Data Science Part 4 – Tutoring Center Data

Previously:

At this point in the unit, my goal is to create real-world, authentic experiences for students that also promote their writing skills in preparation for the AP Explore task. In previous projects, my students have been thrown into the deep end of Big Data a few times and hopefully feel more comfortable with pivot tables and visualizations to be able to swim. This project is like throwing them into the ocean.

I reached out to a local tutoring center at our community college and asked if they kept track of any data related to their tutoring sessions – times students arrived, who they worked with, what subject they worked on, etc etc. I’m lucky to have some connections in that world and was able to get a set of data without any identifying student information and separated into separate sheets by individual tutors but with the names removed. I was able to add fake tutor names (that matched names of my students) and consolidate all of the data onto a single spreadsheet that you can find here.

I wanted my students to look at this data to find trends and make recommendations based on the data they’ve found. One thing that makes this type of analysis different from previous projects is that there are lots of interesting entry points to this data – which tutors work with the most students? Which days of the week are most popular? What are peak times during the day? Which subjects see the most students? There’s more freedom in this data to pick and choose which data you want to pursue, which made it difficult for me to find some boundaries on how I wanted students to look at this data.

I was again influenced by the RAFT Writing staregy, so I invented a whole scenario for my students: they now work as a data analyst for the local community college and they’re in charge of finding patterns and trends in the data that will go in a presentation to the campus president focusing on recommendations to improve services at the learning center. Students also have to single out 2 tutors and make recommendations on whether or not those tutors deserve a raise with data to support your recommendation.

In order to do this, they have to look at the data and create several charts and write explanations stating what the data shows and make recommendations based on that data (although not every chart necessarily needs a recommendation – some charts can just be informational). Students compile the charts into a Google Slide deck where all they have to do is copy-and-paste in the chart and write their explanation – they don’t have to worry about formatting or making it “look pretty” because that’s someone else’s job. They also had to write a letter to their colleague explaining the trends they found and the recommendations they made – or, at least, that’s what I had them do this year. In making adjustments for next year, I would call this an email rather than a letter – the goal is to communicate professionally between colleagues, not necessarily write a formal letter to a stranger.

I made slide and letter templates in Google Classroom for students to use, and I created a rubric for students as well. All of those files are in this folder on Google Drive along with a longer description of the project and a collection of exemplar slides. I gave students a week to work on this assignment with a few interruptions: we spent half of a class period going over the details of the project and close-reading the project write-up and what the expectations were; we had a half-day in there as part of our school schedule, so classes were much shorter; and I gave a quiz at the end of the week that took up about half of a class period too. Since I use Excel for my projects, I think it’s important from an equity standpoint to make sure students have lots of time to complete this project in-class since they may not have access to Excel at home or outside of school.

In the beginning, students mostly stuck to my bullet points and found the big-picture trends that I explicitly outlined in my project description. As they started to get a handle on the data and see larger patterns, I started to challenge them to try to investigate trends that no one else in the room would find and make connections between multiple charts to make an argument – this was the inspiration for the Data Expert part of my rubric. Students seemed to respond positively to the challenge of finding unique data or little nuances that weren’t completely obvious – but, if a student was very linearly focused, they could still just progress through my recommendations in the project description and earn most of the points. There was also a mini Excel lesson I had to do in representing some of the data as a duration of time rather than a specific time – I made a little gif showing how I was able to make those changes.

I compiled some of the exemplary results in this google presentation, but here are some highlights:

Some thoughts: my goals for this unit as a whole were to get students comfortable with the tools used to analyze and present data, and to create authentic experiences with big data – I think this project accomplishes both of those. I was able to have some really exciting and insightful conversations with students as they looked for trends and searched for the best way to represent the data – they had statements they wanted to say and we could talk about ways to represent the data so they could make those arguments. It was pretty cool – I felt like they were Excel wizards and I hope they did too.

As the unit progressed, I realized there were opportunities to emphasize writing in a way that was consistent with the AP Explore task, so I found myself adjusting assignments a bit to try and emphasize that and is how I found myself framing the tutor raise recommendation part of this assignment. Putting a bunch of data about a tutor onto a slide isn’t enough – you’ve got to turn that data into an argument, which requires writing a few extra sentences about what that data means, which is a transferable skill to how you should be writing in the Explore PT.

I think I need to do a better job of narrowing my expectations for this assignment or making this assignment less like the ocean and more like a lake – I could see some fatigue starting to set in by the end. Next year, I’ll probably make this a partnered activity where students can split up the work to analyze their data then re-combine it together to put it into their presentation. That would probably align more with the Create PT where students can collaborate but only by splitting the work and re-combining it – having something similar in this project could help prepare students for that type of collaboration later on.

I also might adjust the rubric after I get all of the submissions turned in. Students would ask me what the ‘minimum expectations’ were for this assignment and I had a hard time articulating it because students demonstrate understanding in this assignment with quality and not quantity. Students wanted to hear “you need at least 10 charts” or something similar, and I was never really comfortable assigning a hard number to the ‘minimum requirements’ because I guess there isn’t one. I wish I had thought of that ‘quality not quantity’ line when I was explaining the project. I’m not 100% convinced my rubric really addresses that – there are probably still a few too many quantitative measures in there – but it’s the first draft I came up with.

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