Conference Attendance - SIGCSE 2019 - Day 2.5

Continuing at SIGCSE, here are several more paper talks that I attended on Friday.  Most of the value at SIGCSE comes from the friendly conversations with other attendees.  From 5-11p, I was in the hotel lobby talking with faculty and students.  Discussing research ideas, telling our stories from teaching, and generally renewing friendships within the field.

On the Effect of Question Ordering on Performance and Confidence in Computer Science Examinations
On the exams, students were offered a bonus if they could predict their score by within 10%.  Does the order of questions (easy -> hard, or hard -> easy) have any impact on their estimated or actual performance on an exam.  Students overpredicted by over 10% on the exams.  As a whole, the hard to easy students did worse, but this result was not statistically significant.  A small improvement is gained for women when the exams start with the hardest problem.

I wonder about whether students were biased in their prediction based on the reward.  Ultimately, the authors gave the reward to all students regardless of the quality of their prediction.

The Relationship between Prerequisite Proficiency and Student Performance in an Upper-Division Computing Course

We have prerequisites to ensure that students are prepared for the later course, an upper-level data structures class.  Students started on average with 57% of expected prerequisite knowledge, and will finish the course with an improvement of 8% on this knowledge.  There is a correlation between prerequisite score and their final score.  With several prerequisites, some knowledge concepts has greater correlation than others.  Assembly is a surprising example of a concept that relates.  Students benefit from intervention that addresses these deficiencies early in the term.

Afterward, we discussed that this work did not explore what prerequisite knowledge weakly correlated with student learning.  How might we better understand what prerequisites actually support the learning in a course?  Furthermore, can we better understand the general background of students in the course, such as class standing or general experience?

Visualizing Classic Synchronization Problems

For three classic synchronization problems: dining philosophers, bounded producer-consumer, and readers and writers.  Each one has a window displaying the operations, as well as multiple algorithmic strategies.  With these visualizations, do students learn better and also find them more engaging than reading about the problems in the textbook.  While not statistically significant, the control group exhibited better recall, although the visualization group had higher engagement.  That said, the control group exhibited higher course grades, so the difference in learning may actually be from unrelated factors.

Conference Attendance: SIGCSE 2019 - Day 1.5

Back at SIGCSE again, this one the 50th to be held.  Much of my time is spent dashing about and renewing friendships.  That said, I made it to several sessions.  I've included at least one author and linked to their paper.

Starting on day 2, we begin with the Keynote from Mark Guzdial

"The study of computers and all the phenomena associated with them." (Perlis, Newell, and Simon, 1967).  The early uses of Computer Science were proposing its inclusion in education to support all of education (1960s).  For example, given the equation "x = x0 + v*t + 1/2 a * t^2", we can also teach it as a algorithm / program.  The program then shows the causal relation of the components.  Benefiting the learning of other fields by integrating computer science.

Do we have computing for all?  Most high school students have no access, nor do they even take the classes when they do.

Computing is a 21st century literacy.  What is the core literacy that everyone needs?  C.f. K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals.  Our goal is not teaching Computer Science, but rather supporting learning.

For example, let's learn about acoustics.  Mark explains the straight physics.  Then he brings up a program (in a block-based language) that can display the sound reaching the microphone.  So the learning came from the program, demonstration, and prediction.  Not from writing and understanding the code itself.  Taking data and helping build narratives.

We need to build more, try more, and innovate.  To meet our mission, "to provide a global forum for educators to discuss research and practice related to the learning and teaching of computing at all levels."

Now for the papers from day 1:

Lisa Yan - The PyramidSnapshot Challenge

The core problem is that we only view student work by the completed snapshots.  Extended Eclipse with a plugin to record every compilation, giving 130,000 snapshots from 2600 students.  Into those snapshots, they needed to develop an automated approach to classifying the intermediate snapshots.  Tried autograders and abstract syntax trees, but those could not capture the full space.  But!  The output is an image, so why not try using image classification.  Of the 138531 snapshots, they generated 27220 images.  Lisa then manually labeled 12000 of those images, into 16 labels that are effectively four milestones in development.  Then, a neural network classifier classified the images.  Plot the milestones using a spectrum of colors (blue being start, red being perfect).  Good students quickly reach the complete milestones.  Struggling students are often in early debugging stages.  Tinkering students (~73 percentile on exams) take a lot of time, but mostly spend it on later milestones.  From these, we can review assignments and whether students are in the declared milestones, or if other assignment structure is required.

For the following three papers, I served as the session chair.

Tyler Greer - On the Effects of Active Learning Environments in Computing Education

Replication study on the impact of using an active learning classroom versus traditional room.  Using the same instructor to teach the same course, but using different classrooms and lecture styles (traditional versus peer instruction).  The most significant factor was the use of active learning versus traditional, with no clear impact from the type of room used.

Yayjin Ham, Brandon Myers - Supporting Guided Inquiry with Cooperative Learning in Computer Organization

Taking a computer organization course with peer instruction and guided inquiry, can the peer instruction be traded for cooperative learning to emphasize further engagement and learning.  Exploration of a model (program, documentation), then concept invention (building an understanding), then application (apply the learned concepts to a new problem).  Reflect on the learning at the end of each "lecture".  In back-to-back semesters, measure the learning gains from this intervention, as well as survey on other secondary items (such as, engagement and peer support).  However, the students in the intervention group did worse, most of which is controlled by the prior GPA.  And across the other survey points, students in the intervention group rated lower.  The materials used are available online.

Aman, et al - POGIL in Computer Science: Faculty Motivation and Challenges

Faculty try implementing POGIL in the classroom.  Start with training, then implementing in the classroom, and continued innovation.  Faculty want to see more motivation, retaining the material, and staying in the course (as well as in the program).  Students have a mismatch between their learning and their perceived learning.  There are many challenges and concerns from faculty about the costs of adoption.

Repost: CRA Memo on Best Practices for Engaging Teaching Faculty in Research Computing Departments

Mark Guzdial noted today that the CRA has prepared its memo about teaching faculty in research departments.  For the past two years, I have been going to a CRA event at SIGCSE geared toward preparing this memo, so good to see its release.  I am thankful that at my institution, we have most of the things outlined in the memo and are treated roughly as equals to the tenure-track faculty.

Conference Attendance SIGCSE 2018

I have just finished attending SIGCSE 2018 in Baltimore.  In contrast to my earlier conference attendance, this time I have had higher involvement in its execution.

On Wednesday I went to the New Educator's Workshop (NEW).  Even being faculty for two years, there was still a number of things that were either new or good reminders.  Such as including or discussing learning objectives with each lecture and assignment, or being careful with increasing one's level of service.  As a new faculty member, each service request seems exciting, as no one has asked me before!  But many senior faculty emphasized that this is the time in which they are protecting us from lots of service opportunities such that we can spend time on our teaching and research.

On Thursday morning, I presented my recent work that updated a programming assignment in Introduction to Computer Systems, and from which we saw improvements in student exam scores.  We did not research the specific action, and are therefore left with two theories.  First, the improvement could be from using better style in the starter code and emphasizing this style in submissions.  Second, we redesigned the traces to require submissions to address different cases and thereby implement different features.  I lean toward the formed, but have no data driven basis for this hypothesis.

Let's discuss active learning briefly.  I attended (or ran) several sessions focused on this class of techniques.  The basic idea is that students have better engagement and learning by actively participating in class.  There are a variety of techniques that work to help increase student activity.  On Thursday afternoon, Sat Garcia of USD, presented Improving Classroom Preparedness Using Guided Practice, which showed how student learning improved from participating in Peer Instruction, which particularly requires students to come to class prepared.  Shortly later, Cynthia Taylor joined Sat and I in organizing a Bird of Feather (BoF) session on using Active-learning in Systems Courses.  We had about 30-40 attendees there split into two groups discussing some techniques they have used and problems they have observed.  5 years ago, a similar BoF had attendance around 15-20, so we are making progress as a field.

On Friday, I spoke with Brandon Myers who has done work on using POGIL in Computer Organization and Architecture.  In POGIL, students are working in groups of 3-4 with specific roles through a guided learning, guiding students into discovering the concepts themselves.  We had a nice conversation and may be merging our draft resources.  This last point is often the tricky part of using active learning in that developing reasonable materials can be both time intensive and requires several iterations.

The Friday morning keynote presentation was given by Tim Bell, who spoke about K-12.  This topic is rather distant from my own work and research, so I was skeptical.  Yet, I came out quite enthused.  It was interesting to think about presenting Computer Science concepts in non-traditional ways, based initially on having to explain your field at elementary school when the other presenters are a cop and a nurse (his example).  How could you get 6 year olds to sort?  Or see the advantage of binary search as the data grows?

In the afternoon, I was a session chair for the first time.  I moderated the session on Errors, so obviously the AV system stopped working for a short duration.  Beyond that incident, the session seemed to go well.

I always like going to SIGCSE.  It is rejuvenating and exhausting.  So many teachers to speak with about courses, curriculum, and other related topics.  And then you find that you've been social for 16 hours or so hours.

Conference Attendance SIGCSE 2017 - Day 1

Here in Seattle, where I used to live, attending SIGCSE 2017.

Exposed! CS Faculty Caught Lecturing in Public: A Survey of Instructional Practices - Postsecondary Instructional Practices Survey (24 items), 7000 CS faculty invited, about 800 responses. If the evidence is clear that active-learning is better for instruction, then we should be doing that more. The overall split for CS was equal between student-centered and instructor-centered (exactly same avearge, 61.5). The survey showed clear differences between non-STEM (student) and STEM (instructor). So CS is doing better than its overall group.

Now, to dig into which differences there are in the demographics. The major difference in instructors is women, and those with 15 years of experience versus 30, both showing a 5+ point difference between student and instructor centered. However, 60s are still "whatever" and are not strongly committed. For those who are strongly committed, there are about 20% for each, while the remaining 60% are whatevers.

Investigating Student Plagiarism Patterns and Correlations to Grades - What are some of the patterns of the plagiarism, such as parts or all and how do students try to obfuscate their "work". Data from 2400 students taking a sophomore-level data structure course. After discarding those assignments with insufficient solution space, four assignments remained from six semesters. Used a plagiarism detector, to find likely cases of cheating.

First, even though the assignments remained unchanged, the rate of cases stayed constant. Most cases involved work from prior semesters. About two thirds of students who cheated, did so on only one assignment. Second, the rate of cheating on the individual assignments was similar to the partner assignment. Third, while students who cheated did better on those assignments, but they did not receive perfect scores and that those cheating did worse in the course than those who did not. And that those who took the follow-on course showed a larger grade difference (p=0.00019). Fourth, the analysis used the raw gradebook data that is independent of the detection and result of that detection.

Six detectors used. Lazy detector (common-case, no comments or whitespace), Token-based (all names become generic, sort functions by token length): identical token stream, modified token edit distance, and inverted token index (compute 12-grams and inversely weight how common these are). "Weird variable name" (lowercase, removed underscores). Obfuscation detector (all on one line, long variable names, etc). Fraction of total cases found by each detector: 15.69%, 18.49%, 49.71%, 72.77%, 67.35%, 0.38%.

Repost: How I spent my time at SIGCSE

When I attend a conference, I try to prepare blog posts detailing the presentations that I see and other items of objective content.  More important are the people in attendance.  I spend more time meeting with colleagues than I do actually sitting in the sessions.  Before you are shocked, understand that much of our conversation are about these sessions.  Depending on the conference there can be between 2 and 10? sessions occurring concurrently.  I cannot be in 2, let alone 10, places at once, so instead we sample (as in the appearance of randomly selecting) the sessions and then discuss.

I met Janet Davis, who now heads the CS program at Whitman College, during my first time at SIGCSE.  I value her different perspective and always try to seek her out at some point during the conference.

These themes merge in her blog post that shows some idea as to how each day is organized and how the sessions (papers, panels, etc) play a part in a very busy schedule.

Conference Attendance SIGCSE 2016 - Day 2

After lunch when we are all in food comas, let's attend the best paper talk!
A Multi-institutional Study of Peer Instruction in Introductory Computing -
This study followed 7 instructors across different institutions as they used peer instruction.  This showed that both the instruction is generally recognized as valuable, while also touching on routes in which it can go awry.  Tell students why this technique is being used and what it's effect.  Hard questions are good questions to ask, as students will discuss and learn from the question.  This requires that questions are graded for participation and not *correctness*.  Possible questions and material for peer instruction is available.

Development of a Concept Inventory for Computer Science Introductory Programming -
A concept inventory is a set of questions that carefully tease out student misunderstandings and misconceptions.  Take the exams and identify both the learning objective and the misconception that results in incorrect answers.

int addFiveToNumber(int n)
{
  int c = 0;
  // Insert line here
  return c;
}

int main(int argc, char** argv)
{
  int x = 0;
  x = addFiveToNumber(x);
  printf("%d\n", x);
  return 0;
}

a) scanf("%d", &n);
b) n = n + 5;
c) c = n + 5;
d) x = x + 5;

Each incorrect answer illustrates a different misconception.  For example, input must come from the keyboard.  Or variables are passed by reference.
Overall, this study illustrated how the concept inventory was developed, but not the impact of having it, or what it showed in the students and their learning.

Uncommon Teaching Languages - (specifically in intro courses)
An interesting effect of using an uncommon language in an introductory course is that the novices and experts have similar skills.  Languages should be chosen to minimize churn, otherwise students feel that they haven't mastered any languages.  And related to this point, languages also exist in an institutional ecosystem.  Furthermore, we want to minimize the keywords / concepts required for a simple program.  A novice will adopt these keywords, but they also are "magic" and arcane.  And then how long are the programs, as we want novices to only have to write short code to start.

I also attended the SIGCSE business meeting and then the NCWIT reception.  I have gone to NCWIT every year at SIGCSE, as I want to know what I should do (or not do) to not bias anyone's experience in Computer Science.

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.

Repost: Incentivizing Active Learning in the Computer Science Classroom

Studies have shown that using active learning techniques improve student learning and engagement.  Anecdotally, students have brought up these points to me from my use of such techniques.  I even published at SIGCSE a study on using active learning, between undergraduate and graduate students.  This study brought up an interesting point, that I will return to shortly, that undergraduate students prefer these techniques more than graduate students.

Mark Guzdial, far more senior than me, recently challenged Georgia Tech (where we both are) to incentivize the adoption of active learning.  One of his recent blog posts lists the pushback he received, Active Learning in Computer Science.  Personally, as someone who cares about the quality of my teaching, I support these efforts although I do not get to vote.

Faculty members at R1 institutions, such as Georgia Tech, primarily spend their time with research; however, they are not research scientists and therefore they are being called upon to teach.  And so you would expect that they would do this well.  In meeting with faculty candidates, there was one who expressed that the candidate's mission as a faculty member would be to create new superstar researchers.  Classes were irrelevant to this candidate as a student, therefore there would be no need to teach well as this highest end (telos) of research justifies the sole focus on students who succeed despite their instruction, just like the candidate did.  Mark's blog post suggests that one day Georgia Tech or other institutions may be sued for this sub-par teaching.

What about engagement?  I (along with many students and faculty) attended a visiting speaker talk earlier this week and was able to pay attention to the hour long talk even though it was effectively a lecture.  And for this audience, it was a good talk.  The audience then has the meta-takeaway that lectures can be engaging, after all we paid attention.  But we are experts in this subject!  Furthermore, for most of us there, this is our subfield of Computer Science.  Of course we find it interesting, we have repeatedly chosen to study it.

For us, the material we teach has become self-evidently interesting.  I return to the undergraduate and graduate students that I taught.  Which group is closer to being experts?  Who has more experience learning despite the teaching?  Who prefered me to just lecture?  And in the end, both groups learned the material better.

Edit: I am by no means condemning all of the teaching at R1's or even Georgia Tech.  There are many who teach and work on teaching well.  The Dean of the College of Computing has also put some emphasis on this through teaching evaluations.  Mark's post was partially noting that teaching evaluations are not enough, we can and should do more.

Conference Attendance SIGCSE 2015 - Day 2 / 3

I recognize that Day 1 afternoon went “missing”.  I presented my poster and that consumed the sum total of my time.  While I am happy with all that I achieved with my poster (writing IRB protocol, independent work, analyzing my teaching, et cetera), it was not considered as a finalist for the student research competition (SRC). Yet I received significant feedback and a number of follow-ons that I will have to try to evaluate the next time(s) I teach.  I have been doing an excellent job of networking and speaking with my colleagues.  And I have seen several exciting techniques to improve my teaching.

BOF2: Preparing Undergraduates to Make the Most of Attending CS Conferences

This was a small Bird of the Feather (BoF), but we discussed some approaches and things to know before attending your first conference, and not just for undergraduates.  However, in some cases, undergraduates are attending their local institution and may never have traveled any significant distance.  What do they really need to bring?



In traveling, take some time to prepare students.  Let them know what to expect.  For example, it is okay to miss some paper sessions, and even return to your room entirely.  It is okay to ask questions 1:1.  Find groups where people are being introduced and join in.  Student volunteering, while takes time, also gives an additional individuals that you will know.  Use the people you know to introduce you to others at the conference.

MIPSUnit: A Unit Testing Framework for MIPS Assembly

This is just what it sounds.  A Ruby based framework that enables writing simple unit tests that will then be applied to a full simulation of the assembly executed.

A Web-based Simulator for Learning Multiprocessor Real-time Scheduling

The presenter(s) were not at this poster, but it showed a high quality interface for seeing the scheduling of threads according to different scheduling policies.  The intent here was not to explore races and parallelism, but rather see how scheduling decisions are made in an OS.

Raspberry HadooPI: A Low-Cost, Hands-On Laboratory in Big Data and Analytics

I was not expecting this poster.  You are walking along and then see 4 Raspberry Pi's all networked together.  Raspberry Pis and HPC?!  A small setup, but it is an interesting development that takes advantage of the low cost Pi and still provide an HPC platform for students.

Building the Pascaline: Digital Computing Like It's 1642 

Plastic parts all worked together to form replicas of Pascal's mechanical calculator.  Interesting and student assembled.

On the Evaluation of Student Team Software Development Projects

Teams of 4 students, approach is evaluated on courses from three years of major (sophomore on up).  Teams are formed with CATME (particularly using dissimilar GPAs in a group), as well as partner selection (when possible).  Students provide peer evaluations after each stage of the project.  Significant data collection looking particularly at what students prefer for to be the evaluation policy (between 100% of grade for the group’s work to 100% of the grade for the individual’s contribution).  This question was taken repeatedly throughout the semester, which leads to whether student preferences change?  More senior students prefer more weight being attributed to group.  The predictor for what grade split is at what point in the course is this surveyed, and effectively as soon as the teams are formed the students prefer to be graded primarily as a group.  Follow on study is looking at experience with team projects, trust in the ability to evaluate individual contribution, and other questions.  This is a hopeful data point.

What Influences CS Faculty to Adopt Teaching Practices?

How do faculty become aware and why do they try out teaching practices?  66 participants in CS, including chairs, tenure-track faculty, teaching faculty, and Ph.D. student instructors across 36 institutions.  First, the mental model of what an instructor does can differ significantly from what the instructor is actually doing.  Second, faculty can find out about practices through a variety of approaches, such as self-identifying that there is possible improvement in their teaching.  Faculty often trust other faculty like them (researchers to researches, lecturers to lecturers).  Third, when adopting a practice, faculty need to evaluate the effectiveness (see also my poster, student feedback, etc).  -- My efforts in this have been having different faculty (my recommendation letter writers) view my lectures / teaching, and thereby giving them demonstrations of different practices.

Personalized Attention @ Scale

"We lost the war on cheating"  Instead, we have to meet with students such that they are demonstrating their understanding of the code.  The requirements of submissions: attribute your sources and understand your submission.  Enables students to work together, use all sources, develop interview skills.  Enables reuse of assignments.  Grading is now 40% correctness / 60% code interview.  Rubric for each interview.  Students should arrive early and have their laptop ready to present / explain.  Students were better able to learn and complete the assignments, as well as feedback for improvement.  Students also felt better able to learn the material by being able to collaborate and not constrained by a collaboration policy.  There are some stressors, such as TAs having to meet with hundreds of students, as well as their inconsistencies.  -- This was perhaps the most exciting new technique that I saw / heard about.

Conference Attendance SIGCSE 2015 - Day 1 Morning

It is colder here in Kansas City.  Fortunately, I will only be outside briefly.  Most often I will be networking and continuing my efforts to both become a better teacher, as well as finding an academic job teaching.

This morning, I am focusing on the "Curriculum" track.  I am excited by the three papers in this track, the first looks at research, the second is on systems courses, and the last on parallel computing courses.  Alas, I was in the hallway track and missed the first work.  Perhaps I can find the authors later.

Backward Design: An Integrated Approach to a Systems Curriculum
The goal of systems is "higher level software creation".  Computer Science courses are split into Core Tier 1 and Tier 2 (a term from the ACM 2013 curriculum), where the former are taken by all CS majors and the later are only taken by most or some.  One issue in the old curriculum was that OS also taught C.  In crafting a new curriculum, first establish a vision statement, which can be used in conflict resolution (and also revised).  Establish SMART objectives to prepare and build the assessments.  The results can be found on github.

A Module-based Approach to Adopting the 2013 ACM Curricular Recommendations on Parallel Computing
Parallel computing is important and important for CS graduates to know.  The 2013 ACM Curriculum increased the number of hours that students should take in parallel computing.  Part of the recommendations are to place parallel computing into the curriculum and not just as a course.  Thus parallelism modules are placed throughout the curriculum (perhaps as early as CS1 or CS2).  Find the level of abstraction for a concept and introduce it appropriately.  For example, Amdahl's Law in CS1 versus cache coherence in senior-level class.  5 modules of parallelism were established, which have equivalences with the ACM.  Each course in the curriculum may have 1 or more modules, which then teaches and reinforces the topics.  Even after adding these modules, there has continued to be incremental development and revisions, which have improved student outcomes.  The key take away is that it is possible to introduce these recommendations without completely rewriting the curriculum.

In the afternoon, I will be standing with my poster -  Using Active Learning Techniques in Mixed Undergraduate / Graduate Courses.  Later I will post updates from my afternoon.

Conference Attendance SIGCSE 2014 - Day 3

Today the day will be in reverse. We'll start with papers and end with the invited speaker. I have met many attendees and even talked to some of them. Let's start with operating systems and programming languages. With the bonus theme of avoiding using Linux for presentations.

Teaching OS through code review. Unified grading workflow with git, the student submissions are viewed as diffs and the grading is via online code review. Most students preferred this system over past solutions and tools. The system also supported incremental reviews / checkpoints. The GradeBoard tool is built on review board and git.

Virtual graphics card in qemu for teaching device driver design. Graphics is selected such that students would clearly see the results. Providing a device through a virtual machine significantly reduced the difficulties for instructors as well as for students. Minimal time required to restore student "machines" when they break. Most students completed the project versus earlier versions based on kernel intercepts.

A programming language compiler compiler. Earlier versions of the class require teaching scheme before students could implement their interpreter / compiler. Now based on java, the tool plcc processes provided lexical and grammar files, so that students can then interface with the java classes. Plcc only supports LL1 languages. Students implement simple interpreted languages.

And then it was time to network again, i.e. the hallway session. This continues to be an interesting expense for an introvert, yet it is also the exponential networking exercise. After I know more people, then it is more likely that I find a group in which that I know someone and can meet others. I've made progress with knowing the participants in my "field". And having more inspiration for teaching is summer.

Conference Attendance SIGCSE 2014 - Day 2

Well rested, it is time for conference again! Keynote today by code.org. When teaching someone programming, you don't tell them this is overloading or this is event handling, but instead the base concept. CS is starting to count for high school graduation requirements at the state level, but districts and universities are slower to change. CSEd Week is Dec 8 - 14 this year, for another Hour of Code. The first one had impressive results (including almost 50/50 male / female ratio) and the key thing is that this hour is providing a foot in the door. So the hour is meant as a just a start and 97% of teachers rated the hour positively.

Adding parallel programming in CS2. Students are taught OpenMP pragmas as applied to for loops. Projects assigned around matrix operations and image processing. Part of the teaching is done through live coding, which is based on demoing patternlets. Students see this component as exciting and fresh. All problems are restricted to those not requiring synchronization. (see Csinparallel.org).

Board game strategy development in CS2. Instructors provide the engine, which provides the graphics and true game state. Students write a player that maintains its representation of the state and decides on a move. In my CS3? we had a similar project with reversei / othello as the game. Then for research, half of the students were assigned to develop components in the game engine and other students developed the players. Students developing players had higher enjoyment and felt they learned more, although there was little difference in grades.

I also visited several posters that were interesting.  In one, they studied why students dropped out of CS1 courses.  Only two measures were statistically significant: first, how much computer science experience a student had before taking the class, and second, how busy (total work, not just credits) the student was that semester.  Switching to active-learning had no real effect.  Gender made no difference.  Intention of majoring in computer science was not a factor.

The other poster looked at measuring the style of the code in CS1 assignments automatically.  They found that their tool was able to cluster the student submissions based on stylistic similarity and that grades for each cluster had a 90% confidence.  I'm intrigued!  Style is important and being able to emphasize style further is great.

And then I talked with other attendees for many hours, which is one of the reasons that I'm there.

Conference Attendance SIGCSE 2014 - Afternoon Day 1

Concept inventory for Operating Systems! Develop an open concept inventory for a wider collection of classes, starting with an OS course. Work toward scenario questions to avoid issues with terminology. Consider page replacement, instead phrased about textbooks on a desk. Identify the concepts that are not intuitive by which answers have a higher percentage correct.  They have a public share.

Process oriented guided inquiry learning in CS1. How pogil differs from active learning? Self managed teams with roles and they work through inquiry based activities, with the instructor as the facilitator. Maintain group composition over several weeks, while rotating roles. Split into two pairs for programming exercises. Both information retention (between CS1 and CS2) and female pass rates have improved. They noted a website carrying many developed resources.

Learning how to teach big data (at the middle school). A narrative game based environment to solve problems via pair programming. Worked first with middle school teachers to learn the CS concepts and then working with them to understand how to teach the students about big data. I'll need to read the paper to better follow this work, yet I am favorable toward pushing more CS content into earlier classes.

Assessment model for large project courses. How do you assess students on a large project when the students have different roles and focuses? Assessment variations: Formative vs summative. Teacher vs student. Group vs individual. Each project group is around 30 students. Grading criteria, oral feedback from instructor at student meetings, coaching from fellow students (code reviews, hackathons, etc), on demand artifacts, student reports (including contribution and time spent), individual teacher assessments (using a rubric), then the final feedback report for the group, and the option of interviews with individual students. And a final retrospective lead by the group. Most students are happy to have their grade based on the group performance. Few saw value of the reflective report.

A repository of novice programmer activity. Two million unique users using BlueJ every year. Blackbox collects anonymous data on the users. Data on each programming session, like compilation including result, line by line diffs of any edits. Then an interesting small analysis on the most common errors and how common the errors are over the duration of a course.

ACM exemplar course integrating fundamentals, programming languages, and software engineering. The problem is covering the increasing diversity of computing, yet reducing the credits required for a degree. This necessitates an integrated approach. The presentation followed with a description of the course, which showed the transitions between the integrated concepts. Several closed lab exercises exist to ready students for subsequent lectures.

Now it is time for the birds of a feather sessions. I am told that in the past there had been a session for students looking for a job, but there wasn't one this year. Still, I am intrigued by active learning in systems courses!

Conference Attendance SIGCSE 2014 - Morning Day 1

Here I am attending a Computer Science education conference. Started off with an interesting keynote, a break and meet a couple of faculty, and now the first paper session.

How to integrate software engineering into upper-level undergraduate courses? A project centered course, which included readings of selected research, as well as visiting local software development companies. Surveying the students before and after the class, and student confidence went down after the course, because students had learned how difficult the problems are. Yet students were more engaged into learning more about the subject and the resulting projects were of a higher quality.

Using real projects in software testing. Students, in teams, select a real world project. They develop a test plan, provide a progress report (requested by the students), and a final presentation. The instructor is both a customer and a coach. Able to work with the project developers. Target is generally low hundreds of classes in the project. Most students enjoyed the project and enrollment has increased. Students fill out a 360 survey on their teammates, and instructor intervention for outliers both positive and negative.

Student code is not throw aways. Best paper award. Prior work on software maintenance has usually relied on artificially prepared code, including lecturer added bugs. For this work, the code developed by prior seniors (11kloc, java, multithreaded) was provided to juniors in an intermediate version, who then added a feature and fixed bugs. Do students then follow proper practices? Most did, but a minority concluded, for example, that the code could not be tested. Many students observed the importance of quality design and had the experience of working on someone else's code base.