Data on Co-requisite Statistics (‘mainstreaming’)

Should students who appear to need beginning algebra be placed directly in a college statistics course?  For some people, this is no longer a question — they have concluded that the answer is an unqualified ‘yes’.  A recent research study appears to provide evidence; however, the study measured properties outside of what they intended and does not answer a basic question.

So, the study is “Should Students Assessed as Needing Remedial Mathematics Take College-Level Quantitative Courses Instead? A Randomized Controlled Trial” by Logue et al.  You can read they report at http://epa.sagepub.com/content/early/2016/05/24/0162373716649056.full.pdf

The design is reasonably good.  About 2000 students who had been placed into beginning algebra at a CUNY community college were invited to participate in the experiment.  Of those who agreed (about 900), participants were randomly assigned in to one of 3 treatments:

1. Elementary Algebra regular    39% passed
2. Elementary Algebra with weekly workshops   45% passed
3. College Statistics with weekly workshops    56% passed

At these colleges, the typical pass rate for elementary algebra was 37% while statistics had a normal pass rate of 69%.

The first question about this study should be … Why is the normal pass rate in elementary algebra so appallingly low?  I suspect that the CUNY community colleges are not isolated in having such a low pass rate, but that does not change the fact that the rate is unacceptable.

The second question about the study should be … Would we expect a strong connection between completing remediation (or not) with performance in elementary statistics?   The authors of this study make the following statement:

it has been proposed that students can pass college-level statistics more easily than remedial algebra because the former is less abstract and ses everyday examples

In other words, statistics is not abstract … not mathematics at the college level.  The fact that statistics focuses on ‘real world’ data is not the problem; the fact that the study of statistics does not involve properties and relationships within a mathematical system IS a problem.  I’ve written on that previously (see “Plus Four: The Role of Statistics in Mathematics Education at https://www.devmathrevival.net/?p=976)

The study uses ‘mainstreaming’ in their descriptions of the statistics sections in their experiment; I find that an interesting and perhaps better phrase than ‘co-requisite’.  It’s unlikely that the policy makers will move to a different phrase.

The authors of this study conclude that many students who place into elementary algebra could take college-level math (represented by statistics in their study) with additional support.  The problem is that they never dealt with the connection question:  How much algebra does a student need to know in order to succeed in basic statistics?  The analysis I am aware of is “not much”; in the Statway (™) program, most of the remediation is in the domains of numeracy and proportional reasoning … very limited algebra.

This is the basic problem posed in all of the ‘research’ on co-requisite remediation:  students are placed into low-algebra courses (statistics, liberal arts math), and … when they generally succeed .. the proclamation is the ‘co-requisite remediation works!’.  That’s not what is happening at all.  Mostly what the research is ‘proving’ is that those particular college ‘math’ courses had an inappropriate prerequisite of algebra (beginning or intermediate).

Part of our responsibility is to explain to non-math experts what the relationships are between various math courses, using language and concepts that they can understand while preserving fidelity with our own work.  We need to make sure that policy makers understand that it is not an issue of us ‘not wanting to change’ … the issue is that we have a different understanding of the problem and potential solutions.  In many colleges, the math department is already ahead of where the policy makers want us to ‘go’.

I encourage you to read this study thoroughly;  Because it using a ‘control’ and ‘random assignment’ design, this study is likely to become a star for policy makers.  We need to understand the study and provide a better interpretation.

 
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Where Dreams go to Thrive … Part III (More Evidence)

The leading cause of bad policy decisions is the phrase “Research clearly shows … ” which suggests that all of us should accept one interpretation of some unnamed set of ‘research’ (most of which is not research at all).  Understanding the needs of students not prepared for college mathematics is a long-term process, involving prolonged conversations among professionals as we attempt to understand what the data and the research say about our work and our students.

My goal is to present another scientific research article on the impacts of developmental education — remedial mathematics in particular.  This article is by Bettinger & Long called “Addressing The Needs Of Under-Prepared Students In Higher Education: Does College Remediation Work?” which you can download at http://www.nber.org/papers/w11325.pdf

This research is based on a large sample of students in Ohio.  The strategy is to adjust for selection bias that is so strong in all studies on remediation — Students referred to remediation tend to have both lower specific skills (math) and more academic challenges.  The authors define a series of variables for this purpose, and eventually calculate a ‘local area treatment effect’ (LATE) which is partially based on the fact that cutoffs for remediation vary significantly among the 45 institutions of higher education in the data.  The analysis of “LATE” involved a restriction on the sample — towards the middle, where the cutoffs have more impact; this analysis excludes the weakest (roughly 10%) of the overall sample.

Key Finding #1: Equal Outcomes for those in Remediation
For outcomes such as dropping out and degree completion (bachelor’s) students who had remediation achieved similar outcomes to those who did not, once the selection bias was accounted for.

Key Finding #2: For those most impacted by remediation cutoffs, outcomes are improved
The “LATE” analysis showed that remedial students had a lower rate of dropping out and a higher rate of degree completion compared to similar students without remediation.  The authors attribute this as an accurate (perhaps even conservative) estimate of the benefits of remediation.

Here is a nice quote from their summary:

We estimate that students in remediation have better educational outcomes in comparison to students with similar backgrounds and preparation who were not required to take the courses.  [pg 19]

The research also explored the impact of remediation on student interest (as measured by type of major); you might find that discussion interesting, though it is not directly related to the question of ‘thrive’ in remedial math.  I say that because the initial major data was taken from the survey attached to the ACT exam — usually completed long before a student examines their actual choices at the college they enroll at.  The authors do find an interaction between remediation and changing type of majors (specifically, changing out of math-related majors).

This study, as the others I’ve listed lately, provide a different picture of developmental mathematics than we hear in the loud conversations by policy makers (Complete College America, for example) and proponents of ‘co-requisite remediation’.  Those external forces almost always refer to ‘research’ that is simple (few variables) and aggregated; they have not dealt with the selection bias problem at all.  If you read the pronouncements carefully, you’ll notice that the biggest evidence of our failure in remedial mathematics is the large group of students who never attempt their remedial math course(s); this ‘damning conclusion’ is presented without any evidence that the nature of the remedial math courses had any causative connection to that lack of attempt.

As professionals, it is our job to both learn about the valid research on our work (the good and not-so-good) and to inform others about what this research says.

Evidence exists which truly does indicate that remedial mathematics is where dreams go to thrive.

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Student Success & Retention: Key Ideas

I’m working on a project which involves a search for strong research articles and summaries, and that included some work on ‘retention in STEM’.  I have some references on that, later; however, I wanted to present some key ideas about how to keep students in class so they succeed and how to retain them across semesters.

Rather than look at certain teaching methods as ‘the answer’, let’s look at some key ideas with surface validity and examine their implications for teaching.

• Students need to be working with the content over an extended period in order to be successful.

We know that learning is the result of effort, usually intentional.  Attendance is easily measured, but is not sufficient by itself.  The class needs to establish environments where students want to work with the material, and we know that grades are insufficient motivation for many students.

• Non-trivial ‘success’ (positive feedback) based on effort is strong motivation for most people.

If success seems impossible regardless of effort, it is easy to see why students would stop working.  However, success regardless of effort is also likely to result in drastic reductions in effort.  As in most human endeavors, people need to see a connection between effort and reward.

• A teacher’s attitudes are more important than specific methods.

A few years ago, I was trying some very different things in a class; in fact, I was not very proficient with some key parts of that plan.  However, my students responding to my attitude more than those methods.  As one student said, “Mr. Rotman would not give up on me!”  An honest belief that almost all students are able to succeed is strong motivation.

We need to see our classes as a human system, a community with a shared purpose.  Most people need relationships with a purpose … connections that help them deal with challenges.  I am not trying to be a friend to my students, but we do form a community which can support all members.

• Every student contributes to the success of the class.

Not all students will pass a math class.  Some of those who do not pass are able to provide help to those who do pass.  This past semester, I had a student who did very poorly on written assessments who routinely helped the class understand concepts and procedures.  The contributions of a student are valued independently of their grade, and independently of any other measure or category (ethnicity, social standing, mastery of formal language, etc).

I have not mentioned any teaching methods; pedagogy does matter … but the pedagogy follows from other ideas.  I can not use the key ideas above if all I do is ‘lecture’ (though I do a fair amount of that).  My class must provide a variety of interactions in order for my attitudes to be clear … and for all students to have opportunities to contribute.  Establishing a community is social navigation, so students need times to talk with each other in smaller groups as well as the entire class.

Here are some good articles and summaries of retention in mathematics and other STEM fields; these studies focus on retention in programs as opposed to courses … though there are obvious connections between the two.

1. Teaching For Retention In Science, Engineering, and Math Disciplines: A Guide For Faculty http://www.crlt.umich.edu/op25
2. Increasing Persistence of College Students in STEM  http://www.fgcu.edu/STEM/files/Increasing_Persistence_of_College_Students_in_STEM.pdf
3. Retaining Students in Science,Technology, Engineering, and Mathematics (STEM) Majors
   http://mazur.harvard.edu/sentFiles/Mazur_399966.pdf
4. Should We Still be Talking About Leaving? A Comparative Examination of Social Inequality in Undergraduate Patterns of Switching Majors http://wcer-web.ad.education.wisc.edu/docs/working-papers/Working_Paper_No_2014_05.pdf
5. Gender and Belonging in Undergraduate Computer Science: A Comparative Case Study of Student Experiences in Gateway Courses http://wcer-web.ad.education.wisc.edu/docs/working-papers/Working_Paper_No_2016_02.pdf

Success and retention starts with us, and depends upon both our attitudes and our professional knowledge.

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TPSE Math … Transforming Post Secondary Ed Mathematics

One of my Michigan colleagues recently reminded me of a national project on transforming post secondary education mathematics “TPSE Math”, which you can find at http://www.tpsemath.org/

This broad-based effort seeks to engage faculty and leadership from all segments of college mathematics, with an impressive leadership team.  I encourage you to check it out.

One of the first things I explored on their site deals with equity; they have a 2016 report on equity indicators (see http://www.pellinstitute.org/downloads/publications-Indicators_of_Higher_Education_Equity_in_the_US_2016_Historical_Trend_Report.pdf)  Interesting reading!

Another part of their web site I want to look at in more detail … “MAG” (Mathematics Advisory Group), which is focused on an ‘action oriented role’.  Take a look at http://www.tpsemath.org/mag

I’m expected that we will all be involved with this TPSE work, to varying degrees.

 
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