revise exam instructions

content/docs/exam/_index.md

@@ -36,36 +36,37 @@ The course content will be tested in the form of a computer exam, taken on campu
 
 ## Form
 
-- On-campus exam (closed book except selected course material that students can download on the instruction page of the exam)
-- Several sections with subquestions (all open questions; both in written, or by means of code/file uploads)
+- On-campus exam (__closed book__ except selected course material that students can download on the instruction page of the exam)
+- Several sections with subquestions (open and closed questions; open questions both in writing or by means of code/file uploads)
 <!--- Some questions will be personalized (i.e., there is only one correct answer per student)
 -->
 
 {{< hint warning >}}
 
 - Communication with anybody about the exam content is strictly prohibited.
-- Students must not copy-paste from websites, academic papers. The use of ChatGPT or similar AI-based tools is only allowed if stated explicitly for selected questions on the pratical part of the exam (given questions permit the use of the internet).
+- Internet access is unavailable during the exam.
+- You will be able to download ["cheatsheets"](cheatsheets-exam.zip) from the exam's introduction page.
 - Students must not mention their names or student numbers in any of the submitted files, except when being explicitly asked to do so. This is to ensure the exam can be graded anonymously.
 
-{{< /hint >}}
 
+<!--
 
-## Content
+- Students must not copy-paste from websites, academic papers. The use of ChatGPT or similar AI-based tools is only allowed if stated explicitly for selected questions on the pratical part of the exam (given questions permit the use of the internet).
+-->
 
-### Theoretical questions
+{{< /hint >}}
 
-- This part of the exam consists of __personalized open and closed (multiple-choice) questions__, shown in __random order__ (i.e., not in order of difficulty or weight/points).
-<!--- Students __*cannot* go back between questions__ (i.e., questions need to be answered in the order in which they appear).-->
-<!--- Allocate approximately 45 minutes to work on this part.-->
-- Cognitive skills that will be tested are knowledge, comprehension, and analysis.
 
-### Practical questions
+## Content
 
-- This part of the exam consists of __personalized open questions__, shown in __random order__ (i.e., not in order of difficulty or weight/points).
-- Expect two questions (potentially w/ smaller subquestions), mixing "mix" various learning goals. For example:
-  - Work on an issue posted at a publicly available GitHub repository, which focuses on data exploration and transformation (e.g., using RMarkdown, tidyverse).
-  - Automate an existing workflow, and cast it into a repository structure that you share (privately) with the course coordinator for grading. Alternatively, add a module to an existing workflow (e.g., regression analysis), and integrate new module in automation pipeline.
-  - Cognitive skills that will be tested are application, evaluation, and synthesis/creation.
+- The exam consists of __personalized open and closed (multiple-choice, ranking, matching) questions__, structured along the [learning goals](../course/) of this course, and shown in __random order__.
+- Cognitive skills that will be tested are knowledge, comprehension, analysis, application, synthesis and evaluation.
+- You can expect to work three hours on the exam.
+- Expect questions using 
+  - Git/GitBash (e.g., work on a repository using `git` commands)
+  - Automate an existing workflow using make, or debug existing makefiles
+  - Generate RMarkdown documents
+  - Clean/transform data using dplyr/tidyverse.
 
 ## Preparing for the exam
 
@@ -74,6 +75,7 @@ The course content will be tested in the form of a computer exam, taken on campu
 - Please work through the tutorials. While this has been difficult in when you did it for the first time, can you do it on your own now?
 - Share with each other the (public) links to your teams' GitHub repositories. Fork them, clone them to your computers, and then try to run them using `make` (and reading the readme).
   - Can you run the workflows of others?
+  - If make does not work - try to fix the makefiles!
   - Work on the project of others (e.g., by creating a new feature branch, improving code, committing to your fork, and making a PR) - "receiving teams": revise the work of others and integrate the PRs.
   - Add "deployment" steps in your forks, e.g., by adding an app to somebody's regression, or adding a regression to somebody's app
 - Create your own, end-to-end GitHub workflow using the publicly available AirBnB data that teams could use for their projects. Fork that repository and collaboratively work on it with everyone!
@@ -95,10 +97,23 @@ Please [view the list of example questions here](examplequestions).
 
 ### Technical tips & beyond
 
-- Verify that you are familiar with the on-campus computers so that you know how to use them (e.g., Windows, how to open RStudio, how to navigate on the command prompt, etc.)
+- Verify that you are familiar with the on-campus computers so that you know how to use them (e.g., Windows, how to open RStudio, how to navigate on the command prompt, how to set working directories, etc.)
 - Know how to zip and unzip files
 - Make use of cheat sheets (e.g., available on this site, or elsewhere) (you can also print them)
-- Revise your code before submission, so that you ensure it runs from top to bottom without problems.
+- File and code management
+  - Rename files from TestVision (e.g., `download.csv`, `download.rdata`, `download.zip`) for clarity and organization.
+  - Sort renamed files into separate folders to prevent confusion.
+  - Revise your code before submission, so that you ensure it runs from top to bottom without problems.
+  - Do not mention your name or student number in your code.
+- Working with data
+  - Familiarize yourself with opening .RData files and accessing their contents, as outlined in [YaRrr](https://bookdown.org/ndphillips/YaRrr/rdata-files.html).
+  - For Git repository submissions, zip the entire folder containing the repository before uploading to ensure completeness.
+- Setting the working directory in R (preferred method):
+    - Create a new .R file and save it in the folder where your data files are downloaded.
+    - Set the working directory to this location via `Session -> Set Working Directory -> To Source File Location`.
+- Local package installation
+  - Practice installing packages locally, especially since internet access will not be available during the exam.
+  - Test local package installation with [package_test.zip](package_test.zip) by unzipping and running `install_packages.R` via `RScript install_packages.R` after navigating to the correct directory.
 
 <!--
 - Verify your software setup (you should be able to produce RMarkdown documents as PDF documents, run `make`, and even run existing Python code on your computer).

content/docs/exam/examplequestions.md

@@ -12,14 +12,13 @@ Questions will be asked along the course's learning goals, and complexity levels
 
 Below, you can find a few example questions, which will be discussed with students in the final live stream of this course.
 
-
-
 {{< hint warning >}}
 
 This exam consists of __open and closed (multiple-choice) questions__. You can freely go back and forth between these questions.
 
 {{< /hint >}}
 ![](../dprep_part1.png)
+![](../dprep_part2.png)
 
 *Note: the number of questions depends on the points awarded to each question. The instructions during the final exam may slightly vary, so make sure to still read it accordingly.*