Even after almost 40 years of teaching at Carroll, the first day of class is anxiety-arousing, pressured, critical, and rewarding. As a youth, I was so anxious about giving oral presentations that I fainted when I participated in my first school debate. I had a similar melt-down during the oral component of my graduate school general qualifying examinations in Social Psychology at Ohio State. With experience and a few set backs I’ve learned to over learn and to reframe (attribute) the performance anxiety I inevitably am experiencing into excitement for the task at hand. Sometimes I whistle a happy tune! Click that link and you’ll receive that sage advice from someone who sings better than I. 🙂
These academic first days of the semester pressures I feel are primarily situational nuisances : making sure that my syllabi and handouts are up-to-date, proof-read, and sufficient in number; visiting the classrooms ahead of time to better guarantee that there are enough seats and that the computer equipment works; thinking through how to handle disruptive classroom situations in particular classroom environments; and of course trying to respond in timely fashion to the myriad course-related emails. [Note the irony that I just now am posting this blog post due to first-semester busyness!].
For me the first class meetings are vital for relationship and credibility building—for getting to know my students, creating shared and appropriate expectations, and establishing standards for both students and for me.
This semester I am teaching two sections of PSY 205 “Statistics and Experimental Design” (and its two labs) and PSY492, a Research Seminar focusing on the topic of brain-training software.
Based on 1) student evaluations, 2) what my students demonstrate that they can do at semester’s end, 3) how I feel every time I teach it, and 4) feedback I get from alumni “Statistics and Experimental Design ” is without doubt my best taught course. Among the challenges in teaching such a class successfully are the attitudes that some students bring (“I hate math”; “I don’t do well in math”; “I’m afraid”), weaknesses in students’ fundamental computational skills, and their inexperience with my strongly believed outlook that statistics (and data analysis) is a tool, a language and a way of thinking. Here are some reflections I shared a few years ago about teaching the course. How amusing that even in that class, the one in which I am most confident and comfortable, I missed seeing the dog who was present!
Was my failure to notice canine Kia (whom I had met numerous times and who was even featured in a local newspaper story) an example of what Daniel Simons calls Inattentional blindness? Or was my attentional oversight/ blindness due to my being used to always having a canine companion near me, under me or underfoot?
I’m quite excited about teaching the Research Seminar PSY492. Every day we meet there will be opportunities for data analysis, critical reading, reflective writing, and discussion related to the course’s topic. Relationship building is easier here since I already know all 10 students.
Let Week Two Commence!!
I have come to believe that a syllabus should be a dynamic learning tool. To that end on the first day of class I randomly select some students to download my syllabus. Using the classroom projection system, they explore in the syllabus embedded links to such things as a paper I wrote about how I teach and they begin using a tool (Research Randomizer) for drawing random samples and for randomly assigning participants to conditions.
Here is the syllabus I use in my PSY205 “Statistics and Experimental Design Course.”
I am moving towards requiring that all my students demonstrate to me minimal mastery of my technology enhanced teaching and the learning tools which I introduce into the classroom.
Here is an example of a Quizlet benchmark: Example 1: Quizlet.
How helpful are these links? How might they be improved?
I also am increasingly incorporating screencasts made by me (or by my students) into the class as additional instructional support—especially as I teach SPSS. Though I realize that there are an abundance of such resources on YouTube (and even on LinkedIn!), I still see some value in my personally producing them (or having my students do so).
Here are some screen casts that Simpson research assistants Tia and Ariana made for me to demonstrate their mastery of using screen casting software tools:
And here is one of my SPSS screen casts made at home with the help of Leo the Dog:
Should I continue to produce these even though their production quality may not be “professional”?
Below is a first draft outline of an ebook I am contemplating writing. I share it at this time welcoming feedback. I shall use this draft as part as a review for my PSY205 students. Here is a brief description of HOW I teach the course.
Each hyperlink is a “module. Thanks to Arianna, Tia, and Lizzy for helping me create this draft (while I was away from the office).
What data analysis should I use?: Test your knowledge by clicking on the link. Eventually I shall incorporate a flow chart / decision tree here.
- Teaching Tools: SPSS, inStat, starQuiz, Camtasia and Research Randomizer.
- Augmenting My Teaching Capabilities: Top Technology Learning Tools Revisited.
- On Engaging Students (Part 2): Adventures with StarQuiz and SPSS
- Changes: How much tinkering should one do with a course that seems to work well?
- Learning by Teaching: Alison and Lizzy’s Guide to Using SPSS Data Analysis for Simple Linear Regression
- Retrospective Thinking: How much tinkering should one do with a course that seems to work well?
- Two-way Between Subjects ANOVA Using SPSS (Part 1)
- What Questions can you Answer with your Data? Using SPSS to guide you.
- Review of One-way Between Subjects ANOVA using SPSS
- t-Time: Three Short SPSS Screencasts for PSY205
- Still Looking for ways to Improve Courses After 36 Years of Teaching (Part 1 of 2)
- Retooling and Sharpening the Saw
- Something Old and Something New: A brief Introduction to Effect Size Statistics
I’m glancing at a research article “The Pandora Effect: The Power and Peril of Curiosity” by Christopher K. Hsee and Bowen Ruan recently published in the journal Psychological Science. Since my Oberlin undergraduate days I’ve been interested in the topics of curiosity and intrinsic motivation. Hence, my nom de plume “Curious David.” I wonder how many of my students are familiar with the Greek myth of this first human woman created by the gods. I suspect that more of them are familiar with the radio streaming service by that name.
I’ll probably use the article in my PSY205 course “Statistics and Experimental Design” in several ways. The studies are methodologically simple. They use data analyses I teach in the course. They illustrate the so-called “New Statistics“. In addition, they are amenable to plausible alternative hypotheses. My quick reading suggests additional studies which could be done—-here by my students..
The first and third experiments’ results sections lend themselves well to illustrating how to check the reported effect sizes using the effect size calculators I introduced in an earlier blog piece. I’ll “borrow” and modify the theme of these studies when I create the exam over one-way between subjects ANOVA which I am scheduled to give tomorrow. That is, I’ll in essence propose a study that could/should be done here at Carroll.
I’ll probably have to defer until this summer mastering the intricacies of “The New Statistics” championed by Geoff Cumming. I want to avoid throwing the baby out with the bath water as I attend to blend the revolution in data analysis into my courses. However, here is a first attempt to introduce the concept, calculation, and interpretation of effect sizes into my teaching. Below the screen cast are five very useful effect size online calculators.
Here are five effect size calculators I have found useful enough to share with my students in Psychology 205.
Effect Size Calculators
- Becker: http://www.uccs.edu/~lbecker/
- Hong Kong http://www.polyu.edu.hk/mm/effectsizefaqs/calculator/calculator.html
- Campbell Collaboration: http://www.campbellcollaboration.org/resources/effect_size_input.php
- Psychometrica http://www.psychometrica.de/effect_size.html
- Cognitive Flexibility http://www.cognitiveflexibility.org/effectsize/
Carroll has become a special place to me. I have been influenced greatly by its students, faculty, staff, administration, and alumni. By its traditions, theater productions and its music.
There are lots of changes these days occurring at Carroll. Some of them are physical, others organizational. Some things never change (read between the lines:); some things never should change.
I asked research assistants Alison and Lizzy to document some of the physical changes. Here is what they produced:
I continue to experiment with my “best” course (Statistics and Experimental Design) to make it better by finding the right balance of technology-assisted and personally- delivered instruction. Here is how I have taught it in the past. I have been pleased at the helpfulness, useful feedback and receptiveness of students past and present as I experiment.
This semester I was influenced in what did the during the first week of class by a Chronicle of Higher Education thought piece about making best use of the first class day.
I began the class wanting to test the sound systems so I shared this amazing tribute to David Bowie:
Instead of calling out the class list to take attendance I give a quiz every day with immediate feedback which goes into a student portfolio. I also call upon a random group of students (selected by students using random sampling software to select the lucky students). Two students won free copies of my workbook!
Since then I have introduced them to SPSS and InStat (i.e. that the latter software exists) and to Survey Monkey.
Here is something Lizzy and Alison produced illustrating one of these tools:
I have also shown them Quizlet, started urging them to read germane articles from the Chronicle of Higher Education, and attempted to alert them to ethical issues about research by sharing lessons I have learned from Diederik Stapel.
To date, I seem to have highly engaged students learning and eager to learn. The first exam is February 10.
I am now invite their feedback and yours.
One of the many lessons I’ve learned from many years of teaching is how much I learn through the act of teaching. It recently occurred to me that one way to facilitate my students’ learning of statistics is to position them to teach it. Below is a video created by two of my students illustrating how to use and interpret SPSS’s procedures for creating a scatter plot, calculating Pearson’s r, and, if warranted, performing a simple linear regression. Here is what they wrote and did:
This video was designed to help demonstrate an SPSS analysis for a simple linear regression. This video helps to show the steps to obtain an analysis of data, but the steps are also printed below for further assistance.
Step 1) Enter the names of the data into the variable view. For our data, the first name is Global Awareness which is the “independent variable” while the second name is “Satisfaction” which is the dependent variable. The data will come up automatically as numeric, but change the decimals to 0. Once complete hit the data view.
Step 2) Enter data under the appropriate name.
Step 3) To see if several of Pearson r’s assumptions are met first create a scatter plot. To create the scatter plot, go underneath graphs, legacy dialogues, and then click on scatter/dot. Then a pop up menu will appear and select simple scatterplot, which is the first option. Then SPSS will ask you for the x and y axis. The X is the independent variable while the Y is the dependent variable.
Step 4) When the scatter plot appears, notice the direction (positive or negative), the strength of the scatter plot, and if the scatter plot is linear. If the scatter plot is linear, calculate Pearson’s r.
Step 5) To calculate Pearson’s r, go under Analyze, Correlate, than select bi-variate, and a pop up menu will ask you for the independent and dependent variable. Make sure the Pearson box is selected as well as the two tailed box.
Step 6) To calculate the linear regression, go under Analyze, Regression, and select linear. A pop up menu will ask for the independent and dependent variable.
To understand the data:
Pearson’s r indicates how strong the two variables are correlated.
r squared is the coefficient of determination which communicates how much of the Y variable is explainable by knowing the X variable.
The standard error of estimate is the range around a predicted score within which you are sure with a specified degree of certainty that the predicted score will indeed fall.
Underneath the coefficients table in the B column, one is able to see the y predicted equation (Ypredicted = Bx + A). B is going to be the next to the independent variable while the A is going to be next to the constant.
I continue to experiment with my “best” course to make it better by finding the right balance of technology-assisted and personally- delivered instruction. I have been pleased at the helpfulness, useful feedback and receptiveness of students as we “experiment.”
I just made a Screenflow screencast of what I taught in lab this week (using SPSS to create a scatterplot, calculate Pearson’s r, and do simple linear regression).
This time I published it on YouTube rather than on Vimeo.
I also, in response to student feedback, created some Quizlet study materials. Click the Quizlet link to try them.
A next step will be to involve students in the creation of such materials—rather than my doing so. That may wait until next year, however, since I want to introduce this year’s students to instruction in using Survey Monkey survey creation software.
Please go here to evaluate the video shown above
It would be fun to teach an entire course on these topics.