Appraisal Meeting

Met with Steve today to reflect on my practice to date.
Began by showing him this blogger where I record the evidence of meeting my PTC

Dept Meeting

Agenda

Flynn effect

The Flynn effect is the substantial and long-sustained increase in both fluid and crystallized intelligence test scores measured in many parts of the world from roughly 1930 to the present day. When  (IQ) tests are initially standardized using a sample of test-takers, by convention the average of the test results is set to 100 and their  standard deviation is set to 15 or 16 IQ points. When IQ tests are revised, they are again standardized using a new sample of test-takers, usually born more recently than the first. Again, the average result is set to 100. However, when the new test subjects take the older tests, in almost every case their average scores are significantly above 100.

Test score increases have been continuous and approximately linear from the earliest years of testing to the present. For the Raven's Progressive Matrices test, subjects born over a 100-year period were compared in Des Moines, Iowa, and separately in Dumfries, Scotland. Improvements were remarkably consistent across the whole period, in both countries.^[1] This effect of an apparent increase in IQ has also been observed in various other parts of the world, though the rates of increase vary.^[2]

So what does this mean? 

We rest and by default over time the scores go up.

Are we older people less intelligent?

http://www.tv3.co.nz/WORLD-CLASS-INSIDE-NZ-EDUCATION-A-SPECIAL-REPORT-World-Class-Inside-NZ-Education-A-Special-Report/tabid/3692/articleID/126943/Default.aspx

Working with different data- Evidence to accelerate

Working with data concepts

This data evidence is a rich part of KEP. We are data rich at our school and i do believe we over assess as well.

What and how we use data is so relevant to the success of outr akonga

Standards-based assessment

Standards-based assessment allows us to make judgments about the level of an individual's learning with respect to shared benchmarks of expected performance, supported by exemplars.

Reliability and validity

The reliability of an assessment tool is the extent to which it measures learning consistently. The validity of an assessment tool is the extent by which it measures what it was designed to measure.

Types of data

An important part of a well-designed analysis is to be aware of the types of data that are available, so that the appropriate analytic techniques are employed, and inappropriate ones avoided.

Mean, median, and standard deviation

The mean and the median are both measures of central tendency. Standard deviation (SD) is a widely used measurement of variability used in statistics.

Percentages, percentiles, and stanines

In order to understand and analyse data from an assessment tool, you need to know the differences between the ways that different tools measure student achievement, and what that might mean for your analysis.

Norms

Norms are statistical representations of a population, for example PAT maths scores for year 6 males, or e-asTTle reading scores for year 9 Māori females.

Effect size

A good way of presenting differences between groups or changes over time in test scores or other measures is by ‘effect sizes’, which allow us to compare things happening in different classes, schools or subjects regardless of how they are measured

Working with data topics

Loading or downloading data onto a spreadsheet

There are several ways by which quantitative data in the form of scores can be entered into a spreadsheet. Data can be downloaded from a digital assessment tool or student management system.

Cleaning and formatting data

When working with data to analyse results and draw conclusions, it is essential that the data with which you are working is ‘clean’. This means that it is consistent, accurate and complete.

Creating your own simple graphs

Graphs (also called charts) play an important role in data analysis. A graphic representation can make the relationship between sets of data much easier to understand.

Disaggregating data

Student achievement data is often reported for whole populations (for example: cohorts, year levels, whole class). This is called aggregate data.

Observation Reflection

Kia eke panuku 24th May

The use of the observation tool was followed up in PLD on tuesday 24th May.
We looked at this and used a video recording of KWi and LNi for the purpose of working towards completing an observation tool.
Questions arising were
1. What do we record- we need evidence not simply observations in general
2. The energy these take

The session was surprising in that not all staff follow instructions and found it difficult to get into groups and write. It was asked why we didn't do this as a full group. Reason is that we can differentiate in small group d and its the conversations that are richer in smaller groups.

We will follow up with the tick and the POWERFUL CONVERSATION leading to the shadow coaching

CR and RP Observation Tool

This is the collaborative result from unpacking the tool

	Sounds-looks-feels like, examples	FINAL KEY WORDS
WIG
Teacher is  talking to W- Whole class , I- Individual, G-Group
Transmission
Other	Anything that doesn’t fit below or if we are unsure	Teacher lead
Instructional	Teacher Dominated No  sharing power Teacher talking - giving instructions Teacher at the front - initial guidelines/content	Teacher Dominated Teacher Driven
Monitoring	doing the roll passive watching/anecdotal notes cruising the class teacher movement and asking questions giving one on one instruction group work strict observation, marking tests checking work	teacher low interaction scanning interaction
Feed-back Behaviour	Behaviour discussions - comments - 4 to 1 praise positive reinforcement stop that, that's good/that isn't marking tests stop clicking your pen	Teacher Driven re directing behaviour
Feed-forward Behaviour	Discussion/Expectations for next time - next steps positive reinforcement sit down, shut up .....over there encouraging good behaviour	Teacher Driven setting expectations
Dialogic
Cultural tool kit	greetings relationships Marae kit - displays on wall Date Use of basic maori words in teaching all positive relationship with all students in particular maori skills brought in from background/experience kit of knowledge about what teacher and student culturally bring to classes	Inclusive student driven
Feed-back Academic	Assessment Learning conversations when marking is done - conversation	student the focus and take responsibility of their own learning assessment
Feed-forward Academic	where to next - date student books what's next? get a ......pen DJ Goal setting	student driven next steps
Co construction	working together collaboratively planning - learning experiences Shared direction from shared understanding	student driven working together
Relationships
Belief in and care for Maori learners Manaatitanga	Manaatitanga Ako knowing names, ID Maori, pronunciation including knowing parallel cultural.....different approach kinesthetic learning knowing your students
High Expectations for Maori learners and their learning Mana-motuhake	All in together Need to push and expose them to role modelling pushing students - have belief in students	Resilience empathy
Well managed learning context, focussed on learning whakapiringatanga	Planning (classroom - department) Learning styles and focus groups Students know what they are doing and that it is relevant to them planning around curriculum	planning
Effective teaching strategies to promote learning ako	Think Pair Shared/Group Best evidence Group work.  Positive praise for learning steps awareness of barriers heart beat monitor ..................M.......
Evidenced based learning whakataunaki	Assessment/ data / proof teaching based around curriculum
Accelerating improvement for Maori learners Whai pikinga	challenging new learning co constructive learning

HOD meeting 23rd May 2016

Meeting held on 23rd May 2106

1.DARE template for departments RDr	PB4L have shared a document for each department to pull the DARE apart and contextualise it to our areas. Indicate what we give token for.
2.   Numeracy across departments RDr	Postponed till later but
3a   Appraisal/Inquiries SAb	Back in departments we must look at these and make staff accountable and use the data from 8 to push this point How can I make inquiry relevant to raise year 11 achievement
3b Sharing department inquiry (focus on evidence gathering) KBl	Katie shared her inquiry- Solo and data analysis
4.   Assemblies KBl	KBl brought to the views of year 12 students What is the purpose of these, why, what do we want from these, who will organise it? Can we survey the students?
5.   House Competitions SAb/KBl	This is open for grabs and Katie asked if anyones to take it on but not particularly an HOD.
6.   Chrome books LNi	Chrome boxes arriving 27th May 2016 Each area is responsible for booking their chrome books out. They are located I Nelson block and Year 7 and 8 area.
7.   Achievement data SAb	See the manilla folder handed out. It contains info from Kamar that may have errors. There are some starting figures here especially our year 11 data ( year 12,13 is very different) Key learning- what are we going to do as a school, department, individual Need to look at engagement of our students- focus of our TOD
AOB	How can we put P into kamar?