edits to assessment/week schedule

pgt52m/pgt52m.qmd

@@ -4,7 +4,7 @@ toc: true
 toc-location: right
 ---
 
-# Introduction 
+# Introduction
 
 This module introduces you to data analysis in R. The first 4 weeks covers core concepts about scientific computing, types of variable, the role of variables in analysis and how to use RStudio to organise analysis and import, summarise and plot data. In weeks 5 to 8, you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one-way analysis of variance (ANOVA). You will learn how to write reproducible reports in Quarto in weeks 9 and 10. Finally, there will be a drop-in for your questions in week 11.
 
@@ -24,19 +24,19 @@ The Module Learning outcomes are:
 
 # How 52M is organised
 
-A key feature of 52M is that you really do learn as you go along and you should not need to revise very much. To support this learning, every week is structured in the same way with contact time and well-guided independent study to prepare you for the contact time and consolidate what you have learned. 
+A key feature of 52M is that you really do learn as you go along and you should not need to revise very much. To support this learning, every week is structured in the same way with contact time and well-guided independent study to prepare you for the contact time and consolidate what you have learned.
 
 Each week has:
 
--   An overview on the "About" page which gives the Learning Objectives, a topic summary and the instructions for the week. You should read this first. 
+-   An overview on the "About" page which gives the Learning Objectives, a topic summary and the instructions for the week. You should read this first.
 
--   Some independent study on the "Prepare!" page to prepare you for the workshop. This will be reading from the course book ([Computational Analysis for Bioscientists](https://3mmarand.github.io/comp4biosci/)),  watching a video, or doing some coding or set up. It is designed to take about 30-45 mins on average. You will most likely learn best if you can find people to study with. 
+-   Some independent study on the "Prepare!" page to prepare you for the workshop. This will be reading from the course book ([Computational Analysis for Bioscientists](https://3mmarand.github.io/comp4biosci/)),  watching a video, or doing some coding or set up. It is designed to take about 30-45 mins on average. You will most likely learn best if you can find people to study with.
 
 -   A two-hour workshop using R. This will usually start with me doing a short demonstration of one or more of the examples that were in "Prepare!" but you will spend most of the session going through some exercises. Anything you have not done before is explained and guided but you will also have to use the skills gained in previous workshops. I often remind you to take care of future you by making notes so you can look up your previous work but you can also search the [Data Analysis in R](https://3mmarand.github.io/R4BABS/) site (search is top right). Talking to other people in the workshop about the exercises and working together will really help you understand more. There will be plenty of help from me and my demonstrators.
 
--   Some independent study on the "Consolidate!" page to give you more practice. The exercises are usually similar to those in the workshop but with less guidance. Occasionally, there will be reading to do. It is designed to take about 30-45 mins on average but may be quicker if you understood the workshop very well or slower if you need to revisit the workshop. 
+-   Some independent study on the "Consolidate!" page to give you more practice. The exercises are usually similar to those in the workshop but with less guidance. Occasionally, there will be reading to do. It is designed to take about 30-45 mins on average but may be quicker if you understood the workshop very well or slower if you need to revisit the workshop.
 
-Learning Data Analysis in R is like learning to speak a new language or play an instrument or a technical sport - you can't really rush it or cram for it. You need regular practice. 
+Learning Data Analysis in R is like learning to speak a new language or play an instrument or a technical sport - you can't really rush it or cram for it. You need regular practice.
 
 -   a little bit of engagement and practice is always better than none
 
@@ -70,10 +70,11 @@ This week you will how to use and interpret the general linear model when the x
 Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.
 
 ## Week 9: Assessment intro
-Reproducible analysis of some relevant data.
+This week will introduce you to the assessment for this module. We will look at a specimen assessment which will use techinques you have already learnt and apply them to analysing a dataset. Your assessment will use a different dataset but you will apply the same principles. We will be covering what your assessment submission should contain using this example.
 
 
 ## Week 10: Reproducible Reporting
-Using Quarto
+Following on from last weeks introduction to the assessment. You will be introduced to quarto, which is a way of generating reproducible reports and you will need for your assessment. This will cover how to embedd sections and exectuable chunks of R code within your quarto files.
 
-## Week 11: Drop-in
+## Week 11: Drop-in
+This session contains no set material, however we will cover topics that people have had difficulty with during the course and cover any material you may still be struggling with from the workshops. This will be our last timetabled session to ask questions prior to the asessment release and a good opportunities to ask any outstanding questions. No questions are silly questions! 

pgt52m/week-9/workshop.qmd

@@ -60,9 +60,9 @@ Record your answers in your script for future reference.
 
 During this module, you should have learnt in the first eight weeks how to create and plot data and understand the appropriate statistical tests that can be used to analyse biological data. For the assessment, you will be provided with a messy data set with which to address key questions, and will submit a reproducible analysis in quarto markdown format. In this week we are introducing the format of the assessment and giving you an example assessment and we will look at the marking criteria. In the following workshop we will be looking at how to do reproducible reporting using quarto which you will need for your assessment. You will have a further opportunity to ask questions about the assessment during the final week workshop which is a drop-in session.
 
-The assessment will be released on the VLE on the **11th of December**, the deadline for submission of the assessment will be the **15th of January 2024**. You will be required to come up with a hypothesis to test on the dataset given and write the results section of a report which will comprise of figures with appropriate captions (figure legends), tables (where appropriate), and a text that will connect the figures and tables/statistical results together. You should submit a zipped Rproject containing an annotated quarto markdown file with R code embedded that reads in data, reformats or reshapes it as required, calculates statistics, and draws the figures.
+The assessment will be released on the VLE, and the assessment section of this module on the VLE will contain the release and submission deadlines. You will be required to come up with a hypothesis to test on the dataset given and submit a zipped Rproject folder containing an annotated quarto markdown file with R code embedded that reads in data, reformats or reshapes it as required, calculates statistics, and draws the figures. This markdown section should contain the code used to perform this analysis, and it should also contain a results section which will refer to the Figures generated  with appropriate captions (figure legends), tables (where appropriate), and a text that will connect the figures and tables/statistical results together. This zipped folder should also contain a html file rendered from your quarto markdown file.
 
-There should be sufficient annotation for a reader to follow the steps in the R code.  The datasets will be made available to you on the VLE under ‘Module Assessment’. The marking assessment criteria will also be available to you on the VLE under Module Assessment and also [here](https://docs.google.com/document/d/1zCuQUqUa3HtAcAn87tsNnolDiE2IRlcqj6y3_5gu5oM/edit?usp=sharing)
+There should be sufficient annotation for a reader to follow the steps in the R code.  The datasets will be made available to you on the VLE under ‘Module Assessment’. The marking assessment criteria will also be available to you on the VLE under Module Assessment.
 
 
 # Specimen assessment
@@ -71,11 +71,10 @@ There should be sufficient annotation for a reader to follow the steps in the R
 
 ![](images/do_on_your_computer.png) You will need to first unzip this project so you can see the contents, this should contain the folders figures, data_raw, figures and a .qmd file.
 
-![](images/do_on_your_computer.png) Here is a specimen results section of a report generated based on the analysis of the specimen data:[Specimen.docx](data-raw/specimen.docx)
 
 # Assessment criteria
 
-![](images/do_on_internet.png) It is good to familiarise yourself with the [marking criteria for module assessment](https://docs.google.com/document/d/1zCuQUqUa3HtAcAn87tsNnolDiE2IRlcqj6y3_5gu5oM/edit?usp=sharing) so that you know what things are marked in the assessment and to give yourself a checklist of things to make sure you have included.
+![](images/do_on_internet.png) It is good to familiarise yourself with the marking criteria for module assessment so that you know what things are marked in the assessment and to give yourself a checklist of things to make sure you have included. 
 You're finished!
 
 # 🥳 Well Done! 🎉