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Commonality in Learning Systems – Functionality

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Commonality in Learning Systems – Functionality
  • Instructors have access to course enrollment information
  • Instructors can push surveys/assessments during active class or post-class
  • Instructors can update rosters
  • Allow instructors ability to print rosters, send e-mails to participants, and update course attendance post-class
  • Offers reports – and provides data, including what content X took, what they completed, and other learner data
  • Reports provide overall information by group, department, etc., and individual
  • Ad-hoc
  • Search functionality allows learners to find specific courses, materials, and delivery formats based on keywords, titles, delivery formats, and other tagged identifiers (Some systems limit what can be generated, so think of this more as a search capability)
  • Courses have standard attributes regardless of delivery mechanism, including title and description.
  • If the system offers “opportunities” – it is viewable on the learner side with additional information and specifics – the learner can apply. Opportunity will list requirements – this won’t stop someone from applying anyway.
  • Learners can enroll in or request enrollment for a course, content, group, etc.
  • Learners can view the entire catalog and select their courses
  • Learners can be assigned courses/content, learning paths
  • Learners can view documents and download them (Common is PDF)
  • The learner can see their status, i.e., a progress bar, percentage of completion, or whatever format the vendor has presented it in – some do use colors and expect the learner to know what they mean.
  • Learners can be assigned materials, videos, etc.
  • Create catalogs of content only for job roles tied to skills
  • Curriculum can be set by skills, interests, job roles matched to skill or skills
  • Catalog Search by content related to a specific set of skills/interests (Example: Leadership)
  • Recommends courses/content based on job role, skills
  • Skill gap analysis
  • Skills tied to opportunities (openings or, for example, specific limited opportunities such as a project manager for an upcoming project) in the company/organization – This applies only if the system offers opportunities within their platform
  • Analytics directly tied to career development and acquisition of skills for specific job roles
  • Identify by each learner the skills assigned to them AND the skill(s) they selected
  • Skill Ratings – I can tell you the majority lack one key capability, okay two (specifically what each rating means, and what are the base requirements for that skill – i.e., skill three means you have achieved a level of blah, can do blah blah, blah; and have done blah blah) – I mean what does a two mean without such information? Think this way – I am training you at an ice cream store. What does one mean? At two, you should know how to scoop ice cream and talk to a human across the other side of the counter.

Oh, and

Are You a Fan of Anime?

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Once a niche, countercultural art form, anime is now mainstream. What are your experiences with anime, and what do you think of its explosive popularity?

Word of the Day: montage

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This word has appeared in 190 articles on NYTimes.com in the past year. Can you use it in a sentence?

Teach Taxicab Geometry With Steven Strogatz and The New York Times

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Welcome students to a world of math where pi equals 4 and circles aren’t round.

Are Youth Sports Getting Too Expensive?

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Organized sports for young people have become a multibillion-dollar industry in America. How is this affecting high school athletes’ experiences and access to play?

Braces

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Metal braces are suddenly cool. What do you think of the trend?

Word of the Day: comatose

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This word has appeared in 31 articles on NYTimes.com in the past year. Can you use it in a sentence?

Weekly Student News Quiz: Charlie Kirk, Test Scores, Mars Rock

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Weekly Student News Quiz: Charlie Kirk, Test Scores, Mars Rock

Above is an image related to one of the news stories we followed this past week. Do you know what it shows? At the bottom of this quiz, you’ll find the answer.

Have you been paying attention to current events recently? See how many of these 10 questions you can get right.

The Future of Learning: Identifying Disruptors

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The Future of Learning: Identifying Disruptors
  • ExpertusOne – original incarnation. First vendor to have geolocation in their mobile app – even today, you find systems that lack that, heck even trade show apps – I mean, how can I find the Cornerstone booth in section 309, when I have no idea what section I’m in, because your signage is awful – attend a LTUK show – and you will know first hand.
  • Litmos – Original version – not the version you see today. Strong UI/UX out of the gate and proof that you could be streamlined yet very effective in understanding your audience.
  • Cobent – (RIP) – 100% all compliance platform. Disruptor – they couldn’t adapt enough for compliance
  • Training Orchestra – Training management system – it is all about even management and scheduling here – you want the best? Here you go.
  • Growth Engineering – Truly a gamification platform that understands what is achievable by disruption. Sadly, they couldn’t sustain. Still playing catch-up.
  • Blue Volt – First gamified platform – and first to really target blue-collar workers.
  • Degreed – The behemoth of LXP. I’d add EdCast here too – both disruptors early.
  • Xyleme – an LCMS that was worth its weight. Disruptor – just failed to understand the market (although one vendor, not X here, thinks LCMS is back – no, it’s not)
  • Type of system (LMS – Learning Management System, LXP – Learning Experience Platform, KM – Knowledge Management Platform/System)
  • PR – Product Review – Coming by Nov 25. The specific month is shown.
  • Answer Engine – it drives the entire platform. The AE is using Gen AI – the basics are that the learner asks a question, and a response is presented. The learner can click text or a word, and it goes right to that article, content, or video that exists within the platform. Includes cites.

Behind the Build: Skills Tracking

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Behind the Build: Skills Tracking

It’s one thing to make it to the end of a course — it’s another to know what skills you’ve actually gained from it. Whether you’re switching careers, leveling up in your current role, or just trying to stay sharp, it can be hard to pinpoint what you know and how well you know it. 

We recently rolled out a new skills tracking feature for Codecademy learners, born from user research into our “upskiller” audience — people actively building new skills or deepening existing ones to advance their careers or stay current in fast-changing fields. 

“We did a deep dive into our upskiller segment and discovered re-emerging themes about what learners really wanted,” explains Mark Hannallah, Group Product Manager at Codecademy. The research revealed three core pain points: learners wanted to understand what skills they’d gained, assess their proficiency levels and knowledge gaps, and find meaningful ways to apply those skills. 

The solution seemed straightforward, but building a system that could automatically extract skills from our diverse content library and deliver precise, skill-based recommendations (beyond just course and path recommendations) was the real challenge our engineering team had to solve. We needed AI to parse and structure skills from an enormous, unstructured content library — something that simply wouldn’t have been possible with traditional approaches. 

The project: Create a system that tells learners what skills they have, lets them self-assess, and provides project-based practice opportunities. 

This cross-functional effort involved Product, Design, Engineering, and Curriculum teams using generative AI to parse course content, backend services to integrate skill metadata across systems, and front-end experiences to surface skill tracking in learner journeys. 

Read on to hear how the team brought this feature to life — from early brainstorming to launch and beyond. 

Investigation and roadmapping  

Mark Hannallah, Group Product Manager: “The first step in any project is having a clear enough understanding of the problems you’re aiming to solve, and then breaking those into discrete steps to get started. We were trying to diagram out what was a pretty complex problem — the relationship between what you learn, the skills you walk away with, and how those things ladder up. We had lots of FigJam sessions and brainstorms.” 

Jerimie Lee, Staff Product Designer: “In the earlier stages, there was a lot of jumping on a call and really taking into consideration the goals that we’re looking at and then each other’s expertise. Everybody on the team has primary expertise, but we also kind of flexed across each other’s domains a little bit.”  

Neil Daftary, Engineering Manager: “One of the challenges was philosophical at the beginning where it was like, what is a skill? The language that we were using for it was challenging to define — but also I like that kind of stuff. For me it was it was really fun to be in that space and in the definition stage. I really enjoyed all the FigJams and brainstorming.”  

Mark: “I’ll give credit to the process we follow — it’s called the ‘shape up’ process. It’s somewhere between quarterly planning and sprints. We had a really robust approach where engineering, design, and product disciplines came together to talk through different use cases, sketch out flows, and work backwards from there to build out the system requirements.” 

Good to know: We define skills as broader capabilities that are built from specific techniques or concepts. 

Implementation  

Mark: “Prior to this project, we didn’t have structured metadata about skills associated with our content. So with generative AI, we were able to parse all our course content — past and present — and derive the skill metadata. We then structured that across our catalog.” 

Neil: “One of the cool things I’d highlight is using generative AI, but with a human in the loop. There was a ton overview that Mark and [Senior Instructional Designer Alex DiStasi]  did to make sure our skill metadata and outcomes was correct. 

I worked with one of our engineers to write out scripts that basically query our content and generate these nice CSV or Excel files.” 

Mark: “There’s also a platform layer: as learners complete content, we needed to track their progress toward skills. That meant iterating on and expanding robust platform services to pass metadata between the front end and back end — essentially tracking how learners engage with skill content. 

Then there’s the front end — the user experiences and touchpoints. We had to figure out where in the learner journey the feature should appear and test those ideas with users.”  

Neil: “Language-wise, we used JavaScript and on the platform side was Ruby.” 

Troubleshooting  

Mark: “When designing a system, it’s got to be a complete, functioning system — or else it just doesn’t work. It took a lot of thinking through, discussing, and trying different things to come up with an end-to-end system that works. If something fails, then the system isn’t designed the right way. 

That kind of thing can be tough — you think you’ve made progress, and then it’s a couple steps forward, a couple steps back. But for me, we were really grounded in strong user research, and I think we understood what was important to the audience we were designing for. 

At the end of the day, we were able to return to the problems and figure out how to solve them. That’s what gave me energy throughout the process. Product teams are in a ton of meetings, but this class of meeting — where you’re creatively solving problems that benefit the user and the business — tends to be more fun and less draining.” 

Neil: “One of my main lessons was just the importance of working cross-team — and the ‘contracts’ we formed to deliver these features. It was a learning process, especially since our team hadn’t worked closely with platform historically. Figuring out a good way to collaborate, staying up to date on what’s possible with the current data model and what’s not — that was key. 

Feature-wise, it came down to things like APIs — the shape of the data, how we were going to request it, and scalability. We pushed the system to its limits in ways we didn’t expect at the beginning, so performance became a big focus.”  

Ship  

Jerimie: “Even before this project, one of the key things that added fuel to it was the release of the Skill XP feature. We got really positive feedback from learners and saw statistically significant increases in engagement. That told us, There’s something here. There’s a lot more we could do to help learners see their progress. 

The way it happened to be built — partly because we had to scope down and pace ourselves — was that we launched it for Java first. That gave us a mechanism to get input partway through the project as we continued building. So, it wasn’t like we had to wait 8 months to get feedback. We were able to get something out earlier, and I thought that was really nice.” 

Mark: “We spent two cycles getting the MVP product to market for a subset of our catalog, and then two more cycles to scale the product across the full catalog. After our MVP launch, we got mostly positive feedback, and people started asking for more: ‘This is really cool, this is what I’ve wanted. Can you do this, this, and this next?’ It felt very validating and fueled us to continue scaling across the full catalog.” 

Retrospective  

Mark: “I think the pace of development these days is just getting faster. And the threshold for getting buy-in for big, platform-shifting projects is higher because people expect development to happen faster — especially with the use of AI. One learning with any project is staying mindful of scope and figuring out how to get to market as fast as possible. 

The other learning is that when you build a system that powers a platform, it’s really important for the organization to understand its value and how it can be leveraged. In any company or product team, you want to be aware of how your features can empower other teams — and making that well known can be a big driver of success.” 

Jerimie: “Honestly, the entire thing was a great case study to reference. A lot of the initial ideation definitely came full circle. We slowly engaged more of the organization to get buy-in, eventually pitching upwards to get the official stamp of approval. That process, even though we’ve gone through it in our careers before, felt extremely gratifying in this case given how complex the project was.”  

Snaps 

Mark: “Definitely the LX [learner experience] team, the engineering team, the platform team came in clutch, and then the leadership team. There was a lot of key support at the right times.” 

Neil: “Data science was involved a lot at the beginning in understanding how our content is related to each other, so I’d give them snaps.”