2020 - Agile Analytics, LLC

2020: A year full of data

By Lindsay Lamb

As we near the end of what was a year like no other, I wanted to reflect on what I have learned and shared with you all while writing this blog.

Front and center for me are all of the innovative, telling, and nuanced data visualizations that have come from modeling and documenting Covid-19 cases. Initially, my go-to source was the John Hopkins Coivd-19 tracker. This interactive website was critical when the virus was beginning to spread worldwide and across the US. It painted a realistic picture and helped us mentally prepare for what was to come. We could drill down to countries, states, and counties with alarming precision. As the pandemic progressed, I found myself turning more and more to the visualizations presented in the New York Times. To this day, a visualization is included front and center and it can be filtered based on all sorts of criteria. Additionally, there are usually several other visualizations showing trends based on different populations, trends intersecting with state policies, and more. I have really enjoyed digging into these visualizations (okay, I guess enjoyed is the wrong word as I am constantly looking up Travis County (where I live), Dona Ana County where my folks live, and Orange County in California where my brother and his family live). In a weird way, I find comfort in looking at the trends in the data. It helps me mentally prepare for what is to come and to take comfort in knowing that we are all in this together. I also think they are really thought-provoking and have made folks who are not usually comfortable with data become more so.

I have also enjoyed the infographics the CDC have created. Some are pretty alarming, such as this one:

Source: CDC.gov
Note: I would argue that all of dots should be in some variation of red as no one should swallow hand sanitizer, no matter who says it is a good idea, but other than that it is an effective infographic.

To be fair, I have not spent a lot of time reviewing the CDC’s infographics prior to the pandemic, so maybe they have always produced high quality work. Regardless, I have enjoyed their infographics and have gotten a lot of great ideas from them.

On a more personal level, one lesson I learned this year, okay re-learned, is to let your data tell the story. It is okay to have complicated data, let it tell its own story. I wrote a post about this issue earlier in the year when Andrea and I took data from a line graph and changed it into a lollipop graph. I also wrote about it in reworking some Covid data displays, which can get confusing. I think we all remember my extreme Covid makeover post 🙂 As a rule of thumb, if data cannot be explained on their own, consider breaking it up, making an infographic, or creating a one-pager.

Finally, as I recently discussed, simplify your presentations/reports/data visualizations and then simplify again. PowerPoint is a great tool to use in creating reports, and obviously presentations, and sometimes can get too busy with all the bells and whistles. As I wrote back in March, simpler is better.

This year was a challenge, to say the least. I spent a lot of time looking inward and forcing myself to work even when the world around me felt like it was falling apart. Having a blog was a good outlet for me and allowed me to share my experiences, some personal and some less so, in a way that also taught me important lessons. My hope is that at least one of you out there learned something or enjoyed my musings this year. I honestly believe that you are never too old/young/experienced/unexperienced/exhausted/stressed to learn something new.

So, my friends, go forth and visualize your data and remember my biggest lesson of this year: have grace and patience.

Timeline visualizations with Covid-19 vaccine data

By Lindsay Lamb

During the times of Covid-19, we have seen a boom for data visualizations. In my opinion, this is great for everyone (one of the few benefits of this awful pandemic)! Sure, there have been some misses (well, maybe some were more than misses) but people who never talked about or heard the term data visualization are now talking about data. Data visualizations are making it into mainstream media – TV news channels, newspapers, school district dashboards… everywhere.

We have written blogs about Covid-related data visualizations in the past, and as the end of the year is approaching — and with it, hopes of a vaccine — I thought it would be a nice time to share how folks are depicting vaccine timelines.

I came across this one on the Orange County Register that depicts how long it took other vaccines to get approved and stop the spread of a particular virus.

When I first saw it, I thought the syringe was a cannon. (Perhaps that says something about my current mental state.) I like the idea of using an icon (you know how I love icons!), but perhaps the shot icon is just too big. I do like the overall concept of the visualization — how you can see the timeline of when the disease emerged and when the vaccine was created. Simplifying could make this graphic so much more clear.

Here is a simpler version from A Learning a Day that averages the number of years it has taken to produce a vaccine and compares it to the rapid timeline for the Covid-19 vaccine. You can easily see that most vaccines take about 5 years to develop with several years devoted to the trial process. I like this graphic much better, but I also think it would be good to also include number of years across the top so you could see that the Covid-19 vaccine will be distributed in about a year and a half compared to 5 years. That information can be inferred, but it takes some previous knowledge.

Here is a similar graph from the New York Times:

The grey-ed out blocks indicate the normal timeline for producing a vaccine, which is also helpful in showcasing how truncated the timeline is for the Covid-19 vaccine. I also like this visualization because it shows the projected year when each component of the vaccine timeline is typically completed, which really hammers home how quickly and unprecedented the Covid-19 vaccination timeline has been.

As you can see, there are several ways to depict timeline data. When making a timeline visualization, the important rule is to choose what works best for the data. Let the data tell their story and build a visualization around that story. You might start with an idea in mind, but after messing around with your visualization realize you need to change plans. That is totally ok! Unsure what to do? Ask a colleague, friend, or family member (or all three)! Ideally, all of these folks should be able to read your visualization and understand it without having you explain it to them.

I hope you have found this era of data visualizations as inspiring as I have. Now get out there and share some data stories!

I’ve got the Power! How many participants do I need for my study? / What effect size can I detect? A DIY Guide.

by Andrea Hutson

Urban Missionary Blog » Blog Archive » I've Got The Power!

More and more of our clients are being asked to do power analyses for grant applications. This is good in a sense, but it’s also a barrier to those out there who are not statistics nerds like me. Don’t worry though – it’s not hard, and you can even DIY!

What is power? Power represents the percent chance that you will find a difference between groups if it is really there. If you have low power (anything below 80%), there’s a chance that you’ll go to all the trouble of conducting a study only to find…nothing. And, even worse, you won’t know if it was because there truly was no effect of the program you’re studying, or if it was just due to the study being ‘underpowered.’

How many participants do I need to have appropriate power?

Luckily, there’s an easy way to calculate this for a simple study that does not require MPLUS or STATA or any software. In fact, it’s available for free online (and it has the exact same results as the above programs).

It’s here: https://www.ai-therapy.com/psychology-statistics/sample-size-calculator

What to enter? When you open up the calculator, the bottom section will look like this.

Assuming that you are trying to see if two groups are different (the program group vs. the control group), the only thing you will need to change is the effect size. The effect size statistic describes the magnitude of the difference between groups. Luckily there is standard guidance for this: a small effect is a size of about 0.2, a medium effect is about 0.5, and a large effect is about 0.8. In education research, I would say that these numbers change slightly: small is still 0.2, medium is 0.4. and large is 0.6. You might be able to find the effect size from other studies in your field. You can also calculate it from your own data if you have the means from each group and the standard deviation.

So let’s say you want to detect a medium effect in educational research. Simply change the “Effect Size” field to 0.4 and click “Submit.” At the top of the calculator, the result appears:

That is – we need 100 students in the experimental group and 100 in the control group to have a reliable study where we can detect a medium effect of 0.4 or above.

Here’s how you might write about it on a grant application:

Our sample size calculation is based on examining the effect sizes of similar studies in the extant literature. [Insert citation here if you can.] With an expected 200 participants (N = 100 in each group), we will be able to detect an effect size of 0.40 with 80% power.

What effect size can I detect?

If, on the other hand, you already know how many participants you have, you can play around with the calculator to find out the effect size you can detect. Let’s say we know we have 254 participants in the experimental group and 236 in the control group. Just to be safe, let’s go with the lower number – 236 participants.

Here’s how to write about it:

Given our sample size of 490 participants (N = 236 control; N=254 experiemental), we will be able to detect an effect size of 0.26 or greater with 80% power.

See? Not so bad!

What about groups?

if your data are GROUPED – let’s say that you have 450 participants participating from 10 different schools, the calculation gets a little more complicated. You actually need slightly MORE participants to account for the fact that students from similar schools are going to share many characteristics — teachers, school environment, neighborhood – that make their data more similar to each other than students from nearby schools. Because these data are correlated, our power to detect differences goes down slightly.

You’ll need to, therefore, generate an effective sample size that is affected by these things:

the number of participants per group
the number of clusters/groups
the Intraclass Correlation Coefficient, or ICC. This is probably the most challenging to obtain, but might be available from the literature. See this fantastic article for more on ICC (Killip, et. al, 2004): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1466680/

Once you have all three pieces of data, simply use this formula:

numerator = (number of participants per group) * (number of clusters/groups)

denominator = 1 + (ICC * (number of participants per group – 1))

effective sample size = numerator / denominator

Example

Let’s say we have 10 schools with exactly 45 participants per school. Let’s say we know our ICC is 0.015.

numerator = (number of participants per group) * (number of clusters/groups) = 45 * 10 = 450

denominator = (number of participants per group) * (number of clusters/groups) = 1 + (0.015 * (45-1)) = 1 + (0.015 * 44) = 1.66

numerator / denominator = 450/1.66 = 271. That’s right, even though we have 450 total participants, because the data are clustered, we effectively only have 271 participants.

Using the calculator, we find that instead of being able to detect an effect of 0.19, we really can only detect an effect of about 0.25. This isn’t a huge difference, practically, but it could sink your grant application.

Quick fix: Survey items not displaying properly in your visualization

By Lindsay Lamb

With the election and obsessively recent rise in Covid cases, I have been spending a lot of time obsessively reviewing data. That coupled with reviewing recent survey data from teachers and students regarding their experiences in school (both in person and online), has left me knee deep in data. No matter where I review data, I keep coming across the same problem (which happens to be one of my biggest pet peeves): survey questions not displaying properly in a visualization.

Survey questions do not display properly when the survey question is too long, and instead of being able to read the full sentence, you get this:

Source: EdWeek To be fair, the website where I found this visualization was interactive and displayed the entire survey question when you hovered on the bar. This got difficult as the bars got smaller.

In some cases, you can resize the entire graph which can sometimes help. However, if you have limited space to display your data, you need to make some changes. You can either shorten survey item in your figure (which should signal that you should shorten the item when you administer the survey), allow the ellipses (my eye is twitching just writing this), or only show some of the responses (maybe set up small multiples).

If you do nothing (as in the figure above), this can be problematic. As the consumer of the data, you have to guess what the survey question is before you can make sense of the data. This is difficult and ultimately takes away from the overall meaning of the data. Sometimes you don’t choice. Some data dashboard programs (like Vocalize) do not allow you to change the space allocated to survey item labels. If, however, you are up for some Excel Ninja hacking, keep reading!

If you are using excel, simply change the data label by creating a fake row (I learned this super cool trick from Stephanie Evergreen), adding a data label for the fake data, and then displaying the survey questions with each corresponding label. Here’s a quick tutorial:

Create a fake column with fake data.

Now, add data labels in the figure to your fake data bars. As you can see, my fake data column contains one entry of 100% and the rest are 0%. I did this so I could easily grab the bar corresponding with the 100% data entry and add a label to it and all entries in that series (see below):

Now, change the label position so it is to the left your bar and add the survey question. If you are using a Mac, you have to type in each survey item. I know this is tedious, especially if you have a lot of survey questions, but it is worth it. You can clone data labels if you have found a position you like you can clone this in the label option menu. If you have a PC, in the label menu you can select label values from a range in your spreadsheet and voila! Done!

Still seeing some ellipses? No problem! You can now manipulate the size and position of your survey labels. Yay!!!

Here is a revised version of the visualization:

I should also note that I removed the axis Y-values and removed the horizontal tick marks to clean it up a bit.

This chart is still a lot to take in, so in reality I would recommend splitting it up, or changing the color of items to either highlight the most or least common practices… but that is for another day and another blog post 🙂

Breaking Away from EXCEL – Find & Replace in R

by Andrea Hutson

Note: This post may be for you even if you don’t regularly use R! You’ll learn a technique that is FASTER than Find & Replace in EXCEL and is more accurate to boot.

Raise your hand high if are in the Find & Replace club – you use it in EXCEL to clean up your data before starting analyses, no matter what software you eventually use to analyze data.

If you are editing your data in EXCEL before analyzing, you’re not alone.

Here’s one way we often use EXCEL – to recode text data into numeric data. Let’s imagine we have a dataset like the one below:

a table of values that need to be recoded ("Strongly Agree", "Somewhat Agree" etc.)

We need to recode these variables so that they’re numeric and then we can analyze them.

The typical recode method in EXCEL looks something like this:

Use of Find and Replace in EXCEL - Find "Strongly Agree" and replace with "5"

It takes a while, but it works, right? Well, mostly. Have you ever done something like this?

Whoops, I forgot to do “Strongly Agree” before Agree, or to check “Find entire cells only”

In the example above, I coded “Agree” first, which replaced all of the text for “Strongly Agree”, “Somewhat Agree”, etc. with “4”. Yes, there are ways to avoid this happening,but I often forget about them until I’ve made the mistake.

Have you ever confused yourself so much you’ve had to start COMPLETELY OVER?

I’m here to tell you something exciting – your copy and paste days can be TOTALLY OVER, even if you don’t normally use R.

STEP 1: Load Your EXCEL Data

In step 1, you need to load your EXCEL data into R. This is harder than it should be, and requires you to use the “openxlsx” library or convert your file to a .csv format as there’s no native EXCEL support in R. I’ll plan to do a post all about this in the future, as this was my first major roadblock to using R – actually loading the dang data.

Here’s the first step. For simplicity in the next steps, please name your dataset “myData”.

 
setwd("~/Directory Of Your Files")   # set working directory
 
     # You can get to this in R studio by going to Session -> Set Working Directory 
 
library(openxlsx) # if this is your first time using openxlsx, you'll need to install first
 
    # Use this code first if so ->   install.packages("openxlsx") 
 
myData <- read.xlsx("Your EXCEL FILE.xlsx", sheet=1)

STEP 2: Recode

I’m going to skip explaining the ‘lapply’ function right now – just know that you don’t need to change anything here but the values for your scales. That is, you can change “Strongly Agree” to “Very Often” if that’s the scale you’re using, or change the value for Strongly Agree to a different number. You can add more values, too, just make sure that:

each value is separated by a semicolon (;)
your text values are in SINGLE quotes,
the final bit of code has an end double quote and a closing parenthesis.

# --> Load the library that has the recode function (car) - this one generally comes with R so you shouldn't need to install
 
   library (car)
 
# --> Now do a batch recode!  The code below works for your typical 1-5 "Strongly Disagree -> Strongly Agree" scale but you can use 
 
myData <- lapply(myData, FUN = function(x) recode(x, "'Strongly Agree' = 5; 'Agree' = 4; 
                                              'Neither Agree nor Disagree' = 3; 'Disagree' = 2;
                                              'Strongly Disagree'=1; 'not applicable' = NA")

STEP 3: Convert back to a table

Almost done. The last step to find/replace is to convert this new object you’ve created back into a data frame, which only uses one line of code.

 
# --> Change object back into data frame
 
   myData <- as.data.frame(myData)

STEP 4: Send back to EXCEL

Many of us that use R for data analysis will skip this step, and just start analyzing here. But you can absolutely just send this right back to EXCEL if you’d like.

# --&gt; Write to EXCEL
 
   write.xlsx(myData, 'Data recoded.xlsx')

Boom – you’re done! Now, I know, some of you are thinking, ‘But Andrea, in EXCEL find/replace is just one step, and you’ve just given us FOUR steps to follow. I’m going to ignore this post and keep doing things as I’ve always done.’

But here’s why this way is better:

It is NOT just one step in EXCEL! You’ve got to Find/Replace each individual value…and it’s more time consuming than you think.
Once you have the base code written down you can reuse it over and over!
Similarly, if you save the R file, you can recode this particular data set over and over again. If you get a survey update with 10 more students, you don’t have to spend an hour finding/replacing – you literally just use the same exact code you’ve already written.
You’re not going to make silly mistakes.

Try it out! I’ve got the entire code below for your copy/pasting needs. The items that you will need to change should appear in RED. You should be able to leave all of the rest of the code as is.

 
# --> Set the directory where your files are : you can get to this in R studio by going to Session -> Set Working Directory 
 
setwd("~/Directory Of Your Files")   # set working directory
 
 
library(openxlsx) # if this is your first time using openxlsx, you'll need to install first
 
    # Use this code first if so ->   install.packages("openxlsx") 
 
myData <- read.xlsx("Your EXCEL FILE.xlsx", sheet=1)
 
# --> Load the library that has the recode function (car) - this one generally comes with R so you shouldn't need to install
 
   library (car)
 
# --> Now do a batch recode!  The code below works for your typical 1-5 "Strongly Disagree -> Strongly Agree" scale but you can use 
 
myData <- lapply(myData, FUN = function(x) recode(x, "'Strongly Agree' = 5; 'Agree' = 4; 
                                              'Neither Agree nor Disagree' = 3; 'Disagree' = 2;
                                              'Strongly Disagree'=1; 'not applicable' = NA")
 
# --> Change object back into data frame
 
   myData <- as.data.frame(myData) 
 
 
# --> Write to EXCEL
 
   write.xlsx(myData, 'Data recoded.xlsx')

Try it out and let me know how it goes! I promise, after you’ve saved yourself an hour of tedious finding and replacing, you’ll be a convert!

Teaching kids about statistics doesn’t have to be spooky

By Lindsay Lamb

When the pandemic first hit, Andrea and I wrote a few posts about working at home with our kids. I’ll be honest – I thought it was something that would be over sooner rather than later. Now that we are nearly 8 months in to working and learning from home, honestly, not much has changed. If, like me, your child is still at home learning you might find yourself in need of a break from “zoom-school.”

One thing that got my family through a particularly difficult fall was getting excited about Halloween. Traditionally, our neighborhood goes all out for Halloween. Nearly every house has decorations and several streets are shut down to throw a huge block party. Houses on these streets are open for all to enter. Some are turned into haunted houses and others showcase incredible decorations inside the home (and hand out top notch candy). My daughter had the time of her life with two of her closest friends last year… while my son (who was a little over 1 at the time) threw in the towel rather early in a fit of tears. Oh well, I thought, maybe next year!

Oops.

Since our neighborhood block party and traditional trick-or-treating won’t be an option this year, I knew I needed to do something to keep my daughter excited about Halloween (which is her favorite holiday). As we were walking around the neighborhood one evening admiring the decorations, I thought of something fun for us to do instead (reminder: I’m a data nerd, so my idea of fun might be different from yours!): document all of the Halloween decorations in our neighborhood to determine which decorations were most popular. I made all of us (including my husband and Hayes) hypothesize which decoration would win. I also included a few more guiding questions for Hannah including, would we see more Halloween decorations the closer we got to Halloween? Which streets (or blocks) had the most decorations? What was the spookiest house? Which house had the most decorations?

Every day Hannah took off on her bike with lightning speed and I trailed behind her with Hayes and Lola (our dog) in tow. We stopped to take pictures and document all of the decorations we saw (which was a great way to chat with neighbors). Hannah never complained about riding her bike (which she started to do frequently in the summer); she was on a mission. My little scientist had to collect data.

In addition to Hannah truly enjoying the project, Hayes began honing his skills at spying skeletons, spiderwebs, and pumpkins. I was grateful to get us out of the house, forget about all the seriousness going on in the world these days, and see them truly be happy if even for a bit. When we got home, I helped Hannah document all of the decorations we saw. She made a list of each kind of decoration (e.g., skeletons, spiderwebs) and added a check for each time she saw a house with that particular decoration.

Hannah documenting Halloween decorations.

Hannah tallied how many houses we saw each day (28 was an all-time max!), and how many of each decoration we saw that day. It was fun! As an added bonus, I was sneakily getting her to practice handwriting, math, reading, and PE! It was great!

On our last day, she was excited to see the results come in – although we had a pretty good guess what the results would be.

I helped her count up the decorations, figured out what we saw the most (and least) of, and then we made a graph.

Then I showed her how to make the graph in excel and talked to her about percentages. As we worked, I talked about how I do this type of thing in my job so I can help people make sense of things they want to count. Like the number of students who are engaged in online learning, the number of students from low-income communities who graduate high school and college, the number of high risk students who experience academic improvements because of a community based support model, and the number of teen parents who believe their relationships have improved as a result of an program designed to strengthen relationships.

Our excel graph using percentages. Hannah helped pick the icons and colors of course 😉

Now she at least some idea why I lock myself in the closet for a couple hours at a time every day… and maybe she thinks it is worth it. Who knows, we might even have a future data nerd in the family.

When designing a survey, how many response options are okay?

By Lindsay Lamb

Recently, Andrea and I were speaking at a conference. Our session was entitled, Survey Design 101. We were going back to the basics with our audience. We walked them through the importance of designing good survey questions. We gave examples and cautioned them about being mindful of the wording of questions and selecting the right response options. Our goal in the session was to provide participants with a few strategies to make sure they are actually getting the answers to their main evaluation questions.

As we were talking about how to avoid common pitfalls of bad survey design (e.g., keeping surveys short, avoiding double-barreled questions, using easy to understand language), one of our attendees asked how many response options should be included for each question.

Our answer? It depends.

I know, I know. That is probably not the answer you want to hear, but honestly and truly, it does depend! Here are just some of the things it depends on:

It depends on the age of your participants.
- Are your participants younger? If your participants are younger (think under 3rd grade), you probably only want 3 response options. Think along the lines of Always, Sometimes, Never as good response options. Responses need to be clear and easily distinguishable from each other. Kids at this age think in very black and white terms, so design survey questions and responses accordingly.
- Are your participants older? If your participants are adults or in 3rd grade or above, you can consider using 5 to 7 response options. Some researchers use 9 response options. Sometimes using seven or more response options is exactly what you need. Perhaps responses tend to stack up on the extremes, and you want more spread in their responses so you can understand the subtle gradations in participants’ perceptions. Personally, I find it difficult to distinguish between some response options if there are 7 or more, but maybe that is just me. On the other hand, you can also err on including too few response options (e.g., Yes and No only). Sometimes asking too few questions limits the information you could get from participants. Maybe most participants’ viewpoints are not at either extreme (always or never) but are instead somewhere in the middle. Knowing this information will help program staff identify which elements or concepts are most favorable or least favorable. Unsure on how many responses to include? Test out your survey!
It depends on whether or not you want a mid-range response option. In some cases having a mid-range response option (e.g., sometimes) will result in most people selecting this as an option. Other times, this will push people to either extreme. My advice? Know your audience. Younger participants tend to select the mid-range options while older students do not. Unsure? Test out your survey!
It depends on the type of response.
- If your response options involve frequency, make sure the frequencies are sequential and can be easily recalled and make sense based on the question. Make sure the responses capture the frequency and are timelines or frequencies that are realistic. This should help you determine the number of response options.
- If you are unsure you are capturing the right responses, allow participants to write in their own response with an “other” and text-box option. This is particularly effective when you are piloting a survey and can give you insight into responses you may never have considered otherwise.

Regardless of how many response options you use, I strongly recommend using Likert scales. Likert scales help anchor response options and allow them to be more easily be analyzed.

One more thing to consider as you are designing your survey: if you have the time and bandwidth, test your survey! You can test your survey with a sub-sample of your larger survey population, you can test your survey with a sample of similar participants who are not in your study, test your survey with program staff, or test your survey with family and friends (probably the easiest, and likely the most unbiased!). After you test your survey, ask participants some follow-up questions about the response options and survey questions. They should be able to tell you if the responses match the questions, if there are too many survey responses, too few responses, or if the responses are just right.

These are just some of our internal rules of thumb, but I am sure there are so many more. Do you have some rules of thumb you would like to share with us? Drop us a line and let us know!

Back to the basics with PowerPoint

By Lindsay Lamb

Recently, Andrea and I were asked to create a mini-course on survey design for a virtual conference. Given our years of experience creating, researching, administering, and analyzing survey data, this seemed easy enough. We decided to call it Survey design 101. We were excited –this presentation was going to be fun! (Okay, that is nerdy, but we have to find things that motivate us these days!)

I got to work on the PowerPoint, and boy did I have a lot to say! I remembered all I have learned from my data viz heroes Tufte, Stephanie Evergreen (who just posted a great blog on changes to make to your powerpoint to take it form looking great in-person to looking great in a webinar), and Ann Emery. I used color purposefully, only included points I considered integral to the presentation and trimmed my content. Then I trimmed again.

I was also motivated to keep the presentation short. I wanted to ensure there was ample time for questions, and wanted to have some time for interaction – at least interactions over chat. I sent a draft over to our colleague to review and her feedback was clear, and also a little unexpected: remove bullet points (maybe have one or two per slide, but no more), and if bullet points are important, make them their own slide. Oh, and add more images and icons.

The icons and image piece was fine (you all know how much Andrea and I love icons and images) but I was worried about making my presentation too long by pulling apart the bullet points. It felt a little weird only having one sentence or thought on a slide, but what the heck. As always, Andrea helped by adding some flair to our slides.

Here is how we took one slide and pulled it apart.

*Here is what our slide looked like before*

…And here is how some of our slides looked after the revamp.

*Here are some slides containing the same information, but each point is presented separately making our points clearer.*

Importantly, all of the information is the same. The only difference is that the information in the first slide is pulled apart so each point can stand alone. If the point couldn’t stand alone, we removed it. We kept the ‘title’ of the slide (Pitfall #1 of surveys – Too long!) the same for consistency in all 3 slides.

Although I thought adding more slides to the presentation would make the presentation itself longer, the amount of time we spent discussing each point was the same.

As a final note, pulling apart information into multiple slides is even more important now as people are passively watching webinars rather than actively participating in-person during conferences. Unfortunately, our attention spans are even worse now that we are working from home (and if you are anything like me these days, you are likely watching a presentation while also helping your 5 year old with school, keeping your two year old from turning your dog into a Jackson Pollock painting, and ensuring everyone is quiet while your husband talks with state leaders), so keeping your presentation moving at a relatively fast clip will help with this issue… Maybe 😉

Virtual learning best practice: Expertly using technology

Teaching English Online to Chinese Kids with VIPKID — *Source*. https://pagingsupermom.com/teaching-english-online/

This is the fourth in our series identifying best practices in online learning. As stated in earlier posts, supporting students’ (as well as educators and their families) social and emotional needs must come first in any learning environment, particularly a virtual one in the midst of a pandemic. Next, educators should foster building relationships with each other, with students and with families. After establishing these foundations – which must be nourished throughout the academic year – educators can begin focusing on creating engaging content. One critical component to build a successful learning environment is to ensure that educators, students, and families know how to use the technology provided to them.

Create a virtual learning “studio.” Moving learning into an online environment is hard. Instead of using the first few days of class to practice new technology applications, create a space to practice delivering the content before working with students. Doing so will add an air of professionalism and create a smoother transition to online learning on the first day. There are two major components to this aspect of online learning: visual and audio. Importantly, neither of these require a large investment in equipment.

Visual professionalism includes:

Adding a webcam or setting up the internal camera so that the you face is well-framed (you might need to stack some books under your computer).
Ensuring good lighting by placing one or more lighting sources (e.g., window, lamp) in front of the camera.
Dressing for the occasion.
Setting up the background. Some instructors have gotten creative with backgrounds and have held class in different locations in their homes on different days to spark student interest. Some educators have made scavenger hunts with their backgrounds – use this tool to your advantage!

Audio professionalism includes:

Having a quiet space for the meeting, free from audio distractions.
Ensuring that the microphone is good and does not echo
Speaking in a clear, engaging style (you might need to slow down so learners can stay engaged)

Educators should practice video sessions with a friend and/or record a practice session to practice, review, and refine. Over time, this process will become smoother, and taking these initial steps will ensure both educators and students are comfortable with the process.

Get to Know Your Learning Management System (LMS). Additionally, educators should take the time before classes meet to get to know the features of their online LMS and/or group meeting platform. Can participants chat in your platform? Can participants use a chat feature? Can you create breakout sessions? Do students and families know how to use your various platforms? Make sure you take the time to answer these questions and familiarize yourself, as well as students and families, prior to the first day of class. Also, be sure you (and students and families) know where to go, and with whom to contact, for technical support.

For example, use features embedded in Zoom to create polls, break out rooms, share screens, and take quick temperature checks of participants using emojis (e.g., clap, thumbs up, etc.). Doing so helps make learners feel safe to share answers, stories, ideas, and to speak up when experiencing a technical issue and need help. This gives learners a voice when they could have easily fallen through the cracks.

Use the chat function. Public speaking is a strong fear for many adults and children, and sometimes it can be challenging to get people to speak out – especially when the topic is quite personal. One of the benefits of virtual meeting programs is the simple text chat feature. Educators who are working with groups of more than 5 participants at a time would be wise to become familiar with the chat feature and to encourage its use. This feature is clearly better suited for older students, and is also useful for family members helping younger students engage in online learning.

Use technology to meet the need – rather than finding the technology first. Most importantly, use technology and platforms that will actually help fill a need rather than finding a technology application and forcing their desired content or need into that application. There are a lot of great educational technology resources out there, and not all of them will be right for you or your students. For example, technology can be used to engage students by sending messages to families (using platforms like GroupMe or Class Dojo), texting students an interesting question for them to respond to during synchronous or asynchronous gatherings, creating individualized assignments with video, and using Google voice/voice memos/videos to provide comments and feedback on students’ assignments. Similarly, students can share their work using video (such as Loom), images or Google voice/voice memos. This creates personalization in the learning and allows students to showcase their work and talk about it without interruption from peers.

As you move forward in an online learning environment, we hope these best practices will help you navigate these challenging times. If you want to learn more, stay tuned for our summative report!

Best practices to create an engaging virtual classroom

By Lindsay Lamb

This is the third in a series of blogs reporting on best practices associated with online learning. As discussed previously, supporting the social and emotional wellbeing of our students, educators, and their families is the highest priority in any learning environment right now. The next important thing for educators to improve online learning is to build relationships with students and families. These two strategies should be ongoing throughout the school year. Once educators address students’ social and emotional needs (as well as their own!) and build relationships, they can begin focusing on academic content. How can they do so? By creating engaging content.

In a virtual environment, engaging and relevant course material is even more critical than for in-person classes. A panelist during an EducationWeek online summit on reopening schools in the pandemic shared research gathered in Spring 2020, noting that synchronous learning cannot replace in-person learning simply by offering the same in-person content and pedagogy in an online format. Educators need to recognize that online learning is different, and we must play to the advantages of online learning. For example, online learning is more effective when students can engage in smaller groups where they can interact more directly with peers and can connect one-on-one with their teacher. These strategies build community within the class and between students and teachers.

Another best practice is to ensure that students have time to deeply engage in work on their own and away from the computer rather than passively listening and taking notes for the entire class period (or school day). Allowing students to work independently – or with a parent – and then come back to either a smaller group or a larger group to go over their learning is an excellent way to incorporate both online and individual work. Including a few synchronous learning opportunities for students coupled with asynchronous learning opportunities is a great balance. Doing so provides students with an opportunity to relate with their teacher and peers online as well as giving them a screen break and foster deep learning away from technology.

One strategy that works for all age groups is inviting guest speakers to attend lessons and share their experiences, stories, and personal reflections. As I’m sure we can all attest, guest speakers is a nice break for learners from the normal school routine. Students will benefit from hearing stories of strength and resilience during these challenging times. During a virtual internship program for high school students we had the privilege to evaluate this summer, we found that students were more engaged in lessons when a guest speaker was invited to participate and share their experiences that related to the course material. Students were able to reflect on the speaker’s story, making the lesson content relevant to their life.

Limit the amount of time students spend in an online learning environment. This is particularly important for younger students. According to the Illinois State Board of Education, the minimum and maximum length of engagement time for remote learning for all students under 2^nd grade was 90 minutes, and for high school students, 270 minutes, still short of a typical school day. Schools should not require students to complete a full 6-7 hour school day remotely.

Showcase student work. This is a strategy that effectively works to engage students in person and can easily adapt to the online environment. Simply ask students take a picture of their work, record a video of their work, or share their work during online group meetings. Doing so will keep students engaged, excited and motivated to engage in the lessons.

Finally, continually checking in with students to see if they are engaged in the material is critical as lessons go online. For example, ask students if they liked the lessons, thought the online content was too long, if the course material was relevant to their life, or had any other feedback to improve their experience. For younger students, checking in with parents is equally, if not more important. During our evaluation of the summer virtual internship program, we created weekly check-ins with interns. We asked questions about interns’ interest in the lessons, relevance of the lessons, and length of the lessons. These survey questions helped Ignite MindShift staff identify which lessons should be shortened, and overall student engagement.

Providing different formats for students to engage in learning, and continuously checking in with students and families to assess engagement with the material will help keep students engaged in the learning.

Year: 2020

STEP 1: Load Your EXCEL Data

STEP 2: Recode

STEP 3: Convert back to a table

STEP 4: Send back to EXCEL