Deliver Personalized Learning At Scale With Adobe Learning Manager’s New AI-Based Recommendation Engine

Are you an organization looking to invest in customer or partner education, and do you have a complex product or service portfolio catering to multiple user roles? Your customers/partners want a personalized learning experience based on the products they are associated with and the roles relevant to them. Also, it is likely that different levels of maturity exist for the roles–for example beginner, intermediate, or advanced.

In such a scenario, how do you construct a personalized customer/partner education experience in your learning platform without constant manual effort?

Secondly, learners would also need to discover content beyond their stated preferences and wish to discover popular content. How do you ensure that your best learning content is always displayed upfront dynamically to your learners?

Also, if you are driving revenue from your customer/partner education platform, the two aspects—deep personalization, and dynamically displaying the popular content that your learners will readily enroll in become even more critical.

Adobe Learning Manager’s new AI-based recommendation engine is built to do exactly this—personalize learning and automate the discovery of popular content on your learning platform.

Prefer to read this article in a PDF format? To download a PDF version of this article, select the Download PDF icon below.

How does Learning Manager’s new recommendation engine upgrade your learner’s experience?

Personalized Learning Experiences

Learning Manager’s new AI-based recommendation engine provides learning leaders with a configurable parameter-based recommendation system for crafting a personalized experience for learners. The parameters are—Products/topics, Roles and Levels. Additionally, these parameters can be renamed to suit your needs. So, ‘products’ can become ‘topics’ or ‘roles’ can become ‘regions’.

Popular Content Discovery

Learning leaders also want learners to discover popular content on their platforms. This is an old engagement trick in the book, perfected by modern social media platforms. By surfacing the most popular content on the platform via recommendations, organizations can drive better engagement, more cross-selling initiatives, and ensure that the learner is not boxed-up within their initial preferences – which also keep evolving.

Learning Manager’s AI-based recommendation engine recommends the most popular content across the platform dynamically for all learners.

Course Ranking Algorithm

At the core of the recommendation engine is Learning Manager’s new breakthrough Course Ranking Algorithm. The algorithm uses 50 million data points and five years of aggregated learning data across millions of users to rank courses based on their likelihood of enrolment and completion. This ranking ensures that most enrollable courses are displayed upfront to the learners.

Key Features of the New AI-Based Recommendation Engine

Configurability for Learning Admins

Let us take an example of a SaaS company that provides custom IT solutions to banks. The company has a diverse portfolio of solutions which include fraud detection systems, secure cloud storage, data analytics tools, loan origination systems, and so on. At a customer bank’s end different roles may be associated with the solutions purchased by it, for example, the bank’s own IT team, UI designers, data scientists, loan sales reps, and so on. Further, these roles might have different levels of competency such as beginner, intermediate, and advanced.

As a learning leader of this SaaS company focused on improving the customer education experience, you need to provide personalized learning to your customers based on the products they have purchased, and roles + levels applicable to them.

Learning Manager’s new AI-based recommendations engine enables configuring learning recommendations based on the parameters—Products, Roles and Levels (PRL). The parameters “Products” and “Roles” can be renamed to adjust to an organization’s needs. For example, “Products” can be renamed to “Topics” and “Roles” can be renamed to “Regions.”

Balance Between Configurability and AI-Driven Dynamism

Learning leaders know best what parameters will drive successful business outcomes in their learning platform. Learning Manager’s new recommendation engine strikes the right balance between configurability and the dynamic nature of AI-driven recommendations. The Product, Role, Levels parameters are defined by the learning leaders, while the algorithms analyze learner and course profiles, rank courses, and decide the order of display in the recommendation strips on the learner homepage.

 

Figure 1: The Recommendation Engine provides a balance between configurability and the dynamic nature of AI-driven recommendations.

Structured Data and Management

The ability to capture learner preferences data and course metadata in a structured format can have a wide array of applications for the organization. With the new recommendation engine, Learning Manager is introducing the concept of ‘User Profiles’ and ‘Course Profiles’ that allow learning leaders to capture learners’ preferences across products, roles and levels, and course metadata in a structured format within the platform. This data is easy to manage, search, and monitor for assessing and improving the quality of recommendations via a dashboard.

Setting up the New AI-Based Recommendation Engine

Learning Manager’s new recommendation engine simplifies the Admin workflow involved in setting up personalized recommendations because data pertaining to Products and Roles associated with a customer/partner is typically available to Admins (for example, from purchase records).

There are primarily three workflows involved in setting up the new recommendation engine:

Admin workflow
Author workflow
Learner workflow

Admin Workflow

Admins configure the Products, Roles, and Levels parameter values for the account. For example, an IT solutions provider with banks as their primary customer base may configure the “Product” parameter to have values such as: Payment Gateway, Secure Cloud Storage, Fraud Detection System, Trading Platform etc. and the “Role” Parameter to have values such as Integration Specialist, Network Administrator, Risk Analyst, Compliance Officer etc.

Admins are provided a guided workflow in Learning Manager so that they can set up the recommendation engine optimally and customize the engine based on the account’s use case. Additionally, Admins also get the option of setting up PRL recommendations via a one-time CSV upload.

Figure 2: Admins can define the parameters for recommendations.

Figure 3: Admins are provided a guided workflow in Learning Manager so that they can set up the recommendation engine optimally.

Author Workflow

When Authors create or edit courses, they tag them with the relevant Products, Roles, and Level values created by the Admin. This tagging creates the course/content profile for the recommendation engine to analyze.

Learner Workflow

For an account that has PRL-based recommendations set up, when a learner logs into the learning platform, a guided workflow helps the learner set up recommendations based on his/her product, role, and level preferences. This creates the learner profile for the recommendation engine to analyze.

Figure 4: Learners are provided with a guided workflow to set up recommendations preferences.

Recommendations on the Learner Homepage

With the new recommendation engine set up, when a learner logs into the platform, the following recommendations ‘strips’ are displayed on the learner homepage.

Recommendations Strip
Logic

Super Relevant Strip
Displays personalized content based on all three learner preferences–Products, Roles, Levels and ranked by Learning Manager’s AI-based ranking algorithm. The algorithm is built on a model that uses 50 million data points and five years of aggregated learning data across millions of users.

Product/Topic Strips
Displays personalized content based on learner’s Products/Topics interests, ranked by Learning Manager’s AI-based ranking algorithm.

Discovery Strip
Displays popular content from the account that may be outside of the learner’s PRL preferences. All courses in the account are ranked by Learning Manager’s AI-based ranking algorithm to drive recommendations to this strip.

Figure 5: Types of Recommendations Strips and their logic.

A Glimpse Into How the AI Recommendation Engine Works

Personalization Modes

When learners come to a learning platform looking to acquire new skills, they may expect varying levels of personalization in the recommendations they are provided with, and these can be broadly categorized into three types:

Uber Personalization
Personalized Discovery
Popular Content Discovery

1. Uber Personalization

Very often learners come to the platform looking for specific learning content on their preferred topics or products and within a specific context. Context here would mean that learner, for example, wants to learn about sales but within the context of – 1) sales for an enterprise tech product, and 2) for someone who has significant experience in this function and therefore is looking to learn advanced sales techniques. The learner here does not want to learn about sales in the context of retail sales and is not looking for the basic stuff. The learner in this case is anticipating an “Uber Personalized” experience.

2. Personalized Discovery

A second type of personalization is where learners are specific about their interest area but prefer an element of exploration in terms of the topics they could learn. They want to be suggested topics within a defined learning area. An example could be of a sales professional who is currently into selling IT services but is looking to broaden her expertise in Retail/FMCG sales. Another example could be of a graphic designer who is great at using the image editing tool/product Adobe Photoshop and is an expert in photo editing and restoration but looking to broaden his Photoshop skills into additional areas such as graphics for social media. In this case, the learners are anticipating a ‘Personalized Discovery’ of learning content.

3. Popular Content Discovery

In this third type, learners are keen to know what everyone else is learning about and what courses/topics are trending. For example, currently topics/products such as Generative AI, Design Thinking, Microsoft Outlook Productivity Hacks, Chat GPT could be of interest across various learner profiles. In this case, the learners are open to exploring different topics/products and are looking to learn what’s interesting based on their popularity.

A learner coming to the learning platform could be in any of the three modes or more than one mode simultaneously and the recommendation engine should be able to provide learning recommendations accordingly. This is the key philosophy behind the three types of recommendations strips that can be set up on the learner homepage.

Personalization Mode

Recommendation Strip

Uber Personalization

Super Relevant Strip

Personalized Discovery

Product/Topic Strips

Popular Content Discovery

Discovery Strip

Figure 6: Personalization modes and the corresponding Recommendations Strips.

Please refer to Figure 5 to revisit the details on the types of Recommendations Strips and their logic.

However, this is just the first part of the equation. The second part is Learning Manager’s course ranking algorithm. In short, the course ranking algorithm ensures that within each recommendation strip, the most useful content is displayed upfront in terms of the order of display.

Course Ranking Algorithm

The goal of recommendations in a learning platform is to get learners to learn more. The intention to learn is primarily signalled by enrolling in a course and completing it. By self-enrolling in a course and completing it,  a learner effectively signals his/her interest in the course. What then makes sense is that we leverage this signal and transmit it to other learners in the platform as well.

Learning Manager’s course ranking algorithm takes enrolment and completion as a proxy for interesting content and therefore considers the “likelihood of enrolment and completion” as a decisive factor while displaying interesting courses/content. Further, how does the algorithm measure the likelihood of enrolment and completion? This is where Learning Manager’s vast amount of learning data—five years of aggregated learning data across millions of users helps. The AI algorithm is built on a model that uses 50 million data points to find out what influences a course’s enrolment and completion rates.

Our research shows there are primarily five major factors that influence a course’s enrolment and completion rates and they become fundamental inputs for the AI algorithm while ranking courses/learning events in the platform.

Figure 7: Factors that influence a course’s enrolment and completion rates.

How many learners have enrolled in the course in the past? Courses with greater enrolment numbers are preferred by the algorithm.
How recent is the course? Was it one published in the last week, last month, or older? Recently published courses are favored by the algorithm over older ones. This basically drives more fresh content to the learners.
How well was the course rated? Better-rated courses get recommended more.
What is the duration? Shorter courses require lesser time commitment from learners and therefore such courses are preferred over courses with longer duration.
How many learners were able to complete it in the past? This metric brings in the much-needed element of content quality into recommendations. Courses that have better completion numbers are preferred by the algorithm.

The ranking algorithm considers these as key factors to arrive at a score for each course/learning event available in the platform. A higher score means higher likelihood of enrolability and completion.

Bringing it all Together

Learning Manager’s AI recommendation engine analyses the PRL parameters defined by the admin, learner preferences, course/content metadata, and course scores provided by the ranking algorithm to display tailored recommendations in each of the recommendations strips. The recommendations are updated dynamically so that content with a higher likelihood of enrolment and completion are always displayed upfront in the display order.

We believe that the Learning Manager’s recommendation engine is a powerful tool in the hands of learning and development leaders to achieve two things:

Dynamically place your best content in front of your learners and drive higher enrolments and completion and therefore learning.
Discover the most popular courses on the platform and examine what makes courses popular. This can then flow into internal feedback mechanisms for content creation.

Conclusion

Learning Manager’s new PRL-based recommendation engine is a powerful tool for organizations that want to construct a highly personalized customer/partner education experience. The recommendation engine dynamically places the best content in front of learners and drives higher enrolments and completion and therefore learning. The PRL-based recommendation is lightweight to implement and significantly reduces the Admin workload involved in setting up personalized recommendations.

Ready to learn how to step up the PRL-based recommendation engine for your account?

The following helpx article provides step-by-step instructions.

https://helpx.adobe.com/learning-manager/recommendations-adobe-learning-manager.html

The post Deliver Personalized Learning At Scale With Adobe Learning Manager’s New AI-Based Recommendation Engine appeared first on eLearning.