From Concept to Content: A Deep Dive into Theorizing and Planning a Lab Collection
The decision process When creating new content, the first step is deciding what to commit to. We consider: User demand: Are users frequently requesting a specific topic? Evolving landscapes: Is there new technology or industry trends we should cover? Internal analysis: Do our cyber experts have unique insights not found elsewhere? Overarching goals: Is the content part of a larger initiative like AI security? Regulations and standards: Can we teach important regulations or standards? Cyber competency frameworks: Are we missing content from frameworks like NICE or MITRE? After considering these points, we prioritize one idea for creation and refinement. Lower-priority ideas are added to a backlog for future use. Feasibility and outcomes Having a concrete idea is just the beginning. Over the years, we’ve learned that understanding the desired outcomes is crucial in planning. Our core mission is education. We ensure that each lab provides a valuable learning experience by setting clear learning objectives and outcomes. We ask ourselves, “What should users learn from this content?” This ranges from specific outcomes, like “A user should be able to identify an SQL Injection vulnerability”, to broader skills, like “A user should be able to critically analyze a full web application”. Listing these outcomes ensures accountability and fulfillment in the final product. Setting clear learning objectives involves defining what users will learn and aligning these goals with educational frameworks like Bloom’s Taxonomy. This taxonomy categorizes learning into cognitive levels, from basic knowledge and comprehension to advanced analysis and creation. This ensures our content meets users at their level and helps them advance. Turning big topics into bite-sized chunks Once a topic is selected, we must figure out how to break down huge subject areas into digestible chunks. This is a fine balance; trying to cram too much information into one lab can be overwhelming, while breaking the subject down too much can make it feel disjointed. One good approach is to examine the learning objectives and outcomes set out in the first step, map them out to specific subtopics, and finally map those to labs or tasks. For example, consider this theoretical set of learning outcomes for a Web scraping with Python lab collection. A user should understand what web scraping is and when it’s useful. A user should be able to make web requests using Python. A user should be able to parse HTML using Python. A user should understand what headless browsers are and when to use them. A user should be able to use a headless browser to parse dynamic content on a webpage. These outcomes can be mapped into two categories: theory outcomes (“A user should understand”) and practical outcomes (“A user should be able to”). Understanding the difference between these two is useful, as a few things can be derived from it – for example, whether to teach a concept in a theory (heavy on theoretical knowledge without providing a practical task) or practical (teaching a concept and exercising it in a practical environment) lab. Using this, the outline for a lab collection can start to take shape, as seen in the table below. Learning outcome Knowledge Type Suggested Lab Title Suggested Lab Content A user should understand what web scraping is and when it is useful. Theory Web scraping with Python – Introduction A theory lab showing the basics of web scraping, how it works, and when it is useful. A user should be able to make web requests using Python. Practical Web scraping with Python – Making web requests A practical lab where the user will write a Python script that makes a web request using the “requests” library. A user should be able to parse HTML using Python. Practical Web scraping with Python – Parsing HTML A practical lab where the user will write a Python script that parses HTML using the “beautifulsoup” library. A user should understand what headless browsers are and when they should be used. Theory Web scraping with Python – Understanding headless browsers A theory lab explaining why dynamic content can’t be scraped using previous methods, and how headless browsers can solve the issue. A user should be able to use a headless browser to parse dynamic content on a webpage. Practical Web scraping with Python – Using headless browsers A practical lab where the user will write a Python script that scrapes dynamic content from a website using the “puppeteer” library. All Demonstrate Web scraping with Python – Demonstrate your skills A demonstrate lab where the user will complete a challenge that requires knowledge from the rest of the collection. Each learning objective is assigned to a lab to ensure thorough and user-friendly coverage. Often, multiple objectives are combined into one lab based on subtopic similarity and the total number of labs in a collection. The above example illustrates the process, but extensive fine-tuning and discussion are needed before finalizing content for development. Next time… In part two of this mini-series, you’ll read about the next stage of the content development process, which involves laying the technical foundations for a lab collection. Don't miss the Series… You can opt to receive an alert when part two of this series is released, by “following” activity in The Human Connection Blog using the bell at the top of this page. In the meantime, feel free to drop any questions about the content creation process in the replies. Are there any parts of the planning process you want to know more about?Feature Focus: Introducing Drag and Drop, Free Text Questions, and Instructional Tasks in the Lab Builder
I’m excited to announce the latest updates to the Lab Builder. Today, we’ve introduced three new task types: Drag and drop Free-text questions Informational/instructional These exciting new task features will enhance the flexibility and interactivity of your labs, offering even more engaging learning experiences. The new tasks can be added to your lab as usual via the Tasks library. They’re live now, so you can start adding them to your labs right away. Drag and drop Drag-and-drop is a dynamic, interactive task. Designed to challenge the user's recognition and matching abilities, it’s perfect for testing their knowledge in various subjects. This task type consists of text-based items and targets. Users need to drag the items to the correct corresponding targets. It’s easy to add and edit items and targets in the Lab Builder quickly. You can have a minimum of two items and a maximum of 12. You could use the drag-and-drop task type for questions and answers, completing sentence fragments, or matching terms with definitions. Once added to your lab, the new task will appear as follows: Free-text questions This task type requires the user to manually enter text to answer a question. For this task type, you need to write a question and provide at least one possible answer – but there can be multiple correct answers. You can configure this easily in the Lab Builder. Fuzzy matching automatically detects answers that are close enough to the correct answer. For example, if the user submits the right answer with a minor spelling error, it’ll still be accepted. This is designed to reduce user frustration and is enabled by default. You can disable fuzzy matching by turning off the toggle at the bottom. Finally, you can also provide feedback to users if they get an answer wrong, sort of like a hint. This is useful if you want to help point your user in the right direction and prevent them from getting stuck. Instructional tasks This task type is designed to provide users with vital information, guidelines, or instructions. In the Lab Builder, they have the same configuration options as the Briefing panel. Instructional tasks are particularly useful in explaining what the user is expected to do in a following task, presenting story details, or providing a learning journey for users as they go through the lab. You may want to remind users about specific information they need to answer some tasks or tell them to log into an application. The example below reminds users to refer to a specific part of the briefing panel before answering the next questions. Why are these new features useful? Increased engagement: These new question types introduce a gamified element to your custom labs, making learning more interactive and enjoyable. Versatile content creation: These features expand the possibilities for creating diverse and engaging labs, allowing you to tailor your content to your organization's unique needs. Enhanced learning: Drag and drop encourages active recall and association, while free text questions promote critical thinking and deeper understanding. Go and build some engaging labs! Explore the possibilities and build labs that truly engage your users! For more guidance, visit our Help Center, where there’s ample documentation on using the Lab Builder in more detail.
S3: Access Policies (Q5)
Hi I don't get passed this question when I put this for the access point: What am I missing here please, I always get an error on AWS saying that the access point can't be implemented. { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "AWS": "arn:aws:iam::407044316022:user/metrolio-accessor" }, "Action": ["s3:ListBucket", "s3:GetObject"], "Resource": [ "arn:aws:iam::407044316022:accesspoint/metrolio-access-point/object/data/*" ] }] }
