Conference Time: SIGCSE 2017 - Day 2

I started my morning by attending my regular POGIL session.  I like the technique and using it in the classroom.  However, I should probably make the transition, attend the (all / multi-day) workshop, and perhaps get one of those "ask me about POGIL" pins.

Lunch was then kindly provided by the CRA for all teaching-track faculty in attendance.  There is the start of an effort to ultimately prepare a memo to departments for how to best support / utilize us (including me).  One thing for me is the recognition of how to evaluate the quality of teaching / learning.

Micro-Classes: A Structure for Improving Student Experience in Large Classes - How can we provide the personal interactions that are valuable, which enrollments are large / increasing?  We have a resource that is scaling - the students.  The class is partitioned into microclasses, where there is clear physical separation in the lecture room.  And each microclass has a dedicated TA / tutor.  Did this work in an advanced (soph/ junior) class on data structures?

Even though the same instructor taught both the micro and the control class, the students reported higher scores for the instructor for preparedness, concern for students, etc.  Yet, there was no statistical difference in learning (as measured by grades).

Impact of Class Size on Student Evaluations for Traditional and Peer Instruction Classrooms - How can we compare the effectiveness of peer instruction being using in courses of varying class sizes?  For dozens of courses, the evaluation scores for PI and non-PI classes were compared.  There was a statistical difference between the two sets and particularly for evaluating the course and instructor.  This difference exists even when splitting by course.  This difference does not stem from frequency of course, nor the role of the instructor (teaching, tenure, etc).

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: 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.

PhD Seminar - teaching, writing and oral presentation skills

Every month, here are Georgia Tech we are getting together to cover some of the things that PhD students should know / learn during their studies.  This month we gathered to cover various communication skills.  I have copied the points from today's seminar:

Teaching Advice

• Strive for engagement
• Trust your expertise
• Conference Talk != Teaching
• Get Students to Talk

Writing Advice

• Write for a general audience occasionally
• Read to find models of writing
• Book Recs: Oxford Guide to Plain English / Bugs in Writing
• It is a process
• Find out what motivates you to write
• Inside-Out style
• Pay attention to copyediting and learn

Presentation Advice

• Motivate them to want to read the paper
• The audience doesn't know what you don't tell them
• Toastmasters (or Techmasters at GT)
• Slides as a reminder
• Eye contact
• Repeat the question (everyone has heard it and you know what is being asked)

Liberal Arts College Positions Mini-Seminar

In continuing my search and preparation for a faculty position, today I attended a mini-seminar by LADO faculty.  (LADO - liberal arts colleges with diversity officers)  Here are some points that were raised during the breakout and panel discussions:

Teaching:
- You will teach both upper-level courses, as well as "service" courses.  Service is a good term to describe the low-level / introductory courses, as the faculty are rotating through this service to the department.
- Try to setup the teaching schedule so that 1 day is free solely from research.
- Continue to revise courses so they are fresh and current, but also avoid constantly creating all new courses.
- Valuable to set aside "non-office hours" times, during which the door can be shut.
- Faculty will sit in on courses and additionally interview the students, as part of composing an evaluation of your teaching.

Research:
- Recruiting undergraduates earlier for research to have time to train them, so that they will later be able to contribute.
- You can still collaborate and have broad impact through research with faculty at other more research-focused institutions.
- Grant proposals can also be keyed "RUI" (research at undergraduate institutions)
- Regular funding for sabbatical leaves, first often after the renewal of the 3-year contract.  This leave is focused on research and may be held at R1 or other institutions.
- Startup package is present to cover the transition to grant-based funding.
- Research lab costs are significantly lower at these institutions, as funds are not required for grad students, post docs, etc.
- Schools are looking for faculty hires that add diversity to the research available.

Service:
- Service is a component, but is much smaller than teaching and scholarship.  So be cautious about accepting invitations to committees, in terms of time commitment.  The service time can provide valuable insight to the functioning of the institution, as well as possible collaboration with collegues in other departments.
- You will be chair of your department someday.

Other:
- Many liberal arts institutions are located in small towns.
- Take the time to customize the cover letter.  Do you really want and care about this job?

Preparing for Academic Jobs

I went to a recent seminar about the preparation and practice of finding an academic job.  The following summarizes the answers given by the panelists, each of whom was giving his or her opinion.  The short version is that your letters of recommendation are key.  They are the summary of your skills and qualifications by your (future) peers.  The panelists are all research-oriented faculty, which may skew some of the opinions provided.  One quality resource on teaching jobs can be found here.

Most important things in a candidate:
- Publications (some in the right places)
- Letters (don't really lie)
- Fulfilling the needs of the department
- Put "top" school in middle of interview schedule, chance to work out mistakes but not be burned out
- Energized / excited about place
- In 1:1 with faculty, only discuss own research for half of time (~15min)
- Be formal (jacket, etc)
- Prep work with faculty letter writers (explain research, plans, etc)
- Ability to connect across areas (your own area will get you the interview, the other areas will get you the offer)
- Talent, passion, impact in research
- Have a set of questions for 1:1 time of "do you have any questions?"

Things to avoid:
- Wrong / bad job talk (did you target the right audience, and yet convey knowledge in subfield)
- Attitude (arrogance that job is yours, or desperation about finding a job)
- Two interviews in one week

Letters of Recommendations:
- Especially letters from externals
- Prepare a statement of contributions (what have you really done / achieved?)

Things to focus on:
- Take risks in your research
- Network and get your name out / aware of

Postdoc versus Second Tier:
- Find collaboration and mentoring in a postdoctoral position
- It depends

Non-Research:
- Still except some quality research
- Your research talk is a demonstration of teaching

Deciding on schools to apply:
- Location
- Areas of Focus

Packages:
- Find packages from previous applicants