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).
Showing posts with label teaching. Show all posts
Showing posts with label teaching. Show all posts
Saturday, March 11, 2017
Thursday, March 3, 2016
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.
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.
Thursday, December 17, 2015
Teaching Inclusively in Computer Science
When I teach, I want everyone to succeed and master the material, and I think that everyone in the course can. I only have so much time to work with and guide the students through the material, so how should I spend this time? What can I do to maximize student mastery? Are there seemingly neutral actions that might impact some students more than others? For example, before class this fall, I would chat with the students who were there early, sometimes about computer games. Does those conversations create an impression that "successful programmers play computer games"? To these questions, I want to revisit a pair of posts from the past year about better including the students.
The first is a Communications of the ACM post from the beginning of this year. It listed several seemingly neutral decisions that can bias against certain groups. Maintain a tone of voice that suggests every question is valuable and not "I've already explained that so why don't you get it". As long as they are doing their part in trying to learn, then the failure is on me the communicator.
The second is a Mark Guzdial post on Active Learning. The proposition is that using traditional lecture-style advantages the privileged students. And a key thing to remember is that most of us are the privileged, so even though I and others have "succeeded" in that setting, it may have been despite the system and not because of the teaching. Regardless of the instructor, the teaching techniques themselves have biases to different groups. So if we want students to master the material, then perhaps we should teach differently.
Active learning has a growing body of research that shows using these teaching techniques help more students to succeed at mastering a course, especially the less privileged students. Perhaps slightly less material is "covered", but students will learn and retain far more. Isn't that better?
The first is a Communications of the ACM post from the beginning of this year. It listed several seemingly neutral decisions that can bias against certain groups. Maintain a tone of voice that suggests every question is valuable and not "I've already explained that so why don't you get it". As long as they are doing their part in trying to learn, then the failure is on me the communicator.
The second is a Mark Guzdial post on Active Learning. The proposition is that using traditional lecture-style advantages the privileged students. And a key thing to remember is that most of us are the privileged, so even though I and others have "succeeded" in that setting, it may have been despite the system and not because of the teaching. Regardless of the instructor, the teaching techniques themselves have biases to different groups. So if we want students to master the material, then perhaps we should teach differently.
Active learning has a growing body of research that shows using these teaching techniques help more students to succeed at mastering a course, especially the less privileged students. Perhaps slightly less material is "covered", but students will learn and retain far more. Isn't that better?
Tuesday, December 1, 2015
Course Design Series (Post 3 of N) - Features versus Languages
One problem I had in preparing the curriculum for this fall is how to balance teaching programming languages versus the features that exist in programming languages. Some analogs to my course have generally been surveys of programming languages, for example - Michael Hewner's course. In such a course, study is focused strongly on learning a diverse set of languages, particularly pulling from logic and functional programming paradigms.
In general, the problem is that the each feature needs examples from programming languages to illustrate how it is used; however, many of these languages have never been previously taught to the students. Therefore, I could spend the first month teaching the basics of Ada, Fortran, Pascal, et cetera. But in doing this, essentially the class starts as a language class. I had considered this approach; however, the non-survey courses and the textbooks do not begin with the languages. These examples all begin with the features. Furthermore, I have taken the further approach to avoid focusing on specific syntax of languages and instead devote my time to teaching features and design.
Having then accepted that the textbooks had a purpose and were rational in design, I followed their structure. And in retrospect I can see that having upper-level students provides a base of knowledge about programming languages such that the need to cover specifics is avoided. I have still taken some class periods to delve into specific languages; however, this is the exception rather than a rule. I do note that in the future, I would spend some time teaching / requiring specific languages. Without this requirement, I have been faced with grading a myriad of languages and find myself unable to assign problems / questions based on specific languages.
In general, the problem is that the each feature needs examples from programming languages to illustrate how it is used; however, many of these languages have never been previously taught to the students. Therefore, I could spend the first month teaching the basics of Ada, Fortran, Pascal, et cetera. But in doing this, essentially the class starts as a language class. I had considered this approach; however, the non-survey courses and the textbooks do not begin with the languages. These examples all begin with the features. Furthermore, I have taken the further approach to avoid focusing on specific syntax of languages and instead devote my time to teaching features and design.
Having then accepted that the textbooks had a purpose and were rational in design, I followed their structure. And in retrospect I can see that having upper-level students provides a base of knowledge about programming languages such that the need to cover specifics is avoided. I have still taken some class periods to delve into specific languages; however, this is the exception rather than a rule. I do note that in the future, I would spend some time teaching / requiring specific languages. Without this requirement, I have been faced with grading a myriad of languages and find myself unable to assign problems / questions based on specific languages.
Friday, August 28, 2015
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.
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.
Monday, August 17, 2015
Course Design Series (Post 2 of N): Choosing a Textbook
Having now read both Programming Language Pragmatics, Third Edition and Concepts of Programming Languages (11th Edition), I have settled on the former as my textbook for the fall. I do not find either book ideally suited, and I wish that the Fourth Edition was being released this summer and not in November, which is why it still hasn't arrived.
For the choice of which textbook to use, it was "Concepts" to lose and having 11 editions, the text should also be better revised. I dislike reading the examples in a book and questioning how the code would compile. Beyond which, the examples felt quaint and contrived.
(edit) For example, I was reviewing the material on F# and copied in an example from the text:
let rec factorial x =
if x <= 1 then 1
else n * factorial(n-1)
Does anyone else notice that the function parameter is x on the first two lines and n on the last?!
Before the Concepts book is written off entirely, there are many valuable aspects. I enjoyed reading about the history of programming languages, especially for exposing me to Plankalkül. The work also took a valuable track in the subject by regularly looking at the trade-offs between different designs and features. This point certainly helped inform my teaching of the material.
Fundamentally, when I looked at the price of the two textbooks, the benefits of using the newer Concepts textbook could not outweigh the nearly doubled pricetag. Most of the positive points are small things and can be covered as addendums to the material.
(FCC note - Concepts of Programming Languages was provided free to me by the publisher.)
For the choice of which textbook to use, it was "Concepts" to lose and having 11 editions, the text should also be better revised. I dislike reading the examples in a book and questioning how the code would compile. Beyond which, the examples felt quaint and contrived.
(edit) For example, I was reviewing the material on F# and copied in an example from the text:
let rec factorial x =
if x <= 1 then 1
else n * factorial(n-1)
Does anyone else notice that the function parameter is x on the first two lines and n on the last?!
Before the Concepts book is written off entirely, there are many valuable aspects. I enjoyed reading about the history of programming languages, especially for exposing me to Plankalkül. The work also took a valuable track in the subject by regularly looking at the trade-offs between different designs and features. This point certainly helped inform my teaching of the material.
Fundamentally, when I looked at the price of the two textbooks, the benefits of using the newer Concepts textbook could not outweigh the nearly doubled pricetag. Most of the positive points are small things and can be covered as addendums to the material.
(FCC note - Concepts of Programming Languages was provided free to me by the publisher.)
Thursday, April 2, 2015
Course Design Series (Post 0 of N): A New Course
This fall I will again be Instructor of Record. My appointment is to teach CS 4392 - Programming Languages. This is an unusual situation in that the course has not been taught for over 5 years (last time was Fall 2009). Effectively, the course will have to be designed afresh.
The first step in course design (following L. Dee Fink and McKeachie's Teaching Tips: Chapter 2) is to write the learning objectives using the course description, along with prerequisites and courses that require this one. Let's review what we have:
Course Description: none
Prerequisites:
Undergraduate Semester level CS 2340 (which has the following description)
Object-oriented programming methods for dealing with large programs. Focus on quality processes, effective debugging techniques, and testing to assure a quality product.
Courses depending on this: none
Alright. Now I will turn to the CS curriculum at Georgia Tech. Georgia Tech uses a concept they call "threads", which are sets of related courses. CS 4392 is specifically in the Systems and Architecture thread. This provides several related courses:
CS 4240 - Compilers, Interpreters, and Program Analyzers
Study of techniques for the design and implementation of compilers, interpreters, and program analyzers, with consideration of the particular characteristics of widely used programming languages.
CS 6241 - Compiler Design
Design and implementation of modern compilers, focusing upon optimization and code generation.
CS 6390 - Programming Languages
Design, structure, and goals of programming languages. Object-oriented, logic, functional, and traditional languages. Semantic models. Parallel programming languages.
Finally, the ACM has provided guidelines for the CS curriculum. Not only does this provide possible options for what material I should include, but they have also provided several ACM exemplar courses (c.f., http://www.cs.rochester.edu/
To summarize, if my first step is to write the learning objectives, then I am on step 0: write the course description. In a couple of weeks, I plan on finishing my initial review of potential textbooks as well as the other materials covered above. That will provide me the groundwork for the description and then objectives.
The first step in course design (following L. Dee Fink and McKeachie's Teaching Tips: Chapter 2) is to write the learning objectives using the course description, along with prerequisites and courses that require this one. Let's review what we have:
Course Description: none
Prerequisites:
Undergraduate Semester level CS 2340 (which has the following description)
Object-oriented programming methods for dealing with large programs. Focus on quality processes, effective debugging techniques, and testing to assure a quality product.
Courses depending on this: none
Alright. Now I will turn to the CS curriculum at Georgia Tech. Georgia Tech uses a concept they call "threads", which are sets of related courses. CS 4392 is specifically in the Systems and Architecture thread. This provides several related courses:
CS 4240 - Compilers, Interpreters, and Program Analyzers
Study of techniques for the design and implementation of compilers, interpreters, and program analyzers, with consideration of the particular characteristics of widely used programming languages.
CS 6241 - Compiler Design
Design and implementation of modern compilers, focusing upon optimization and code generation.
CS 6390 - Programming Languages
Design, structure, and goals of programming languages. Object-oriented, logic, functional, and traditional languages. Semantic models. Parallel programming languages.
Finally, the ACM has provided guidelines for the CS curriculum. Not only does this provide possible options for what material I should include, but they have also provided several ACM exemplar courses (c.f., http://www.cs.rochester.edu/
To summarize, if my first step is to write the learning objectives, then I am on step 0: write the course description. In a couple of weeks, I plan on finishing my initial review of potential textbooks as well as the other materials covered above. That will provide me the groundwork for the description and then objectives.
Friday, March 27, 2015
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
Teaching Advice
- Strive for engagement
- Trust your expertise
- Conference Talk != Teaching
- Get Students to Talk
- 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
- 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)
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