Open Bucket Subjects
Cecelia Seiner
Their mathematics and English results are double-weighted and are in bucket one. Their best three results in EBacc subjects are counted in bucket two. And bucket three, also known as the Open bucket, has three further qualifications in it. The qualifications may include GCSEs, as well as Department for Education (DfE) approved non-GCSE qualifications, such as art, music, and until recently, the European Computer Driving Licence (ECDL).
In the past, schools with very high entries of the ECDL qualification did well, on average, as outlined in a fascinating piece of research by dataeducator. Their research found that 209 schools entered more than 95% of their cohort for it and 2,240 schools used this qualification to some extent in 2017.
We need to get behind the reasons why pupils in some schools are achieving higher marks in the Open element. On the face of it, either the quality of teaching in the non-Open elements is poor, or something remarkable is happening with regards to the Open bucket. Of course, it could be something else.
In inspection terms, this provokes some interesting questions. Is the teaching in the Open subjects, such as arts and vocational areas, strong? Or, in the schools concerned, is the teaching in other subjects very weak? (Given that the same children are performing far less well in other subjects.) Or is it something else? If so, what?
I would want to know, if a school is doing so well at ensuring pupils gain great grades in the Open subjects, why leaders and teachers are not able to make the same difference to their learning in English and mathematics.
Inspectors will not expect school leaders to present separate plans about the EBacc, or to provide extra information outside of their normal curriculum planning. But they will ask leaders about their curriculum vision and the ambition for all their pupils. Inspectors will want to make sure that all pupils are receiving a breadth of knowledge to stand them in good stead for the future. You can read more about this in our recent school inspection update.
For me, as for most of us who choose to work in education, it is all about doing the best for pupils. In curriculum terms, this means making sure that they have a breadth of knowledge that will help them flourish and take a full role in society.
Open bucket qualifications include vocational qualifications that are graded based on criteria as opposed to GCSEs that are graded on cohort performance. Therefore teaching in vocational subjects can be seen to improve whilst in GCSEs it will appear to stay the same. Schools will then opt for the 'safe' option of a vocational qualification. Each year GCSE grades depend on how everyone else did.
So the solution would be either increasing the connect timeout value or resue the bucket connections rather than bucket opening/closing upon every job since sdk manages the buckets via internal bucket cache.
This means that student progress will no longer be based on whether or not students are able to achieve a C grade or above, as not all students start at the same point. Instead, it will focus on the progress a child makes throughout their time in secondary school.
In order to calculate Attainment 8, the traditional GCSE grades are translated into numbers, where a grade 8 will represent the A/A* boundary, a 1 will represent a G and 4 will indicate a pass, the same as that of a C grade.. A 9 has been introduced to recognise truly outstanding work, fewer of these will be awarded than A*s have been historically.
These slots will be filled by English and Maths. The score for Maths will be double weighted, whereas the English score will only be double weighted if both English Literature and English Language are taken.
The highest scoring English mark will then take the double weighted space in Bucket 1, whilst the remaining English score can be used in the third bucket, but only if it is of a higher score than other subjects in this bucket. This is the only bucket in which scores are double weighted.
In order to calculate a student's individual Progress 8 score, a student's estimated Attainment 8 score (the average Attainment 8 score for all pupils with the same prior Attainment score at KS2) is subtracted from a student's actual Attainment 8 score (the score achieved based on their GCSE results) and divided by 10
In reviewing the Splunk documentation on how indexers tore indexes I see that is says "An index can have several hot buckets open at a time". If hot buckets are for storing newly created indexed data with a predefined expiration or roll over date why would there ever be more than one hot bucket for a specific index? Even in a large deployment when you get to >200GB, if a single index is being utalized why would we see multiple hot buckets?
Two reasons are that data comes in outside of the quarantinePastSecs and quarantineFutureSecs - these end up in a quarantine bucket, which is a hot bucket. Also if you have multiple ingestion pipelines, they each get their own set of hot buckets.
The admin study guide says that when an inedx will receive events that are not in time-sequence order, then the number of available hot buckets should be higher than the default 3. For high-volume indexes it recommends to have up to 10 hot buckets.
Sometimes, when you're indexing a lot of data from different sources, the subtle time differences between machines means that events arriving at the indexer are slightly offset from one another in time. Splunk likes to keep the timeline relatively smooth within a given bucket, so it might write event #1 to one bucket, but event #2 in another, to align with the time of events already in those buckets.
So now a new event arrives, and it's got a time stamp that belongs in neither bucket #1 nor bucket #2. Splunk creates a new bucket. But if I now have more hot buckets than the maximum allowed, it's time to rotate one to warm. Let's say we selected bucket #2 to go to warm. Now it's closed up, it's files are no longer being written to, and it enters the warm state. But bucket #2 was only 100M when it was rolled. That's pretty small for a bucket, especially when you're indexing 100G / day.
The search performance part of this discussion is here: If you're rolling buckets too fast, and ending up with a lot of small buckets, then search performance will be hampered as to find events, we have to open more and more buckets.
You'll get events from Splunk which indicate why the bucket went from hot to warm. If it's for reasons like "exceeded maxHotBuckets", then you might not have enough. The "main" index has defaults set up for indexing a lot of data. It uses ten (10) max hot buckets, and uses the "auto_high_volume" parameter for a size limit (10G on 64-bit systems). If you're indexing at a high volume to an index other than main, it might benefit you to mimic some of the config of the main index.
dbinspect is a great command for taking a look around, or from the cli you can check $SPLUNK_HOME/var/lib/splunk/yourIndex/db. If the reason is quarantine you will be able to tell by the bucket naming convention.
Do you have valuable open text data from textbox, essay, or other-specify textbox responses that you're struggling to gain insights from? There are a number of good reasons for collecting open text data in your surveys. If asked in moderation, open text questions can provide you with a wealth of valuable information.
Open Text Analysis is a very useful tool for quantifying and transforming open text responses into actionable data. Using Open Text Analysis, you can read through responses to each open text question in your survey and bucket them into categories. This allows you to report on textboxes, essays, and other textboxes as a pie chart or bar chart!
While well-designed surveys should make careful use of the open text questions, there are still several good cases for collecting open text data. Using Open Text Analysis you will be able to act on this data and repay your respondents for their time spent providing you with this valuable information! Here are some good cases for analyzing open text data:
When asking customers to rate an aspect of your business, a rating alone won't give you the information you need to act and improve your customers' experience. A follow-up essay asking respondents to explain their rating can provide you with valuable and actionable information.
In Alchemer's two-question Customer Happiness Survey (which many of you have been gracious enough to respond to), we ask the below Net Promoter Score question with a follow up essay "What is the one thing that we could do to improve your experience with Alchemer?" We use open text analysis to categorize the feedback you give us to inform our continuing endeavors to improve!
Well-designed surveys should ensure that there is an appropriate response for each survey taker for all required questions. To ensure that surveys have a comprehensive set of options this often requires an other-specify open text field for respondents to which the list of provided options does not apply.
In the example below, customers are asked what additional features would make them more satisfied with a product. Clearly the survey designer has tried to think of all possible desired features from bells and whistles to doodads. However, to make sure that she doesn't miss out on an opportunity to gather valuable feedback from customers, she's added an "Other, specify" textbox. Using open text analysis she can categorize these responses. In doing so, she can summarize and report on them and even update her survey (if it is ongoing) with commonly requested features she didn't originally think to add to the list!
All open text fields from your surveys are available for bucketing in the Open Text Analysis tool. We have also added the File Upload question as a field available for categorization in the open text analysis tool!
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