Seminar Talk: Computer Science Pedagogy - Miranda Parker

This week I have been co-hosting Miranda Parker, from Georgia Tech, as part of our Colloquium on Computer Science Pedagogy.  Her work is titled, Barriers to Computing: What Prevents CS for All.

The question is what are the barriers to accessing Computer Science at the high school level.  Nation-wide, ~35% of high schools offer at least one CS course, although this is self-reported CS.  In Indiana, the most popular courses are taken by ~5000 students, state-wide.  In Austin Texas, about 6000 students take at least one CS course, out of ~110,000.

How do we know if students are successful (i.e., did they learn)?

For this, we need a validated assessment to ensure that we are measuring what we want to measure.  An initial assessment, FCS1, worked fairly well and across multiple introductory programming languages; however, the assessment had limits in its use, which lead to the SCS1.  This assessment correlated well with FCS1, so it can standin; however, it was an hour long.  Assessments should: cover the content, vary in difficulty, and have good discrimination (so individual scores are good predictors for the overall performance).  In analysis, most of the SCS1 questions were hard, and few provided good discrimination.  The assessment was then adapted to focus on the medium difficulty problems that discriminate (in test scores), and expanded to a 30 minute test.

With a measurement of whether students are succeeding in Computer Science, we can walk back to investigate what things influence students to succeed in Computer Science.

Among prior studying factors, we know that students do better in CS if they have more prior experience with Computer Science, and students do better in CS (as well as STEM and general education) with higher socioeconomic status (SES).  There are also known prior links between SES and access to computing (whether it is formal courses, or informally), and SES to spatial reasoning.  And both of these later components are linked to CS achievement.

In exploratory study (large, public university with mainly high SES students), showed statistical correlation from spatial reasoning to CS achievement.  There was not correlation from having access to achievement.  In the interest of time, this point was not presented further.

Barriers to computing

There are three main sources of access to Computer Science courses: state policies, geography (such as, circumstances, industry, or neighboring schools with CS), and resources (such as, time and money or rural versus urban).

In partnership with Georgia Department of Education, CS enrollment data from 2012-2016, plus other public data (such as, characteristics of the county and of the school), for each of the 181 school districts in Georgia, where each district has at least one high school.  Using the definition of CS courses, being those that count toward the graduation requirement in Georgia as computer science, versus other computing courses, such as web design or proficiency with office.  In Georgia, out of 500,000 students, about 6000 took a CS course in a given year, where about 50% of schools offered computer science during at least one year in that time frame.  And the 6000 students are actually student course events, where a student taking two CS courses would count twice.

For those schools, CS enrollment, total high school enrollment, and median income contribute to whether CS will be offered in the next year.

This work is yet ongoing, where the next steps are to visit schools and collect further data on these factors, such as why a school discontinued a course offering, or now offers one.  Or what do students these courses go on to do?  An audience question wondered whether the CS courses offered relates to the courses offered of other parts of STEM.

Conference Attendance SIGCSE 2016 - Day 1

Here I am at SIGCSE again.  This is a wonderful opportunity to think and reflect on how I assist students in learning Computer Science and to be Computer Scientists.  And to connect with other faculty, researchers, etc who are interested in teaching and doing so in a quality manner.

An Examination of Layers of Quizzing in Two Computer Systems Courses -
In this work, the instructor taught the Intro Computer Systems course and based on Bryant and O'Hallaron's book (paid link).  After several years of teaching, she introduced a new layer of quizzing to the course.  Effectively before each class, students take a pre-quiz worth ~0% of their grade (20 quizzes combine to 5%), and can then come to class with knowledge and feedback toward their deficiencies.  From the experience of the quizzes, students have been doing better in these courses.

Subgoals Help Students Solve Parsons Problems -  (previewed at Mark Guzdail's blog)
When learning new things, students benefit from labeling subgoals in solving.  These labels provide a basis for solving similar problems.  There are two different strategies for labeling: students can provide the labels or the assignment can provide the labels.  An example labeling can be found with loops: initialize, test, change.  If students provide the labels and provide cross-problem labels, they do best.  If they provide the labels and they are problem-specific such as "are there more tips" (with respect to an array of tips), then these students do worse than those provided the labels.  Developing labels can be valuable, but it may require the expert to still provide guidance to help abstract them across problems.  This talk had one of the great moments when someone asked a question and Brianna replied by, "So and so has done great ..."  And the questioner pointed out that he is "so and so".

As CS Enrollments Grow, Are We Attracting Weaker Students?: A Statistical Analysis of Student Performance in Introductory Programming Courses Over Time -
In this study, one instructor has analyzed the data of student assignment grades across 7 years of Fall semesters in the CS 1 course.  Several specific and clear reasonings were applied to get a clear and comparable data set.  The first test is that the number of student withdrawals remained the same as a percentage of the total class size.  The second test is that the means of the grades for the courses are statistically indistinguishable.  The third test is to use a mixture model (weighted combination of distributions) for each class's scores.  A good fit is found with two gaussian distributions, such that there is one for the "good students" and a second for the high variance students who are "potentially weaker".  From this, the study concluded that (at Stanford, in Fall CS1), there are more "weak students" and more "strong students" as the student enrollment is drawing from the same larger population.

A (Updated) Review of Empiricism at the SIGCSE Technical Symposium -
Using the proceedings from SIGCSE 14 and 15, they examined the empirical evaluation and the characteristics of these evaluations.  How was the data collected in each paper?  And what was being evaluated (pedagogy, assignments, tools, etc)?  Is the subject novel or replicating other studies?  Based on this study, would SIGCSE benefit from a separate track for longer paper submissions?  Or workshops on how to empirically validate results?  This and other material is being developed under an NSF grant and released publically.

Birds of a Feather -
In the evening, I attended two Birds of a Feather sessions.  Both of which have given me further ideas for what I might do to further (attempt to) improve student learning.  And also possible collaborators toward that end.