allows the return of long term memory capabilities for people who have lost (perhaps from traumatic injury, or Alzheimer's) that ability. Once that's possible, would there be a service that offered to preserve your memories on a chip so that should you suffer a stroke, and receive a memory implant, the implant provides not just the ability to create new long term memories, but restores key prior ones? What if the implant could give you memories you never had but would like or need, such as, oh, maybe remembering the contents of that history textbook you never got around to reading before the final exam?

MFA Writing Programs, where MFA = (M)entors (F)or (A)spiring Writers

arn Wilson, writing for Brevity, has a post (http://brevity.wordpress.com/2013/05/13/of-spinning-and-writing-in-defense-of-mfa-programs/) called "Of Spinning and Writing" In Defense of MFA Programs."

She sets her piece as ruminations that occupied her mind during the a spinning class, where she reviews the often scathing and dismissive critiques of MFA programs, acknowledges those critiques have some merit, but then she considers what MFA programs do well. It's not just the feedback on writing, but on seeing how to live a life that makes room for writing. And she touches on the differences between people who can find mentors organically, perhaps because they live an already networked life, a life where they get to know and see and talk to people who think of themselves as writers first, on the one hand, from those who need help finding a mentors to learn how to live a writing life. MFA programs offer that.

Here's something else she says that's true, and not only for MFA programs, but for developmental writing courses, first year writing courses, or any other teaching of writing situation:

And those complaints that MFA programs produce too many writers and that writing can’t be taught? I agree there is a certain luminous originality in the finest writing that can never be taught. But after years of seeing students’ awe-inspiring growth over a semester’s time, no one can convince me that most writers won’t improve, dramatically, with regular practice and structure and meaningful feedback.  (I think that is also a cultural bias, an American Western, individualistic, frontier mentality: many other cultures value apprenticeship, elders and generations of accumulated wisdom.)  The typical MFA program may not birth genius, but the students improve.

Writing can be taught, it can be learned -- by writing, getting and giving feedback, revising, and learning habits of mind that foster writing. Every writing course can help any writer improve, no matter how novice the writer, how unsure, how raw, simply by offering "regular practice and structure and meaningful feedback."

Mindsploitation -- Now That's the Way to Do It

Laura Runge, an English professor at the University of Southern Flordia, tweeted a link to info on Mindsploitation, a book by television comedy writer Vernon Chatman, a comedy writer. I bought a copy (http://bit.ly/12epUAL) because it touches on an issue I frequently do faculty workshops around -- plagiarism and cheating. But I really bought it because it looks to be a fun read.

Here's what the book, along with its full title, is about:

Mindsploitation: Asinine Assignments for the Online Homework Cheating Industry
There are hundreds of online companies that will do your homework for you – at a price. But will they write ANY essay you request? Only the WORST of these horrible companies were employed in the composition of Mindsploitation. A GREAT DEAL of money was wasted ACROSS THE GLOBE to commission what may be the dumbest collection of ridiculous assignments in HUMAN HISTORY.

What does it say about our society that we can buy a quick custom eulogy for our grandmother, or pay to have a love poem for a mistress prepared by a stranger at the click of a button? How entitled is a culture that keeps these services afloat? Mindsploitation uses such questions as a launching pad for wildly entertaining comedic exchanges. The 50 assignments in this book hilariously explore self-help, spirituality, family, health, diet, pop culture, love, and more.

I haven't read the book yet, but just based on the description above, I'd be tempted to assign it in a writing course if I were currently teaching. It reminds of Don Novello's The Lazlo Letters (The Lazlo Letters by Don Novello | 9781563052859 | Paperback | Barnes & Noble ); in that, Novello, playing a character named Lazlo Toth, who sent off-beat letters to politicians and other leaders which resulted in funny exchanges, a kind of literary version of what Ali G went on to do less inventively. Mindsploitation seems a work in that vein, one that exposes some absurdities and creates a few of its own along the way.

I wish textbook publishing had room for books or digital learning projects that could do more with humor, could teach with a lighter touch. An outlying instructor here or there pulls that off, but by and large, most professors eschew humor from their pedagogy, and so making a book which relies on it simply isn't practical. Too much self-seriousness in teaching.

That said, I wonder how it would work to use a professional writing cheat to do some of the writing the job requires. If you were try that, engage a writer to something you're asked to do, what's the most asinine assignment, based on the kind of writing you do for work, that you could imagine?

To help imagine the kind of thing Chatman did, here's a sample request he made for a commissioned school assignment:

My midterm thesis essay paper is an exploration of Alternate Endings To Great Works of Literature. All I need from you is to come up with some Alternate endings to some Great works of literature … Provide a new ending to Catcher In The Rye where Holden Caulfield turns into a crawfish and goes into some kind of retail business.

Here's a Dilbertesque work assignment of the kind I mean:

My editorial team has been asked to provide use cases on nontraditional teachers for programmers at an educational software vendor coding a product for us. Can you write use cases for the following potential teachers?: the odd lady at the bus station with the sign on her neck about end times who follows families around trying to get the children to get the parents to get outta Sodom, Sodom being, in her mind, the bus station;  the loud guy in the business suit and bluetooth cell headset at the airport bar whose dropping names and instructing someone somewhere to buy, buy, buy, or sell, sell, sell, the guy whose voice gets louder, back gets straighter, and stomach gets sucked-trimmer every time any woman even looks at the stool next to him, which stool remarkably stays as empty as a handicapped driver parking space in a crowded parking lot during a torrential rain.

Cynicism Leads to Evil Learning Software

Yesterday I read a talk, "Decode the Academy," that Barbara Fister, co-author of Research and Documentation in the Digital Age and a gifted librarian, whose scholarship focuses on teaching students to use the library effectively, gave at LOEX, a conference for librarians. I want to draw attention to a critique she made of one particular brand of citation software:

But before we do that, let's take a look at a student's-eye view of research. There's a clever piece of software on the market that explains very clearly how many students perceive the practice of putting together a research paper, because it's designed to help them do it as quickly and efficiently as possible. It’s called Citelighter (http://citelighter.com) and it lets students go to webpages, grab quotes, save the bibliographic information so citations can be generated from it, and offers a space to glue them together in a document. The video on the website explains how it can make your life better. You go to your favorite sources, highlight the facts you need (and everything you find in a source is described as a "fact"), arrange them, add your own thoughts, and push a button. Your paper is done. Better yet, this process is socially networked so you can share your collections of facts and borrow them from others. No need to search out and read any sources at all. Perhaps more dispiritingly, the video shows how a student with dreams and an urge to create something meaningful is finally able to do that – once he has completed that tiresome paper.

It's a clever app for doing more efficiently what students apparently think they should be doing. And we have evidence that this is, in fact, exactly how many students perceive the practice of writing research papers, in the Citation Project (http://cite.citationproject.net), an ongoing study led by Becky Howard and Sandra Jamieson. They led an effort to gather and analyze first year writing samples from multiple institutions, largely in an effort to understand student research writing behavior to help them avoid plagiarism. What they found is that most students avoid paraphrasing or summarizing the source they use. Instead, they grab quotes and don’t bother to interpret them. They grab them mostly from the first or second page of articles. They grab what works, whether or not it's in any way representative of the main point of the article. And they use those quotes as building material glued together with a thin mortar of their own words. Most of the work reviewed in the study is “patchwriting” rather than analysis or argument. The study suggests students are able to find the kinds of sources we hope they will use—that’s the good news. The bad news is that they don’t read them. Reading is not required when you think the point is to harvest and arrange quotes. By the way, this is not a product of our digital era. Jennie Nelson studied undergraduate writers some years ago, back when the quotes they mined had to be copied from books, and concluded almost exactly the same thing: most first year writers gather material and quote it without engaging in the recursive process of reading, writing, and making meaning, the very process that we are trying to promote with these assignments.

Now Citelighter markets directly to students, and so the cynicism of their approach taps into the drudgery students experience with wretchedly designed research assignments, the kind that emphasize research mechanics and penalties  -- double spaced, 1" margins, MLA style, at least 6 sources, three must be from the library, no use of Wikipedia, piss-in-this-plagiarism-detection-engine-cup-before-turning-in-the-paper-because-if-you-plagiarize-the-wrath-brought-down-upon-you-will-be-like-no-judgment-you-have-ever-known kind of thing -- more than being curious and having something worth saying to people who might want to read it because they're curious about the same stuff. Citelighter knows that for many students, sadly, research assignments aren't about writing, they're about compiling a correctly cited and formatted document, one that meets a checklist of requirements, the least of which is to be interesting to read.

We're looking at/for software that at its base does what Citelighter does -- helps researchers save and cite sources, notes on sources, draft writing that uses the sources, and source sharing -- but we want software that offers those features (and others) in the service of research as inquiry and curiosity, not as a meaningless hurdle. We're looking for software that respects the writer's integrity, assumes it, and gives them tools for learning what integrity means and tools for managing their sources to avoid accidental plagiarism. We're looking for software that lets teachers see what students are doing, so that teachers can coach students beyond quote harvesting, help them assess sources, help them revise their thinking as they learn more, help them decide what role a source plays, and other acts of deeper reading and synthesis that the Citation Project and Jennie Nelson described as missing from most novice research writing.  We seek software that supports student-to-student sharing so that students can swap source leads, share drafts, co-author if a project calls for it and otherwise help one another in all the ways that faculty get help from colleagues when they do research. We seek to do no evil.

And here's what else -- we know for that software to work well, to really help writers, good design is essential, but never enough.  Instructors will need help on how to design research assignments that take advantage of what the software offers in two ways:

One, if software features address the mechanics and formatting and time management and proofing checklist aspects of the research project, if it helps address plagiarism, it addresses two areas that cause many teachers anxiety. If we can show teachers how the software lessens anxieties for them and students, then we can show teachers how it can affirm aspirations. No teacher really wants to read a boring, atonal, quote-harvesting research paper. It's mind-numbing and soul-crushing to end a semester doing that. But fear of students not being seen as correct by other teachers, or fears that research is too hard to teach and mechanical correctness in source handling is essential above all to route the scourge of plagiarism, leads to assignment designs that lead to mind-numbing, soul-crushing reading.

Thus two, the need will be to help teachers out of those fears, which can be deep-seated. Software alone won't budge it. Teachers' instincts will be to use their same assignments and practices and tone that just student writing down, that misplace emphasis, and that lead to wretched-to-read research. Having software that offers a better path isn't enough; teachers will need help taking steps on that path. The software, chosen well and designed right, provides an opportunity and means to find a better way to teach research writing, offering tools that never before existed, but teachers will need stories of how others like them changed their assignments to take advantage of the possibilities, workshops on drafting their assignments, practical advice on how to work differently, what to read and when, what not to  ready and why of students' works.

There is no magic software. Only software that makes magic possible.

Words Count, and So Should Word Counts

James Fallows, at http://www.theatlantic.com/technology/archive/2013/05/interesting-software-follow-up-scrivener-googles-orphans/275563/, points out some advanced features of Scrivener, a word processor, one of which is kind of cool in my view: it provides word length targets for projects and measures on progress. The link above leads to his post, and it shows an image where a range of writing projects are listed in table. You’ll note three columns that abut one another in this order: Total Words; Total Target; Total Progress.

Fallows lives, as a journalist, in a world where assignments are by word length, but more and more writing teachers in first year composition in the U.S., and teachers in other courses where writing is assigned, are basing assignments on word counts instead of page length. For those professors, the move isn’t tied to column inches but rather to the erasure of the print manuscript. Interestingly Fallows reports that he doesn’t use the feature for his columns, but rather for writing books, and he uses them as a way of “setting the daily output targets that are crucial to maintaining sanity through the months-long slog of finishing a protracted writing project.” I know a lot of folks working on dissertations and first books who would benefit from this.

Such a tool rejiggered a slight bit for novice writers in college, who are learning the craft of writing longer pieces, or, simply who need to hit writing targets to keep up with work, especially in integrated into an online classroom space, could offer useful learning analytics. For example, if a professor assigned a sequence of writing activities with total word targets, especially for low stakes writing like daily discussion prompts or journal writing -- where part of the goal of such assignments is to work muscles, to get students into a writing habit, much the way some activities in a gym are to build strength, build cardiovascular capacity, build muscle memory – then the system might allow students to see their total words, the target words, their progress on target and as well the class average of total words. Or if students opted to form writing teams or groups, they could see the total words of each member.

So just this one slight thing – capturing word counts and displaying them in slightly different contexts -- changes how writers see what they’re doing and how teachers of novice writers see, and can assign, different writing activities. 

Wisdom of the crowds my Yelp

So, you want stars do you? Reviews by readers of your books, of diners of your restaurant, of sleepers in your bread and breakfast, of subscribers who view the movies you stream. Stars, likes, micro reviews, and more. They matter. "The Harvard Business School’s Michael Luca has found, for example, that a one-star uptick in a Yelp review can lead to a nine percent improvement in revenues for independently owned restaurants. Other studies have shown a similar impact for independent hotels—and for books," notes Tom Vanderbilt writing in Wilson Quarterly (http://www.wilsonquarterly.com/article.cfm?aid=2292). But as Vanderbilt goes on to say, the quality of the written reviews can be paltry, and which reviewers get higher rankings has more to do with being early to a review than to being good; also, has an average number of stars emerges, reviews that deviate from the average, even if well-reasoned, get less respect:

But curiously, as more ratings trickle in, a study by business professors David Godes and Jose Silva has found, the average rating begins to decline. “The more reviews there are,” Godes and Silva suggest, “the lower the quality of the information available”; later reviewers tend to be either less serious or less disposed to like the book, or to respond to other reviewers rather than to the book itself. While one might think a five-star review would summon more passion than a four-star review, one study found that four-star reviews were, on average, longer.

What consumers make of reviewers is also a fertile field of study. A team of Cornell University and Google researchers, for example, found that a review’s “helpfulness” rating falls as the review’s star rating deviates from that of the average review—as if it were being punished for straying. As the team noted, defining “helpfulness” is itself tricky: Did the review help people make a purchase, or were they rewarding it for conforming with what others were saying? There are a number of feedback effects: Early reviews tend to draw more helpfulness votes, simply because they’ve appeared online longer. The more votes a review has, the more its “default authority,” and the more votes it tends to attract.

So the crowds aren't so much wise as lazy maybe? Another interesting tidbit Vanderbilt exposes -- when people judge others reviews, they tend approve critiques of objects more readily than critiques of art, or put another way, a critique that carries a resemblance to fact more than opinion, will get more likes and favorites:

As Temple University’s Susan Mudambi and David Schuff found, people tend to rate longer reviews for “search goods”—such as cameras or printers—more positively than those for “experience goods.” A strong negative review for a camera might reflect some discrete product failure (pictures were blurry), but a strong negative review for a book might simply be another person’s taste getting in the way.

My use of online reviews follows the split -- I'll skim for a technology purchase, or to buy a tool, or to hire an electrician or carpenter -- but not ever for things like restaurants, hotels, music, books, films, and other things where the use is for immediate enjoyment while consuming, watching, reading, hearing, or tasting the thing to bought.

Vanderbilt's piece goes on to focus on the quality of written reviews, the stuff that justifies the stars. Most of what he finds in written reviews isn't useful because as so many opinions accumulate, it's hard to know how to weigh the picture of the whole, especially in a review system where stars seem to be generously given for often vague reasons. Who's right -- the person who decrees the food the best ever and most authentic or the person who decries it as wretched, with poor service to boot? I don't want to wade through that thicket.

What interests me as an editor and some one who occasional writes on things I've read a lot about, or on or in areas I've done a lock of work in, often for audiences who know my prior work and my name, is how amateur reviewers often try to signal their authority. Vanderbilt again, "If the Internet was supposed to wrest criticism from elites, a good deal of the reviewing energy on Yelp (and other sites) is precisely an effort to establish one’s bona fides. In the reviews for a new seafood restaurant in my neighborhood, a number of the writers tout themselves as “New Englanders,” thus implying that they implicitly know of what they speak."

What professional reviews offer, restaurant, move, book, travel, is a consistent view and voice. I don't need all critics to agree on a movie before I decide to plunk dollars for a ticket, or all notices of a restaurant to be raves. I just need a few critics whose views I understand and can trust to be consistent. Even if I come to disagree with their tastes, if I know their tastes, then I know what will work for my own.

You don't get that kind of consistency in the Yelpland, where the wisdom of the crowds seems nothing more than the aggregation of exhaustion.