What is Personalized learning?
To make the learning process a better fit for each Learner, with their special learning style, background, needs, and prior experiences, personalized learning involves customizing and adapting Learning methods and procedures.
There are numerous locations, activities, approaches, and time frames in which learning might take place.
There are so many different shapes that learning may take, from the lecture hall filled with hundreds of Learners listening to an instructor to a one-on-one mentoring program, from interactive online games to difficult technical volumes.
Every learning experience is unique and personalized because there are numerous learning methods and teaching and learning philosophies to choose from. Each method of Learning has advantages and disadvantages and will provide varied benefits to different Learners.
To help learners learn more quickly, comprehend new ideas more readily, and improve their learning performance, a personalized learning strategy connects the learner’s unique experience, knowledge, and habits with learning methods.
How does personalized Learning function?
The main goal of personalized learning is to match a learner’s current understanding with new information by matching training materials to their prior knowledge, experiences, and skills.
The simplest instance of personalized learning is when a teacher gives a Learner learning materials that are appropriate in terms of both content and context.
Using what the instructor already knows about the Learner, this is accomplished. The teacher is aware of the best ways to integrate the learner’s prior experiences and aptitudes into the new material, creating connections between old and new knowledge.
Content that is appropriate for learning is relevant to the learner’s prior experiences.
The greatest method for each Learner is to give the knowledge in a way that makes it simple for them to understand the new material.
This could include the format of the content (video, text, or interactive games, for instance), the length of the sessions, the quantity of material covered in each lesson, and the sequence in which new knowledge is introduced. Each Learner will experience this differently because everyone has a unique learning style.
Though not scalable, this is the most straightforward way to implement personalized learning. It involves connecting and providing the appropriate resources to support the learning process.
Organizations today need to be able to build a digital learning infrastructure that can automate this process and make it affordable for it to happen and, more crucially, be scalable.
Organizations must employ a range of digital solutions to do this. These may consist of messaging apps (like Slack), AI innovations (such as machine learning and automation), data analysis, educational platforms, mobile devices, and other things.
An organization can only properly scale and organize the delivery of information to each Learner while also better understanding their needs by developing a system using the many resources at their disposal.
Why is personalized learning important?
Personalized learning is a tried-and-true method for improving learning outcomes in organizations.
Personalized learning produces a more thorough grasp of new concepts, higher engagement, and increased knowledge retention by using data from a learner’s prior experience and connecting that to new concepts. In a nutshell, personalized learning improves the effectiveness of learning.
You can see how personalized learning can change a standard training program and make it more tailored to each user by taking a look at the previous example. These modifications enhance learning by making it more interesting, efficient, and quick.
Making learning materials slightly more personalized, such as by deleting irrelevant information, will have an impact.
It will help if learning materials are even somewhat customized; for instance, by eliminating redundant information and emphasizing the training that will benefit the learner the most.
Learners are becoming more demanding as technology develops, and they expect training programs to stay up. Personalized content is widely available today. On social media, we have individualized feeds, playlists, movie suggestions, and many other features. Any new tool will likely have a feature like this, as we currently anticipate. At work, we anticipate that the learning platform will provide us with something fresh and specifically tailored to our needs.
Organizations run the danger of losing an employee’s engagement in a crucial area if they don’t update the way learning is offered.
Additionally, consider the impression that a company’s antiquated, impersonal training methods may give off to new hires when they begin their onboarding process. Will they have faith that this company can support their professional growth? Will they consider the company to be a fiercely competitive and forward-thinking place to work?
Employees frequently claim that they search for positions that will provide training and career advancement possibilities and that they will work longer in those positions. Employees can perform better as they gain knowledge, which leads to improved company outcomes.
Effective employee training programs can raise an organization’s income, productivity, and innovation. Personalized learning has gained attention as a strategy that helps upskill employees effectively and efficiently as more organizations grapple with this difficulty.
Advantages of Personalized Learning
1.) Saves time
The amount of time it takes a learner to become interested in and comprehend a new subject is reduced via personalized learning.
Additionally, it saves time that would otherwise be lost on learning ideas that won’t benefit the learner by removing content that is no longer applicable or would be redundant given the Learner’s level of knowledge.
2. Improves participation
Learning is more interesting when the content is both pertinent and customized.
Content that speaks to a learner’s present function, projects, or field of employment increases their likelihood of interacting with it and remembering it.
3. Strengthens memory retention
The Learner will retain information for a lot longer when the content is based on prior experience.
The learner will be able to remember information better by connecting it to prior knowledge when a personalized learning route links all of the puzzle pieces together, helping one another to entangle the material.
4. A greater desire to work
Learning that is related to the learner, their occupation, or their interests will make the learner more motivated.
This is especially true if the content offers advice or practical knowledge that may be used right away.
5. Enhances academic performance
According to studies, a personalized learning strategy produces higher learning outcomes. With this strategy, learning is elevated and relevant, and interesting, usable, and memorable content is delivered.
The outcome is a Learner who is happy with their interaction with the subject and does their work more effectively.
Personalized learning examples
1. Private tutoring
A teacher must know precisely what the Learner currently knows to connect that knowledge to new material when there are two people involved—a learner and a teacher.
Take this illustration of a parent instructing their child about cars: My youngster recently questioned me, “What is this and what do you need it for?” relating to my manual transmission car’s shifting.
After giving it some thought, I explained it to him by mentioning the shifter and gears on his bike, which enable him to alter the speed/effort ratio.
I then connected the concept of those mechanisms to the gearshift in my car. By knowing something from my son’s previous experience and linking new information to that, I made this new concept easy for him to learn.
What if the parent chose to explain this concept using diagrams of engines and gearboxes, talking in high-level mechanical engineering language?
The kid would have checked out immediately, as he would have been unable to connect this new information to what he already knew. No learning would happen, and the child likely wouldn’t ask any more questions like this to his parent, as he has learned that the answer will be incomprehensible.
This is how creatively utilizing what is currently known about the Learner and connecting that to fresh knowledge can result in one-on-one personalized learning.
Mentoring is a well-known method of individualized learning. The less experienced employee is given the role of an advisor by the more seasoned employee.
The mentor’s expertise from his own prior experiences allowed him to comprehend the challenges his mentee is confronting and to direct the learning process in the direction of comprehension.
This paradigm performs admirably, however, its scalability is a drawback.
3. Providers of Online Learning
These courses have undoubtedly been seen by you, and you have probably even taken one.
Among these are Coursera, edX, Linkedin Learning, OpenSesame, and countless others. You have a personal account with each of those providers, where the software logs all of your learning data to provide you with new, pertinent courses.
The platform will offer you more advanced courses or information on the topic once you complete one course.
4. Lookup engines
These are the most often-used type of individualized Learning. Google responds to your queries with tailored responses.
Google will remember that if you enjoy reading, you might order books online or visit various publication sites to find a new book. Therefore, the algorithm will present you with the greatest deals from online bookstores if you Google “Harry Potter.” In addition, if you enjoy watching movies and do so frequently online, you will see responses that have to do with movies.
Even if “Harry Potter” is the same subject, Google is aware that it contains a different kind of content.
The underlying technology, known as Knowledge Graph, links together many parts of the disciplines.
Finally, you can view the outcomes that are most pertinent to you. You can find images and profiles of Daniel Radcliffe, the actor who played Harry Potter, as well as information about the book, the movie, and the character.
All of this occurred because Google employs personalized learning to deliver the most pertinent information to users as possible.
5. A case of corporate training
Using personalized learning, the Colorado-based helicopter medical transport firm Air Methods improved its pilot training curriculum.
They used an artificial intelligence-based cloud-based learning system to identify the areas where pilots were having trouble, and then they presented more information and asked questions in a different way to make sure that pilots understood the material.
The pilots were kept interested by using regular, quick tests and games, and the organization found it simple to identify areas that required additional training.
By implementing personalized learning, the organization was able to decrease the number of in-person, instructor-led training sessions in half and lower the number of onboarding days from ten to five.
Personalized learning in a new era thanks to AI
Artificial intelligence technologies are increasingly being used in personalized learning as they grow more sophisticated, capable of differentiating between individual demands, and configurable.
Data and AI, specifically machine learning, are the two main ingredients of a successful personalized learning program.
An organization can train AI to recognize patterns, connect data and expertise, and give users relevant information at the right time by employing data.
The most difficult part of this is gathering and analyzing employee data.
How to launch personalized learning initiatives in businesses
So what steps should a company take to develop personalized learning?
How can a company assist a worker in getting the information they require while also making sure the response is relevant to their prior knowledge?
A mix of powerful search and personalization engines provides the solution.
To prioritize the content that is most pertinent to the employee’s prior experience, a personalization engine first narrows the possible sources of information, such as documents, web pages, or training materials.
We’re used to utilizing search engines, like Google, daily.
In a business setting, it might be a different engine for each information system or a more sophisticated engine that can query numerous backend systems and integrate results.
But how can a company personalize such findings so that they are, for instance, relevant to the employee’s prior work history?
Or, to be more specific, how can we know what prior experience the individual has?
How can the experience of an employee be digitalized?
1) The first step in that manner is to list the skills and knowledge levels that each employee has already acquired.
It might be done, for instance, by utilizing text analytics to analyze their CV or by having them complete a straightforward form with knowledge levels in columns and skills in rows (skill or competence matrix).
There are some hints in this information already, but they are not very detailed. Additionally, this is a largely static view that ignores the learning and experiences that occurred after those talents were identified.
2) In the second stage, HR must gather data on training taken, certifications obtained, and anything else relevant to formal learning in addition to the Competence Matrix data gathered in the first step.
As the system is aware of the employee’s level of knowledge and updates it by the formal learning they have taken, this could considerably increase the relevancy of the content found for the employee. As a result, material that is appropriate for an expert but not a novice will not be displayed.
However, that is insufficient to make search results pertinent and unique. The personalization engine requires considerably more specific data about the employee’s
knowledge, learning preferences, learning styles, areas of strength and weakness, preferred information types, etc.
3) The third and most advanced level combines the previously discussed strategies with the gathering of data on all learning processes taking place in real-time.
We’re referring to utilizing the Learning Record Store (LRS) along with the Experience API (xAPI). With xAPI, it is feasible to gather data with great depth on the employee’s learning that is taking place across many different locations.
- It is possible to get information about opened pages and downloaded documents if the company intranet is xAPI enabled.
- Sensors in a factory may track an employee’s movements, and those readings may be gathered as xAPI statements.
- Actions and events inside the simulation could be tracked and saved as xAPI statements if a business employs simulations or Virtual/Augmented Reality (VR/AR) for training.
- All user actions are automatically logged with xAPI if a business chooses a learning experience platform, like Stratbeans, to offer its training.
How may Learning be further customized and improved?
The customization engine might make considerably more accurate assumptions about the relevance of particular content for the employee if it has access to all that information about them.
It could be possible to determine which learning style is best for a person by looking at their previous learning experiences. Do they like longer learning sessions with a broad background offered or should they be brief and directly to the point? Do their learning needs change based on the time of day or day of the week? For instance, does reading an article add more to learning than listening to audio?
When the employee’s knowledge and experience are contrasted with those of other employees and parallels in responsibilities, talents, or learning activities are discovered, even more, pertinent results may be obtained. Based on that resemblance, the material given to the employee could then be made more relevant.
This is also effective for a new hire who has no prior work experience. A recommendation engine will then feed the learner with suggestions that have already been proven to be relevant to those who have come and gone through onboarding materials in the past by analyzing the history of the previous newcomers. At first, similarities like role and department as well as an absence of prior learning history could bring them relevant onboarding materials.
This is merely the beginning of the story. Closing the loop and applying learning to the customization engine will advance it to the next level by gradually improving relevance. The recommendation engine will continuously develop and adapt using machine learning by analyzing learning activities, looking at what employees were choosing for themselves from the results offered, asking if they were satisfied with the results provided, and investigating refined searches.
How modern technologies can help with personalized learning?
There are numerous tools out there, and more are being created annually to assist hone and redefine what personalized learning is.
can be used to automatically queue up learning content that is tailored to the needs and role of the individual learner and help identify and prevent skill gaps, much like the “recommended for you” section of Netflix is powered by an algorithm that looks at what you have enjoyed in the past and delivers new content based on that information.
By handling content curation and recommendation, an AI assistant frees teachers to focus on meeting the urgent learning needs of each individual.
An AI assistant provides curated recommendations for the Learner to engage with next based on their data, including their talents and the learning path they are currently following.
Flexible Learning Pathways
Each person has a unique way of learning and a varied level of background knowledge in a subject. If a business follows a rigid learning path, there is a danger that knowledge that has previously been learned will be repeated, the necessary information won’t be given, and the learner will lose interest.
The Learner receives information tailored precisely to them through technology by employing a flexible learning route.
The necessary information is supplied in a way that is personalized to the Learner, previously known material is automatically excluded, and high-level learners are given access to advanced content.
Processing of natural language (NLP)
Information searches can be improved, expedited, and made more accurate with the use of natural language processing.
Learners may search for the precise information they need, whether it be in text or video, and obtain the precise answers they require to any questions they may have by utilizing the power of NLP.
How can you make a specialized learning strategy?
A personalized learning strategy should be put into place to fully capitalize on the potential of this method of information delivery.
An individual learner’s best learning strategy is outlined in a personalized learning plan, which also includes the learner’s short- and long-term goals, strengths, weaknesses, abilities, and knowledge gaps. It serves as the learner’s road plan for achieving their educational or training objectives.
A customized learning strategy always starts with an evaluation. Knowing your beginning point is necessary to know where you wish to go.
The same assessment process can be utilized for multiple individuals at once, as well as for specific positions, teams, or departments.
They ought to evaluate the material that should be understood and at what level, and they ought to be able to pinpoint any knowledge gaps.
This information can be entered into the system once the learner has finished their assessment, at which point the creation of a learning plan can start.
The same holds for both departments and the entire business. Data from teams can be used to identify a department’s areas of strength and weakness.
2. Establish the objectives and competencies required for each distinct function.
Having established your starting point, you must now choose your destination.
Which qualifications or skills are required for each function or department should be determined.
An organization can then design or alter Learning resources to help people learn or hone such abilities.
The SMART method should be used to define goals and skills so that the departments’ or learners’ progress towards them can be more easily tracked.
3. Produce a general lesson plan.
You have gathered information on the knowledge and skills of the current workforce and determined what needs to be learned by them.
Creating the route to get them there is the next stage. A learning strategy needs to be created.
This doesn’t have to be extremely explicit; a general set of instructions that get the learner from point A to point B will do; as more Learners utilize this route, data will be gathered to help tailor it for subsequent Learners.
4. List various learning methods
A key component of personalized learning is attempting to comprehend how the Learner interacts with the learning materials and which materials will be most effective for this specific learner.
Would they rather watch videos? Maybe they feel more at ease using text and short tests.
This step should reveal the preferences each person has for how they interact with the material.
This data needs to be mapped when it becomes clear what each person responds to the best.
Making learner profiles at this point is also a smart idea. This profile not only can highlight the journey and successes of the specific learner, but it can also act as a guide for future Learners playing comparable parts.
Understanding how each learner has been successful can be valuable data for a training program.
5. Modify learning plans for individuals according to their specific preferences
By customizing the learning plan, using data about the learner’s specific knowledge level, preference of content, and many other factors, an organization can ensure that learners are engaged, gaining skills, and not wasting their time on unnecessary content.
6. Utilize assessments to track learning
Recurring check-ins through assessments should be implemented to ensure that the learning path is successful.
These assessments, both one-on-one and self, should then deliver data to be analyzed that will help give a window into the learner’s journey.
Here, one-on-one assessments are crucial because they provide feedback, problem-solving, goal restructuring, and many other helpful actions that can support learners in achieving their objectives.
These data form the basis of a personalized learning plan, which may be made to be more responsive the more data there is.
7. Examine and gauge
The organization should assess the process frequently throughout the program, seek to consistently enhance the overall learner experience, and ensure that the process is optimized.
Additionally, this is where a company can modify or expand a program.
Learning results are significantly impacted by personalizing the learning experience. Learning is more successful and leads to improved understanding when new ideas are connected to prior knowledge.
To create personalized learning in an organizational setting that is both affordable and scalable, technology solutions are needed.
The collection of an employee’s experiences in digital form on a very detailed level is made possible by technology, such as a combination of Experience API (xAPI) and Learning Record Store (LRS).
Using a combination of sophisticated search and personalized engines, this information might then be used to create a personalized learning experience for upcoming learning sessions.
No technology solution is flawless, of course, but by preventing feedback from a learner’s actions from returning to the solution, the quality of the answers provided to a learner will be continuously increasing.
Employees may benefit from and experience personalized learning if they had that instead of a “figure it out yourself” style of learning.
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