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Dropping Drop-outs!

I have recently started working with the Drop Out Prevention Task Force at my school, and I am finding that I love it! I mean, I don’t like the act of dropping out, but I love looking at data and identifying student needs and concerns. So, for our first meeting, I was inspired by some Personalized Professional Development stuff I had been following on Twitter, so I decided to find something to derive focus before coming. I found this AMAZING video from PBS that I think every person on Earth should watch and discuss. It is really the best description of the role of Middle School in student development that I have heard. I had everyone watch the video before the meeting to come prepared with what direction we would be going to.

Second, I took a page out of my HS bag, and decided that I needed to create a list of heavy-hitters to focus on at the meeting. Sometimes, leaving the concern portion open-ended can become a little witch-hunty or complaint session, which is perfect for SC venting session, but not so productive for a 20 minute meeting. So I used our amazing data people to pull three lists consisting of the three subgroup of risk in the video: Student with 3+ discipline infractions, 15+ absences (verified or unverified), and 2+ classes failed in the previous 9 weeks. Now, we all know that populations vary, so some of these numbers might need to be adjusted- I was looking for our top 20-30 biggies out of all three grade-level. My last list was a cross-reference of these three lists, to identify students who were on more than one of the three lists.

So at this point, committee members have watched the video, and I have created 4 packets of each of the three lists, the compiled list, and a page for intervention notes and “honorable mentions”. I wanted to put members into a group and have them evaluate each list. On the side of all four lists I added a notes column, and I asked teachers to identify anything they knew about the students mentioned- at-risk factors such as mobility and helpful info such as sibling in the school. Here is the order I took:

  • Welcome
    • Video
    • Less than 80% attendance, fails Math or English, unsatisfactory behavior in core course = 75% chance student will drop out
  • Identifying
    • Identifying our students (lists made for each table)
    • 2+ Failure, 15+ Absences, 3+ Discipline
    • Cross-reference list results- 2 Ss on all three lists, 10 Ss on two
    • Explanation of other risk factors- retention, SC, high mobility- increase risk
  • Action
    • In groups, look at lists and write any notes that would be helpful (e.g. SRT held, deceased parent, lives with grandparents, good relationship with mom, stays after for a sport, e-mail regularly with parent, etc.)
    • Add any names that you think are at risk but not on the students that are on 2+ lists
    • Write any ideas for interventions
  • Moving forward
    • Filio to create master list (that’s me!)
    • Team leads to share risk information with content/grade level
    • Administrators and Counselors will evaluate list/actions

My next steps are coming together too, but proving to be more vague. I compiled all of the information for student information (and we definitely learned so much more from having input from teachers and specialists), and will give that to administrators and counselors. At this point, we know who we need to focus on for a targeted program. I am thinking I would also like to have admin and counselors get together to review what interventions have been used, and what interventions we can collaborate on. Then, at the next meeting, I will have an updated list with updated data and students so that we can review what has been done, get suggestions, and celebrate those who have fallen off of the list. It is so exciting- I could do it all day every day! I can’t wait to hear about what other schools are doing too!

1 thought on “Dropping Drop-outs!

  1. […] at the heart of why we do what we do.  I’m basically covering the process I outlined in a previous post , but I’m also including some examples of what the data might look like, and then […]

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