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Taranaki Secondary Schools’ Literacy NLC

Taranaki Secondary Schools’ Literacy NLC. 2011. Consider this. R eading is “…perhaps the single most important technological skill in post-industrial cultures.” - Bjorklund , 2005, p. 392 . 2011 Goals.

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Taranaki Secondary Schools’ Literacy NLC

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  1. Taranaki Secondary Schools’Literacy NLC 2011

  2. Consider this Reading is “…perhaps the single most important technological skill in post-industrial cultures.” -Bjorklund, 2005, p. 392

  3. 2011 Goals • NLC Sector Leadership: To develop sustained participant involvement in cluster work. • Maori Achievement: Raise teacher expectations of Maori literacy achievement using literacy data. • NZC: Use teaching as inquiry model to frame investigation into Maori achievement. • School Leadership: Develop the leadership capacity of school literacy leaders.

  4. Goal One Evidencing the impact: • Regular email/letter communication • Clear schedule of meetings • Everyone attending regularly • Everyone coming prepared • Principals regularly updated • Responsibilities delegated to participants

  5. Goal One • Monitoring the shifts • Full attendance - Regular letters to principals and NLC members with clear schedule of events sent out once a term. Lots of supporting / reminding / encouraging emails sent too. Full attendance by five schools; partial attendance by one school and lost two schools. The format has meant that for some schools, this is the only PD they come to because relief is paid for.

  6. Goal One Monitoring the shifts: • Accurate minutes of meetings and Wiki updated regularly with dates and information for meetings (put these on Google calendar as well) - Minutes recorded and put on wiki regularly. Google calendar updated intermittently. • Follow-ups completed and reported and participants report incrementally on consequent changes in teacher practice - Informal reporting of in-school changes. • Distributed leadership - Haven’t achieved distributed leadership…yet!

  7. Goal Two Evidencing the impact: • Conduct pre and post e-asTTle testing • Learn to disaggregate and analyse Maori data

  8. Goal Two • Monitoring the shifts: • Complete initial analysis of Term One e-asttle data – we’ve completed means analyses and group learning pathways report analyses as a group and with our individual schools (third time’s a charm! – finally getting some shifts in our teaching staff!) • Review literature about role of expectations in effective practice – not done this but we’ve finally got the data monkey off our back!

  9. Goal Two Monitoring the shifts: • Construct and complete pre- and post- measurement of teacher understandings of ‘teacher inquiry’ – staffroom chat suggests that there has been some significant shifts especially at WHS, IHS and HHS. • Regularly update Wiki site – Done; notes from the meetings show how to disaggregate Maori data and complete analyses 

  10. Goal Three Evidencing the impact: • Unpack the principles of Teaching as Inquiry • Use disaggregated Maori Achievement data to draw up key inquiry questions • Deliberate and consistent focus on strategic responses to data and inquiry questions.

  11. Goal Three Monitoring the shifts: • Use findings to generate on-going cluster meeting program – as a result of the GLP report findings (eg identifying main idea, read, select and respond to information), we’ve been working with Ken on how to broach this impasse (eg how to attack text structures to find information and how to use purpose to help find the main idea)

  12. Goal Three Monitoring the shifts: • Ideas, responses and strategies are continuously trialled and refined by cluster at its meetings and in participating schools - we’ve really focused on getting the data under control and our schools are finally getting on top of it; found that staff have to be exposed to ideas at least three times before it sinks in. • Meeting minutes recorded and put on wiki - done 

  13. Goal Four Evidencing the impact: • Data managed and shared through SMS – specifically KAMAR • Literacy leaders understand purpose of data for kids, staff, principals, BoTs and ERO • Expectations for improved achievement arising from data analysis, especially for Maori students are clearly articulated. • Data is shared with school staff and used to inform decisions about effective practice with respect to Maori achievement.

  14. Goal Four Monitoring the shifts: • All participating schools agree to test and share common data - We’re all using e-asTTle except one school who is coming on board with it soon. • We range from increasing confidence using the tool, to reporting it to staff, to staff actually starting to see what it shows them and wanting to know what next steps they need to do. Our group has supported those less confident players. • But all literacy leaders report increasing awareness of literacy data in their staffrooms – people are finally starting to get the point! • We’ve also supported our local Activities Centre in introducing e-asTTle as a referral by one of our participating schools 

  15. Goal Four Monitoring the shifts: • Results of analysis are reported back to literacy leaders’ schools – again in all of our schools this is been done with increasingly good results and the NLC group is supporting the less confident members of the group in data analysis and how to present that to staff. However, these less confident players are gaining shifts that they don’t realise. • NLC minutes record PD undertaken – we’ve done KAMAR, ERO, DATA and NEXT STEPS – last bit of the puzzle is to analyse end of year data for shifts and next teaching steps.

  16. Goal Four Monitoring the shifts: • Expectations of data use for principals, staff and ERO on wiki - we’ve a clearer understanding on this; data for teaching and learning first and foremost; data to reflect, review and adjust teaching and learning programs; reflected in our minutes which are up on the wiki. • Literacy leaders will provide on going professional development in their schools to assist teachers to raise Maori achievement – it’s definitely on going as we’ve just got the teaching staff interested in the data!

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