Merge branch 'main' into feature/tutor-times

docs/ArtGallery/Lighthouse Project/LighthouseReviewReport.md

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+# Report on task: Review Research from T1 and create backlog
+
+I have reviewed two reports on the Lighthouse scores and identified the issues that needed to be
+rectified. One report was a review on the desktop app and the other was a review on the mobile app.
+Both had many errors so, I had to analyse both reports and did some research on finding
+recommendations and solutions for the issue identified.
+
+## Desktop App
+
+For the desktop app, here are the problems/recommendations I have identified from the report:
+
+- Properly size images (add explicit width & height for images)
+- Work on reducing network payloads
+- Serve static assets with efficient cache policy
+- Optimise the Largest Contentful Paint (make it faster):
+  - Eliminate resource load delay
+  - Eliminate element render delay
+  - Reduce resource load time
+  - Reduce time to first byte
+- Reduce the impact of third-party code (identify slow third-party JavaScript)
+- Reduce JavaScript execution time:
+  - Remove unused code
+  - Minify and compress code
+  - Implement code splitting
+  - Cache code with PRPL pattern
+- Minimise main thread work:
+  - Evaluate the script (debounce input handlers, use web workers)
+  - Check style layout (reduce complexity of style calculations, avoid large, complex layouts).
+  - Rendering (manage layer count, simplify paint complexity, and reduce paint areas)
+  - Parse HTML and CSS (extract critical CSS, minify CSS, defer non-critical CSS)
+- Reduce resource counts and transfer sizes (CSS & JavaScript, Images, Fonts, Documents, Media)
+- Minify JavaScript
+- Improve page loading speed:
+  - Using Preconnect to required origins
+  - Preload key requests
+- Minimise critical request depth:
+  - Minimise number of critical resources (eliminate them, defer their download, etc)
+  - Optimize the number of critical bytes and reduce the download time
+  - Optimize the order for the remaining resources to be loaded (download all critical assets early
+    to shorten critical path length)
+
+## Mobile App
+
+For the mobile app, here are the problems/recommendations I have identified from the report:
+
+- Properly size images (add explicit width & height for images)
+- Work on reducing network payloads
+- Serve static assets with efficient cache policy
+- Optimise the Largest Contentful Paint (make it faster):
+  - Eliminate resource load delay
+  - Eliminate element render delay
+  - Reduce resource load time
+  - Reduce time to first byte
+- Improve mobile friendliness (appropriately size tap targets)
+- Reduce the impact of third-party code (identify slow third-party JavaScript)
+- Reduce JavaScript execution time:
+  - Remove unused code
+  - Minify and compress code
+  - Implement code splitting
+  - Cache code with PRPL pattern
+- Minimise main thread work:
+  - Evaluate the script (debounce input handlers, use web workers)
+  - Check style layout (reduce complexity of style calculations, avoid large, complex layouts).
+  - Rendering (manage layer count, simplify paint complexity, and reduce paint areas)
+  - Parse HTML and CSS (extract critical CSS, minify CSS, defer non-critical CSS)
+- Reduce resource counts and transfer sizes (CSS & JavaScript, Images, Fonts, Documents, Media)
+- Minify JavaScript
+- Improve page loading speed:
+  - Using Preconnect to required origins
+  - Preload key requests
+- Minimise critical request depth:
+  - Minimise number of critical resources (eliminate them, defer their download, etc)
+  - Optimize the number of critical bytes and reduce the download time
+  - Optimize the order for the remaining resources to be loaded (download all critical assets early
+    to shorten critical path length)
+- Shorten main-thread tasks
+- Check and use discernible name for links
+- Add meta description to document
+
+I have added cards and allocated story points for each of these problems and recommendations into
+the Trello board under the ‘Lighthouse Project Implementation’ section.

docs/ArtGallery/Lighthouse Project/SpikeUpskillingReport.md

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+# Report on Upskilling
+
+## JavaScript basics with HTML and CSS
+
+I have spent some time upskilling to contribute to this project. So far, I have learnt the basics of
+using JavaScript with HTML and CSS. I have completed a 7-hour course online and followed through
+each chapter of the basics and I understand the basic concepts of working with these languages
+together for web development.
+
+The online courses focused mainly on the fundamentals of JavaScript, HTML and CSS. I have learnt a
+few important information from this course:
+
+- JavaScript basics
+- Modifying text styles, text boxes and images using CSS
+- Chrome DevTools – tool for debugging and inspecting code
+- CSS Box Model – designing and positioning elements on a website. Structure of HTML
+
+### Courses for this upskilling
+
+- HTML to CSS Full Course (Beginner)
+- JavaScript, HTML & CSS Basics
+
+## CSS and Javascript, vue.js upskilling
+
+I have learnt how to modify existing CSS code to remove unnecessary code without changing the
+appearance of the website. I have also learnt how vue.js works and how to make changes to existing
+code. This is to help with tasks related to improving network payloads, . Here is a list of what I
+have learnt during this upskilling:
+
+- remove unused CSS code
+- remove unused JavaScript code
+- including webpacks to help with CSS & JS codes
+- purpose of vue.js, and how it functions
+
+### Topics covered
+
+- Removing unused CSS, JavaScript and HTML code
+- Learn webpacks to help minify CSS, JavaScript and HTML
+- Vue.js for beginners
+
+## Lazy loading, TreeShaking and Ant Design upskilling
+
+I have learnt how to implement lazy routing and TreeShaking into the existing code to help with some
+performance issues. I have also learnt about Ant Design, it's purpose and how it works. This
+upskilling helps with tasks related to PRPL and modifying/reducing impact of thirs party code. I
+have learnt the following:
+
+- What is lazy routing
+- How to properly implement lazy loading
+- What is TreeShaking and what does it to
+- How to implement TreeShaking
+- What is Ant Design and why is it used
+
+### Topics covered for this upskilling
+
+- Vue router tutorial
+- Lazy routing and code splitting
+- What is TreeShaking and how to implement
+- Ant Design courses

docs/ConvertTrello.py

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+import json
+import csv
+
+# Load the JSON file
+with open('Trello.json', 'r') as file:
+    data = json.load(file)
+
+# Define the backlogs of interest/Sprints Of interest
+backlogs_of_interest_names = [
+    "Sprint 1 -Upstream Review", "Sprint 1 Complete",
+    "Sprint 2 Code Review", "Sprint 2 -Upstream Review", "Sprint 2 Complete",
+    "Sprint 3 Review", "Sprint 3 Complete",
+    "Sprint 4 Review", "Sprint 4 Complete",
+    "Numbas - Code-Review", "Numbas - Complete"
+]
+
+# Map the list names to their IDs/Names
+list_id_to_name_mapping = {}
+backlogs_of_interest = []
+
+for list_ in data["lists"]:
+    if list_["name"] in backlogs_of_interest_names:
+        backlogs_of_interest.append(list_["id"])
+        list_id_to_name_mapping[list_["id"]] = list_["name"]
+
+
+# Extract members
+members = {member["id"]: member["fullName"] for member in data["members"]}
+
+# Process cards
+cards_by_member = {}
+total_story_points = {}
+for card in data["cards"]:
+    if card["idList"] in backlogs_of_interest:
+        for member in card["idMembers"]:
+            if member in cards_by_member:
+                cards_by_member[member].append(card)
+            else:
+                cards_by_member[member] = [card]
+
+            # Extract story points
+            story_points = 0
+            for plugin_data in card.get("pluginData", []):
+                if "storyPoints" in plugin_data["value"]:
+                    story_points = int(json.loads(plugin_data["value"])["storyPoints"])
+
+            # Update total story points for member
+            if member in total_story_points:
+                total_story_points[member] += story_points
+            else:
+                total_story_points[member] = story_points
+
+# Write the CSV
+with open('OntrackTrelloReview.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    writer.writerow(["Sprint", "Full Name", "Card Name", "GitHub Link", "Story Points", "Total Story Points"])
+
+    for member_id, cards in cards_by_member.items():
+        for index, card in enumerate(cards):
+            # Extract story points for the current card
+            story_points = 0
+            for plugin_data in card.get("pluginData", []):
+                if "storyPoints" in plugin_data["value"]:
+                    story_points = int(json.loads(plugin_data["value"])["storyPoints"])
+
+            # Check for GitHub related info
+            github_link = None
+            for attachment in card.get("attachments", []):
+                if "github.com" in attachment["url"]:
+                    github_link = attachment["url"]
+                    break
+
+            # Determine if we should show the total story points for this row
+            show_total = total_story_points[member_id] if index == len(cards) - 1 else ""
+
+            sprint_name = list_id_to_name_mapping[card["idList"]]
+            writer.writerow([sprint_name, members[member_id], card["name"], github_link, story_points, show_total])
+

docs/OnTrack/Front End Migration/Research & Findings/Spikes/SpikeOutcome - Data Analytics.md

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+# \*\*Spike Outcomes
+
+==================
+
+**Spike:** Spike_No
+
+**Title:** Data Analytics Spike
+
+**Author:** Dylan Sutherland, <djsutherland@deakin.edu.au>
+
+## Goals / Deliverables
+
+Summarise from the spike plan goal*Besides this report, what else was created ie UML, code, reports*
+
+- Data Analytics backlog on Trello
+
+## Technologies, Tools, and Resources used
+
+List of information needed by someone trying to reproduce this work\
+
+- Internet Browser; Google Chrome, FireFox, Safari
+
+## Tasks undertaken
+
+List key tasks likely to help another developer
+
+- Engaged in Discussion with Andrew Cain
+- Created a Trello backlog for the continued development of Data Analytics
+
+## What we found out
+
+The Data Analytics feature as it currently exists in OnTrack provides a limited set of
+visualisations given the data available. The current visualisations are: Target Grade Pie Chart,
+Task Status Pie Chart & Task Completion Box Plot. For the future development of Data Analytics,
+although integrated visualisations will continue to play a key role in providing a quick overview of
+the data, the ability to export the data to a third party tool such as Tableau or PowerBI will be a
+key feature. This will allow for more complex visualisations to be created and for the data to be
+analysed in more depth. The current visualisations will need to be migrated to the latest version of
+Angular and maintained as they provide a quick overview of the data. One particular area where the
+current visualisations are lacking is the ability to track interaction time such as tutor time to
+provide task feedback, this is a key visualisation which could be added to the existing
+visualisations. In parallel to extending the current visualisations, the requirements of the export
+feature will be investigated including a questionnaire to understand the needs of Unit chairs and
+tutors, before designing UML diagrams and implementing the feature.