Chapter 1 May 15–21: Introduction

This chapter contains everything that you need to do for Week 1 of HE-930. The first deadline for this course is Sunday May 21 2023, which is when you should please turn in the assignment in this chapter. Please read and follow the instructions in this chapter carefully and then complete the assignment at the end.

This week, our goals are to…

Evaluate and articulate PA (predictive analytics) research questions that apply to education.
Discuss the purpose of PA in educational contexts.
Respond to examples and cases of the use of PA in health professions education.
Complete the following logistical tasks: a) Double-check that R and RStudio are running on your computer; b) Schedule journal club meetings; c) Schedule synchronous seminar; d) Arrange to watch The Social Dilemma on Netflix.

1.1 Course introduction

Welcome to HE-930! This textbook will guide you through the entire course. Every week, just go to the chapter corresponding to that week and follow the readings, videos, and instructions. Then, complete and submit the assignment in each chapter. That is the basic procedure we will follow in this course each week.

Here are some additional details to help you get started:

Please feel free to contact us (the instructors of HE-930) at any time for any reason. You can reach us by e-mail, as explained in the introductory emails that you should have received from us when the course started.
In addition to completing each week’s chapter in the course, you are also required to engage in additional activities such as journal clubs, an oral exam, and a final project. These activities are detailed in the Information and Reference chapter of this textbook, in the Learning Activities, Oral Exam, and Final Project sections. That chapter also contains the Calendar for our course, which shows when all of these activities will occur.

All data analysis assignments should be submitted to the appropriate dropbox in D2L. To access the dropbox, once you are at the D2L website, click on Assessments and then Assignments. Then, click on the assignment for the week you are working on and upload your work.

Thanks for reading the course information above. Please continue reading below to start working on Week 1 of the course!

1.2 Predictive analytics in education, healthcare, and health professions education

To begin our study of predictive analytics (PA) and machine learning (ML), we will read some scholarship and examples of how PA and ML are used in practice today. We will also watch some videos.

Here are a few notes to keep in mind as you begin:

Most of these readings and videos will be revisited in our ongoing discussions, and especially during Week 3.
In the Ekowo and Palmer (2016) reading, they use a three-part framework: 1) targeted student advising, 2) adaptive learning, 3) manage enrollment. When you read the case examples in the rest of the Ekowo and Palmer reading and also when you read the rest of the articles/examples, try to figure out: where does this example I’m reading fit into the framework?
In this week’s readings, there might be research methods and algorithms mentioned that you do not yet know about. That is fine. You can shortlist these to ask about or simply focus on the aspects of the predictive methods mentioned that do make sense to you (most importantly, the research questions and their answers).
“Features” are the same as “variables,” meaning columns of data in a data set or spreadsheet.
It might seem like predictive analytics and machine learning (PAML) involve a lot of math, and the underlying algorithms and processes often do involve a lot of math. However, when we actually use PAML in practice within education and healthcare, we often end up focusing more on the coding and logic of the analytics we are running instead of the underlying math, and we end up letting the computer handle the math for us. In fact, I have often said that PAML is a good break from math.

Required readings about the use of PA and ML (PAML) in HPEd or similar fields:

Please read the following articles or resources:

Ekowo M, Palmer I. The Promise and Peril of Predictive Analytics in Higher Education: A Landscape Analysis. New America. 2016 Oct. https://eric.ed.gov/?id=ED570869. PDF: https://files.eric.ed.gov/fulltext/ED570869.pdf.
Akçapınar G, Altun A, Aşkar P. Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education. 2019 Dec;16(1):1-20. https://doi.org/10.1186/s41239-019-0172-z. PDF: https://educationaltechnologyjournal.springeropen.com/track/pdf/10.1186/s41239-019-0172-z.pdf.
Saqr M, Fors U, Tedre M. How learning analytics can early predict under-achieving students in a blended medical education course. Medical teacher. 2017 Jul 3;39(7):757-67. https://doi.org/10.1080/0142159X.2017.1309376. MGH Treadwell Library permalink: click here.
Arnold KE, Pistilli MD. Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge. 2012 Apr 29 (pp. 267-270). https://doi.org/10.1145/2330601.2330666. PDF: click here.

PDF versions of the readings above can be accessed in our course’s D2L website by clicking on Course Materials followed by Content and then Week 1.

Required introductory videos:

Please watch the following two videos:

Decisive Data. 2018. The Fundamentals of Predictive Analytics - Data Science Wednesday. https://www.youtube.com/watch?v=4y6fUC56KPw. Watch at the YouTube link or embedded below.

Cognitive Class. 2017. Machine Learning vs Statistical Modeling. https://www.youtube.com/watch?v=jaI5aheBOi0. Watch at the YouTube link or embedded below.

Optional (not required) reading:

Allen P. Transforming Health Professions Education: Are We There Yet?. The Journal of Continuing Education in Nursing. 2016 Mar 1;47(3):99-100. https://doi.org/10.3928/00220124-20160218-01. MGH Treadwell Library permalink: click here.

1.3 Assignment

Now that you have completed this week’s readings and videos, it is time to turn to the assignment below. This week’s assignment includes some logistical tasks to help us get started with the course as well as some substantive tasks that will ask you to synthesize the information from this week’s readings and videos.

The deadline for completing all of the tasks in this assignment is Sunday May 21 2023.

1.3.1 Learn about the course

We begin the assignment with a request for you to read some of the key information about this course:

Task 1: If you have not done so already, please carefully read the Calendar and Learning Activities sections of this textbook’s Information and Reference chapter. (You don’t have to read the other sections right now; you can do it later). After reading these sections, do you have any questions about the course? If so, please email them to both Nicole and Anshul.

1.3.2 Logistical tasks

We now turn to some logistical tasks that we want to accomplish this week, so that the rest of the course can run as smoothly as possible.

Task 2: Before the start of Week 3, you will be required to watch a few videos. One of these videos is called The Social Dilemma and is available on Netflix. If you already have access to Netflix, please check if you are able to view The Social Dilemma (you do not need to watch the whole thing this week; just see if you are able to watch it). If you do not have access to The Social Dilemma on Netflix, please inform Nicole and Anshul right away by email, and we will arrange for you to watch it. This link might take you to the Netflix page for The Social Dilemma: https://www.netflix.com/us/title/81254224. It is from the year 2020 and it runs for 1 hour and 34 minutes (according to its Netflix listing).

Task 3: Double-check that R and RStudio are running correctly on the computer(s) that you plan to use for this course. If you have taken the course HE-902 already, then you should know how to do this. If you have not yet taken the course HE-902, you should have received an email from an instructor with more information about setting up R and RStudio. We would like everyone to have R and RStudio set up and operational on your computer by the deadline for this Week 1 assignment. Please email both Nicole and Anshul with any questions related to this.

Task 4: We want to make sure that you are receiving communications from us related to this course. By the first week of class, you should have received a) one or more emails from us and b) one or more calendar invitations for office hours. If you have not received either of these, please let us know immediately (by emailing us, calling us, including a note in your discussion post, asking your academic advisor, or any other reasonable means).

Task 5: You are required to lead one 1-hour journal club session during our class, along with a partner. These are the initials of the assigned partners and the approximate timing of the session you will lead:

ED, FS (Week 5 or later)
DR, MM (Week 7 or later)
SA, DT (Week 9 or later)
JM, RB (Week 11 or later)

To complete this task, please email with your partner above. Decide on a one-hour time during that week when you are all available (or a later week, if the assigned week doesn’t fit your schedule). Email Nicole and Anshul with that time and day. This is the first step for organizing our journal clubs for this summer.

I know that the logistical tasks above might have been tedious to complete. Thanks for your patience and attention to detail. In previous years of this course, these logistics have ended up working out well, so we believe that they will again this year. And of course, we are always open to your feedback for modifications to our plan as long as we can still achieve our learning goals.

1.3.3 Online discussion post

Now we can finally turn back to this week’s content related to predictive analytics (PA) and machine learning (ML). In this part of the assignment, you will engage in written discussion within small discussion groups.

Here are personalized instructions of what each of you should do in this part of the assignment (find your initials in the list below):

ED: Read Ekowo and Palmer carefully. Then, focus on Akçapınar et al 2019. Send your responses to the other members of your discussion group, who are MM and JM.
MM: Read Ekowo and Palmer carefully. Then, focus on Saqr et al 2018. Send your responses to the other members of your discussion group, who are ED and JM.
JM: Read Ekowo and Palmer carefully. Then, focus on Arnold & Pistilli 2012. Send your responses to the other members of your discussion group, who are ED and MM.

FS: Read Ekowo and Palmer carefully. Then, focus on Akçapınar et al 2019. Send your responses to the other members of your discussion group, who are SA and RB.
SA: Read Ekowo and Palmer carefully. Then, focus on Saqr et al 2018. Send your responses to the other members of your discussion group, who are FS and RB.
RB: Read Ekowo and Palmer carefully. Then, focus on Arnold & Pistilli 2012. Send your responses to the other members of your discussion group, who are FS and SA.

DR: Read Ekowo and Palmer carefully. Then, focus on Akçapınar et al 2019. Send your responses to the other member of your discussion group, who is DT.
DT: Read Ekowo and Palmer carefully. Then, focus on Saqr et al 2018. Send your responses to the other member of your discussion group, who is DR.

Optional to read – Instructions for this part of the assignment, in case the instructions above were not clear:

You should read the Ekowo and Palmer (2019) report very carefully.
Then, separate from the Ekowo and Palmer report, you will be assigned one empirical article to focus on.
Please do this section of the assignment ONLY about that one empirical article assigned to you.
Send what you write to the others in your discussion group.

Note that:

The Ekowo and Palmer report is the most important reading this week. Everyone must read that carefully.
You are still required to read all of the assigned empirical articles, but you will only answer the questions below about one of them.
Your responses might be shown to ALL members of the class, not just your own small group.
If you have any questions or want to brainstorm about anything related to this assignment, you can email the instructors, meet with an instructor one-on-one, attend office hours, and/or send your questions to the rest of your discussion group.

Answer the following questions about your focus article and then send them to your discussion group:

Task 6: What is the research question? This should just be ONE sentence with a question mark at the end.

Task 7: What is the main result? Answer in 1–3 sentences.

Task 8: How useful and satisfactory do you think the results are?

Task 9: What would be the real-world, applied use of the analytics process used in the article? What is the article predicting the future about? Is it useful to do this or not? Include all necessary details.

Task 10: What are some possible benefits of using the analytics process from the article in a real world setting?

Task 11: What are some possible risks of using the analytics process from the article in a real world setting?

Task 12: Which Ekowo & Palmer framework category/categories does this study fall under (if at all)? Remember that the categories are: 1) targeted student advising, 2) adaptive learning, 3) manage enrollment.

Task 13: Find a case or example in the Ekowo & Palmer report that is similar to the article you are focusing on. What is similar? What is different? For example, you might write that “Frank’s Story” on p. 7 in Ekowo and Palmer (2019) is similar to the article you read. Then you’ll explain.

Please also answer this question about the Ekowo & Palmer reading, and also include it in what you submit to your discussion group:

Task 14: Find a case or example in the Ekowo & Palmer (2019) report that shows a way in which you wish that you could potentially use analytics within your own work or at your own institution. How is it (the case/example) similar to what you would like to do? How is it different?

And finally…

Task 15: Do you have any questions about anything at all in this first week’s materials? Please include them in your discussion post (or you can send it only to the instructors if you’re more comfortable with that).

You have reached the end of this week’s assignment. Please make sure to submit your responses to all tasks to the appropriate places.