How to Humanise Online Learning?

How to humanise online learning?

How to Humanise Online Learning?

One of the common pain points of digital learning is its passivity. One of the reasons learners often cite for unwillingness to engage is the lack of the human factor. Learning by oneself in an online environment is not necessarily very fun. While techniques like gamification can help to spark interest and keep motivation high, it might not be enough. However, you could tackle a lot of this problematic passivity already at the design phase. By focusing on making learning more active and human, one can go long way. Therefore, here are a few different tips for humanising online learning.

Humanise online learning with peer interactivity

One of the first contributors to the human factor is peer interaction. When digitalising learning, it’s easy to forget to utilise all three levels of interactivity. While peer-to-peer interaction occurs naturally in conventional classroom learning, it doesn’t online unless you create the infrastructure for it. So, when humanising online learning, it’s critical to enable learners to interact with each other.

The interactions can take many formats. Online discussions or internal social media channels are a good way of getting started. Chats and video rooms can also help to connect remote teams and individuals to each other. Whatever the social framework, usually a common rule applies: it’s not easy to get people to interact without any kind of guidance. Therefore, it’s a good idea to prompt and facilitate the discussions, and design them to be a part of the material.

Make it about the people, share stories

Humans are wired to retain, respond and relate to stories. However, training content often tends to stick to the facts and figures. The content moves on an abstract level, often with little explicit relation to the jobs or people in question. This doesn’t do wonders for learning results, nor is it particularly human.

One way of humanising online learning is to shift focus away from the content to stories. Less is more is a good approach when it comes to data and factual information. When you go less on that front, you’ll create room for more storytelling. Now, you can plan the stories meticulously like your marketing department might do. But it could work to also let your people share their stories. A personal testimonial or a story of a use case of the things that is being learnt is likely much more valuable than some facts that end up forgotten anyway.

Experiment with adaptive or personalised learning

Another way of making online learning a more human experience is to personalise it. Personalised learning is about finding out the learner’s interests, needs, requirements and ways to add value, and providing resources catering to them. A one-size fits all passive online learning course is about the least human experience there can be. Personalising the experience, tailoring it to the learner, can take some of that feeling away.

Adaptive learning could also accomplish similar goals. The fundamental idea of adaptive learning is slightly similar to personalisation. The learning content and its sequence doesn’t resemble a linear path, but rather a spider’s web. Based on performance on previous parts and the learners perceived knowledge and skill levels, you direct them to different bits of the material. Similar to before, learners feel that you’ve designed the learning for them, instead of a profile of averages.

Provide comprehensive and rapid support

Finally, there’s often a lot of human touch missing from getting help with one’s learning. In a lot of cases, learners tend to get left alone with the courses and programs they are completing. If they encounter a problem, they are supposed to solve it on their own. If they have questions, they might be able to ask somewhere, but getting a response might take a long time. All of this causes interruptions to the learning process.

Therefore, when humanising online learning, it’s important not to forget the learning support either. Give your learners ways of reaching out to the trainers or admins. Whether it’s usability issues or questions about the content, make it easy to contact the relevant people and ask for help. Having access to a safety network of this kind can help to alleviate a lot of the stigma when it comes to online learning.

Final words

Overall, as organisations make the transition towards online learning, it’s important not to forget the human factor. Passive consumption of online content gets too tedious fast, and learners disengage. Humanising the learning experience can keep them engaged, and feeling that they’re not just the victims of a cost-cutting exercise. Hopefully these tips prove helpful. In case you need help in making online learning more human, feel free to reach out to us. We’d be happy to help.

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How to Make Your Corporate Learning Relevant? And Why It Matters

How to make your corporate learning relevant?

How to Make Your Corporate Learning Relevant? And Why It Matters

One of the bigger problems hindering the impactfulness of corporate learning is not lack quality content or great delivery methods but relevance. Employees often see the corporate just assigning them new training, with little consideration to whether it actually helps them or not. Furthermore, many organisations still do employ a one-size-fits-all type of approach to learning, which is setting up for failure. However, many are realising that providing employees with relevant learning opportunities is crucial. Thus, let’s look at firstly why relevance matters in learning, and then how we can deliver something that truly resonates with the audience.

Why relevance matters in corporate learning?

Relevant learning is crucial for a number of reasons. Firstly, it’s the way our brains work from learning standpoint. Relevant and meaningful activities that resonate emotionally and connect to existing knowledge help form new neural connections and pathways and build long-term memory storage.

Secondly, it’s a matter of engagement as well. Learners that don’t “connect” with the topic or material are much more likely to disengage, resulting in low retention. Furthermore, they might even lose the motivation to try (and it’s harder to win them back afterwards).

Thirdly, relevant learning is important because the fundamental goal of corporate learning is not just to acquire knowledge, but to transfer it into new work practices and behaviours. And change is hard. If we want to elicit behavioural change, we have to address the specific situations and challenges of the employees, rather than simply providing facts and information and leaving them to figure out the hardest part themselves.

How to deliver relevant learning?

So, how could we deliver learning experiences, whether online or face-to-face, that overcome the challenges above? Much of it deals with personalising learning. While that’s another article’s worth on its own, we thought we’d pick a few fundamental things that are easily forgotten.

  • Go learner-centric: designing and developing your learning experiences in a more learner-centric way helps to tackle a lot of the challenges. Spend time listening to your learners, their challenges, problems, contexts and situations. Involve them in the process as much as possible. Don’t deliver “content”, deliver relevant learning experiences that help them succeed.
  • Create scaffolding. Use the information and data you gain from your learner-centric design process to create scaffolding. Relate what is being learnt to the learners’ previous knowledge, learning history, professional experience, job functions, market areas etc.
  • Keep it fresh: remember to revise and update your activities regularly. The subject matter doesn’t necessarily change, but the context will constantly. Keep your examples, scenarios and cases current, which in turn helps in the scaffolding.

Does technology play a part in this?

One of the bigger promises of today’s and tomorrow’s learning technology is the ability to deliver more personalised learning everyone. While tools like AI are still relatively new in the learning and education space, there’s already quite a lot of good that can be done today.

At the very least, the new abilities to collect learning data and determine real learning needs help to fuel the learner-centric design process. Increasingly many learning environments also use algorithms to recommend relevant learning content and personalise the experience. Some more advanced ones venture into adaptive learning, where the individual learning path shifts based on a number of factors.

But even if you don’t have access to such tools or resources to buy into such technologies, don’t worry. Fundamentally, it’s all about doing the simple things right, and spending time to figure out the real needs. One thing that gets you pretty far: talk to your people!

Final words

Overall, relevance seems like a much undervalued factor in learning. However, the science and research is pretty clear: you need relevant learning to get results. In the corporate world, that’s even more evident, as studies have shown that people learn the new, but still easily revert back to the old ways of doing things. So, consider starting to help your employees and learners succeed by focusing on what helps them. And if you need help in going learner-centric, or leveraging technology to design more relevant experiences, we can help. Just drop us a note here.

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How to Help Learners Succeed with Personal Learning Analytics?

How to use personal learning analytics to help learners succeed?

Personal Learning Analytics – Helping Learners with Their Own Data

Generally, the use of data and analytics in workplace learning is reserved for a small group senior people. Naturally, learning analytics can provide a lot of value for that group. For instance, data-driven approaches to training needs analysis and measuring corporate learning are quickly gaining ground out of the need to prove the impact of workplace learning initiatives. However, there could be further use cases for those analytical powers. One of them is helping the learners themselves with personal learning analytics.

What is ‘personal learning analytics’?

Like the title may give away, personal learning analytics is just that: individualised information made available to the learner. The major difference with conventional “managerial” analytics is that most of the information is about the learner in question. Whenever that’s not the case, the information of others would always be anonymised. A few exceptions could include e.g. gamification elements which display user names and achievements. So, effectively, it’s all about giving the user access to his/her own data and anonymised “averages”.

How can we use personal analytics to help learners?

One of the challenges in conventional approaches to workplace learning is that the process is not very transparent. Often, the organisation controls the information, and the learners may not even gain access. However, a lot of this information could help the learners. Here are a few examples.

  • Comparing performance against others. While cutthroat competition is probably not a good idea, and learners don’t necessarily want others to know how they fared, they can still benefit from being able to compare their performance against the groups. Hence, they’ll know if they’re falling behind and know to adjust their effort/seek new approaches.
  • Understanding the individual learning process. All of us would benefit greatly from information about how we learn. For instance, how have we progressed, how are we developing as well as how and when do we engage with learning. Luckily, personal learning analytics could tell us about all of that. The former helps to keep us motivated, while the latter helps us to identify patterns and create habits of existing behaviour.
  • Access to one’s learning history. We are learning all the time and all kinds of things. However, we are not necessarily very good at keeping track ourselves. If we just could pull all that data into one place, we could have a real-time view into what we have learned in the past. Potentially, this could enable us to identify new skills and capabilities – something that the organisation would likely be interested in too.

Towards self-regulated learning

Across the globe, organisations are striving to become more agile in their learning. One key success factor for such transformation is the move towards more self-regulated learning. However, achieving that is going to be difficult without slightly more democratised information.

If the learners don’t know how they are doing, they cannot really self-regulate effectively. And no, test scores, completion statistics and annual performance reviews are not enough. Learning is happening on a daily basis and the flow of information and feedback should be continuous. Thankfully, the technology to provide this sort of individual learning analytics and personalised dashboards is already available. For instance, xAPI and Learning Record Stores (LRS) enable us to store and retrieve this type of “big learning data” and make it available to the learners. Some tools even provide handy out-of-the-box dashboards.

On a final note, we do acknowledge that the immediate applications of “managerial” learning analytics likely provide greater initial value to any given organisation. And if you’re not already employing learning analytics to support your L&D decision making, you should start. However, once we go beyond that stage, providing access to personal learning analytics may be a good next step that also helps to facilitate a more modern learning culture in the organisation.

If you’re eager about learning analytics, whether on an organisational or personal level, but think you need help in figuring out what to do, we can help. Just drop us a note here, and let’s solve problems together.

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How to Deliver Personalised Learning Experiences?

How to deliver personalised learning experiences?

How to Deliver Personalised Learning Experiences?

We have moved on to an era of personalization. One size no longer fits all (well, it never did…) and we’ve realized that. In our daily lives, everything is getting more and more personalized. And that’s increasingly the direction in the sphere of professional and corporate learning as well. While people are struggling with corporate training due to the lack of relevance and meaningfulness, they have also got access to many consumer grade learning services that offer highly personalised experiences. Having seen the greener pastures, people are nowadays looking to receive similar personalised opportunities in the workplace as well.

This naturally has become a challenge for corporate L&D teams as well, as delivering personalised learning experiences requires more effort than the one size fits all – approach. However, it’s not just a burden, as investing the effort required generally results in higher learner engagement and better results.

So, how should we go about all this? Here are a few fundamental concepts to consider for delivering effective personalised learning.

Personalised learning experiences should give control to the learners

Traditionally, corporate training and learning follows a top-down approach. There’s often a single, highly linear way of progressing through a course. Furthermore, there’s a tendency to pack simply too much content into learning activities to ensure there’s something for everyone. But none of this really works.

Rather, the learner should have much higher control on the what, how, when and where of the learning experience. Content should be personalised based on data, while providing omnichannel access to it. Furthermore, learning experiences should be “unrestricted” and non-linear, enabling employees to fill their knowledge gaps as they need.

Now, let’s look at a few important things in more detail and how to implement them.  

Let everyone learn at their own speed

We all learn slightly differently. As our experiences and prior exposure to topics varies by a lot, different individuals require different times to master a particular topic. While providing some kind of a time framework for learning progress is probably required, you shouldn’t control it too much. Let learners progress at speeds they are comfortable with, and provide them with the support they may need. After all, all jobs are different too and everyone doesn’t have the same time to commit to learning.

Stop pushing, focus on pulling

Mandatory is a dreadful word. Psychologically, making learning mandatory is not necessarily a good option. Unless the learning is truly great, and matches the needs and context of the employees perfectly, it’s likely that the employees feel you’re wasting their time. Hence, the learners don’t really learn and the L&D doesn’t get results.

Instead of ‘pushing’ content, organisations should focus on ‘pulling’ the learners to it. By making relevant resources available and known through data analytics, machine learning and recommendations, you’re putting the initiative on the learner. Thus, the uptake is of higher quality, due to the existing intrinsic motivation for the topic. By enabling choice, learning tends to also become more self-regulated, autonomous and continuous. It’s no longer a nuisance, but rather a meaningful medium of support for both the short and long term goals of the employees.

Align learning with employees’ objectives

Like previously mentioned, most of corporate learning fails because of lack of relevance. Employees don’t see the value in the training or realistic ways of implementing it at the workplace. Thus, there can be value in letting employees set their own learning objectives. Setting personal learning goals fosters ownership and responsibility. Furthermore, it also enables multiple definitions of success, instead of just the one “defined by the corporate”. After all, we learn for different reasons as well. Some are learning to climb the career ladder, some to enable lateral moves and some just to stay competent and up-to-date.

As you let the employees set their own objectives, you can also offer them personalised learning paths. People with different goals probably need different types of content and resources to tap into.

How does technology help in delivering personalised learning experiences?

While you can do a lot of the above even without technology, it certainly helps. Different learning technologies help to streamline the whole personalised learning experience delivery process. Advanced data capabilities available today help to ensure that the approach remains scalable, and minimal manual intervention is needed.

The leading platforms out there provide capabilities for curating personalised learning paths. They also provide ways of collecting learning data on an individual level. Connecting this with performance data gives an unparalleled picture of the individual’s learning and resulting effects in performance.

Final words

Personalised learning is not just a gimmick, but rather a topic requiring careful explorations. It not only helps to satisfy the demands of employees, but ultimately has the power to bring corporate learning activities to a whole new level of relevance and context, and consequently, results. So, start looking at your workforce as individuals with varying needs, rather than as grey mass represented by numbers on an excel file. And if you need help in that, or just someone to kick you in the right direction, we can help. Just contact us here.

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5 Ideas for Leveraging Intrinsic Learning Motivation

Intrinsic Learning Motivation

Intrinsic Learning Motivation & 5 Ideas for Leveraging It in Digital Learning

When it comes to corporate learning, motivation is a tricky subject. As we know, motivation comes in two kinds – extrinsic and intrinsic. Learning itself is arguably an area where intrinsic motivation is prevalent. People find meaning in developing themselves and acquiring new skills. However, statistics of corporate learning don’t always support this line of thought. Motivating learners seems to be difficult, and consequently many organisations have adopted maybe an unnecessarily large focus on factors of extrinsic motivation – rewarding and punishing for success or failure in learning activities. However, as learning in its natural state is one of the most psychologically rewarding feelings, it might be good to step back slightly and consider what you can do to leverage your employees’ intrinsic learning motivation.

1. Shift control to the learner to develop a sense of responsibility

As it is, corporate learning tends be a very top-down exercise. From the learners’ point of view, it may seem that their professional and career development is dictated by someone with limited exposure and oversight to their actual needs and responsibilities. Does it have to be that way? Not necessarily. Let the employees have more control over their own learning. Let them make choices on what, how and when to learn. When you give freedom of choice, you’ll evoke a natural sense of responsibility, which goes a long way to to secure intrinsic learning motivation. To take the idea one step further, you could also enable the sharing of user-generated learning content.

2.  Ensure learning content is relevant and applicable

A major hurdle in learning engagement is that employees don’t see the content as relevant. Often, the organisations may have themselves to blame for over-reliance on one-size-fits-all and off-the-shelf programs. If the content moves on an abstract level, learners are more likely to have a hard time identifying ways to implement it in their daily jobs. Thus, it’s vitally important to spare some thought on the real-life applications of the given learning. For practical skills, tools like learning simulations provide a great medium of linking the training with the daily jobs.

3. Give constant and constructive feedback

Giving learning feedback also goes a long way for intrinsic learning motivation. With proper feedback, learners can enjoy a sense of accomplishment. Furthermore, it helps them to understand when they’ve made mistakes and how to improve on them. Try to avoid negativity and bestowing a sense of failure upon the learners and remember to level the feedback with the complexity of content.

4. Encourage collaboration and sharing for intrinsic learning motivation

Learning doesn’t, and probably shouldn’t, be an individual effort. From a motivational standpoint, the feeling of contributing to a larger social context, i.e. social presence is powerful. Whereas the shift in control is likely to help learners develop a sense of personal responsibility, this helps them to develop a shared responsibility. You can use both collaborative and competitive elements to achieve the goal. Collaborative learning activities help to engage through social commitment, whereas different gamification techniques can help to foster friendly competition.

5. Personalise learning experiences

Finally, personalisation is yet another powerful tool in sustaining intrinsic learning motivation. The “difficulty” of content comes across as one of the most important factors. If the learning content difficulty completely matches the employees’ current skill level, they are not likely to engage deeply. Instead, you’ll want to give your learners a challenge which they can overcome to get the sense of accomplishment fuelling the intrinsic motivation. To provide a diverse group of learners with the content of the right difficulty, you may consider an adaptive learning design method.

Are you having trouble motivating your learners? We can help by auditing your learning content and delivery and provide tailored suggestions on improving both. Just contact us

 

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Future of Instructor-led Training in the Digital Era

Future of Instructor-led training

Future of Instructor-led Training in the Digital Era

Instructor-led training (ILT) has been a major medium of learning delivery in corporates for a long time. However, during its long history, instructor-led training and the methodologies used have not evolved all that much. As a result, ILT is struggling with problems of sustaining results, scalability and flexibility. Furthermore, ILT is having a hard time aligning with L&D trends such as personalisation and performance-centricity. Hence, we thought it might be useful to present some tips on leveraging technology to nurture a paradigm shift towards better ILT.

How can we produce better results with ILT?

The problem with ILT is that it tends to be rather transactional. Due to financial and time constraints, corporates cannot have trainers spend several sessions focusing on learners’ individual problems. Furthermore, the learning experience is not spaced over time. Hence, new knowledge is easily forgotten, and results remain poor. To produce better results, training needs to adopt a more blended approach, which also helps with the scalability and flexibility.

A good blended learning approach can be a mix of digital learning activities and instructor-led training. Digital elements such as refreshers, discussions, microlearning and evaluations can be used to support the learning over time. With a careful structuring of learning journeys, employees come to ILT sessions already tuned in to the topic. Hence, it’s much easier for the trainer to pick up the pace and create impact. Furthermore, trainer-led facilitation can continue even after the session.

Instructor-led training 2.0 – facilitating across platforms

To sustain a behavioural change in the learners – to produce real results – requires continuity. Behavioural change doesn’t happen overnight or with a single training activity. Therefore, it’s important that we keep the engagement going. Instructor-led facilitation is a natural way of doing this. Instead of losing more productivity to the classroom, trainers should equip themselves to meet the learners across platforms.

For instance, once the ILT session has gone by, trainers can move to social media tools. Ideally, your digital learning platform comes with a social learning feature of managing discussions. If not, don’t you worry! You don’t need expensive tools to facilitate. It’s highly likely that a vast majority of your learners are already using social media and communication tools (e.g. WhatsApp, WeChat, Facebook). You should tap into that by having trainers facilitate further learning across those platforms – the employees are already there! Sure, it’s not quite as sophisticated as integrated social learning tools with powerful analytics capabilities. Yet, even small things can have big impact. The important thing is that trainers are making themselves available for performance support, the ‘Pull’ type of learning.

Personalising Instructor-led training

Finally, the personalisation problem of ILT is an area in which you can go a long way with proper technological support. In learning, one size doesn’t fit all, it never has. Yet, highly structured ILT activities are aiming to do just that.  Personalised learning is all about understanding the learners’ context: what is relevant? What helps them succeed in their daily jobs? What kind of experiences and backgrounds are the learners building on?

Advanced learning data capabilities and analytics help tremendously in this regard. Trainers can zoom in on each individuals’ skills development in real-time, not forgetting non-learning experiences. This way, trainers are able to deliver learning catering to each individual’s unique needs. This helps in sustaining the paradigm shift from knowledge to performance focused learning and ultimately, better results.

Are you using technology to support your organisation on its way to the future of instructor-led training? If you think you need help, you can always schedule a free consultation with us. 

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AI in L&D – 3 Low-hanging Fruits for Implementation

AI in learning

Implementing AI in Learning – 3 Low-hanging Fruits to Start with

Artificial intelligence (AI) is one of the technologies that is going to fundamentally change learning. With applications from content curation to predictive analytics, AI applications provide powerful tools for making learning more efficient. However, with such a wide range of applications and use cases comes ambiguity. For many L&D and HR professionals who are not domain experts in AI, it may be hard to grasp all the potential. Furthermore, figuring out whether and how to get started with AI can be troublesome. Hence, we’ve compiled 3 different value-add cases for AI in learning.

1. Using AI in learning management to eliminate manual work

When delivering digital learning, one of the least productive and most menial of tasks is the learning management. Professionals use the digital learning environment, or an older LMS, to manage and assign courses, materials and produce reports. Naturally, this is work that needs to be done, but is very menial and repetitive in nature. In terms of productivity, the work is low value-add.

Luckily, machine learning and artificial intelligence can and will eventually take over practically all of this work. This will free the learning professionals from a time-consuming but unproductive load of work. Hence, they are able to focus on designing and delivering the learning, which is the true value-add part. Furthermore, AI is also very likely to outperform people in tasks like this – there’s less room for human error and the computer doesn’t forget. Such using of technology to automate repetitive tasks should be the first application of AI in learning for every organisation.

2. AI in Learning and performance support chatbots

Many organisations have embraced AI when it comes to chatbot applications. Smart chatbots based on AI can provide personalised and customised suggestions to user inquiries. These form an easy tool for on-demand learning and performance support. The users can get answers to their queries quickly, resulting in minimal downtime and better productivity. Think of it as an advanced interactive search engine. There’s no need to go through lengthy documents, manuals or guidebooks. The chatbot is able to pool from the organisation’s knowledge, the previous users and best practices to provide answers in a blink of an eye. Whatever the problem, the machine can likely provide good suggestions as long as it has been exposed to relevant data.

Read more about using chats and chatbots in learning here.

3. Using AI to personalise learning for every employee

Finally, a third use case of AI in learning is to provide personalised learning experiences, as well as designing adaptive learning. AI can collect data, analyse and learn from human behaviour far beyond the human ability. If our learners are having problems with the content, AI detects it, and offers them another set of material with different modalities or difficulty. Furthermore, the AI can suggest additional resources complementing the learner’s existing skill-set. Delivery of content will then be in formats that the AI has detected to be most efficient for the learner (videos, simulations etc.). This improves learning engagement and yields better learning results.

Naturally, AI will have many more applications as the technology develops. However, you can already take advantage of it to eliminate manual work and provide better learning experiences.

Do you want to understand the functionality of AI in learning better? Are you looking to implement AI in your organisation’s L&D? We can help you get started, just contact us here

 

 

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Adaptive Learning Design – What Is It and How to Use It?

adaptive learning design

Adaptive Learning Design – What Is It and How to Use It?

One of the key trends of today is personalisation. Whether it’s shopping, marketing, watching movies or any other thing, we have come to expect personalised experiences. The same is true for learning also, even in the corporate setting. Employees expect the employers to provide learning that is relevant and empowers them both professionally and personally. For the best learning experiences and results, the employer’s and employee’s interests need to align. The modern learner expects learning content of the right difficulty, delivered with the right mediums, reflecting their professional and personal interests. Luckily, adaptive learning design helps us do all of that, and more.

What is adaptive learning design?

Adaptive learning design means that learning journeys transform from straight lines to something resembling a spider’s web. Each milestone of progress influences what the learner sees next and hence where the learner moves on that web. By taking into account the learner’s prior experience, roles and operational experience, we can assign content that is complementing their existing competencies and reinforces their skill-set. Endless amount of factors can influence the content assigned to the learner: prior learning results, prior job experience, age, position, geographical location or own interests.

Adaptive learning design aims to produce relevant learning taking into account as many of these factors as possible. More personalised learning improves engagement, motivation and retention. Employees gain knowledge on items that benefit them in many areas – professional careers as well as personal lives. Thus, they are more likely to stay engaged in their jobs, maintain continuous improvement and deliver better work.

How to make adaptive learning work?

Adaptive learning can be designed in many ways, but one factor is crucial – data. For adaptive learning to hit the spot, the organisation needs to leverage big data and pool information from different sources. Once the required data infrastructure is in place, here are some methods on getting started with adaptive learning.

  1. Initial Competency Mapping

    Before the learners start a course, you should test their existing knowledge on the subject. Then, assign learning content accordingly. This ensures that secret subject matter experts don’t have to waste time on basis level things. Also, the beginners don’t get overwhelmed by too much content too soon.

  2. Post-learning evaluation

    You should also evaluate the learners after they have completed the course. People learn at different paces and in different ways, and good evaluation helps to support learners accordingly. Slower learners can keep on reinforcing what they learner, and faster learners can start to tackle other topics. You can do this by testing, but perhaps a better approach would be to leverage learning data to analyse the learners’ progress. Thus, you can eliminate some of the dreadful formal assessment.

  3. Curate multiple versions of the content

    Naturally, personalised and adaptive learning requires curating the content into the spider’s web model. You can start by mapping out the same content for different positions and seniority levels in the organisation. This can also help in learning engagement, as enthusiastic and competent employees can take on learning above their current role, hence preparing them for more advanced duties.

  4. Get Artificial Intelligence (AI) to automatise the cycle

    Once you’re accumulating the data and have built content into the spider’s web, it’s time to dive into AI. AI tools can analyse this data and help to direct the learner in their own personal journey by recommending materials suitable for their personal style, seniority, experience, etc. With proper integration, AI can take into account changes such as position, job duties and operational issues in real-time. Furthermore, advanced AI capabilities can even help the learner to understand what learning style works best for them and assign materials accordingly. AI in learning is still new, so many learning systems don’t necessarily have the capabilities. But some do, and we’ll be happy to recommend them.

Finally, you should embrace continuous iteration and improvement. AI and advanced analytics provide us deep insights on what works and what not. You should use these to continuously improve the content and get the highest return on investment for your learning.

Would you like to implement adaptive and more personalised learning? Are you interested what AI can do in this aspect? Let us know, we’ll help you.

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Personalised Learning – 3 Things That Go a Long Way

Personalised Learning

Personalised Learning – 3 Things That Go a Long Way

One of the traditional caveats of corporate learning and training has been the lack of personalisation. Due to, among other things, company policies and regulation, organisations are sitting their people through several hours/days/weeks of trainings annually.  In many cases, employees fail to see the relevancy of these training programs, which demotivates them. “Why am I being trained on this? I’m not involved in anything like this in my daily job”. This kind of thinking is commonplace and the frustration for the lack of personalised learning evident.

While we move from compliance driven training to skills driven learning, we need to seriously reconsider our approach. Ironically, it’s information technology rather than persons which is the best in driving personalisation. When training for compliance, you can tick the box as long as the hours are fulfilled. But when you are developing for skills, knowledge and capability, the results define success. And good results require learning engagement, in which personalisation helps tremendously. To understand and drive personalised learning, here are three simple things you can do to personalise your learning.

1. Linking organisational roles and experiences to learning

Naturally, organisations employ people of various degrees of capabilities, knowledge and experience. However, in most cases, there’s quite a clear link between seniority or experience and the individual learning needs. Thanks to technology, we can take advantage of this kind of a link. We can feed our learning systems with information from e.g. the company’s Active Directory (AD) and HRM systems. We can retrieve all necessary information regarding e.g. seniority, tenure, experience, prior learning with this kind of data flows. Once we have this data, we can use it with the learning system to assign learning automatically, based on all these factors. This is the first step of improvement – providing personalised learning based on perceived knowledge.

2. Providing personalised learning based on skills and competencies

Moving to a more individual level, the next step is providing personalised learning based on skills and competencies. Naturally, skills and competencies are a bit harder to track than the roles and seniority. However, by employing seamless testing and data analytics, we can get a better picture of our employee’s actual capabilities. By analysing our employees’ learning history, results, experience and projects completed, we can predictively pinpoint where an individual employee needs learning.

Furthermore, we can complement the above by structuring our learning material in a new way. Firstly, relevant learning materials should include an initial capability assessment. Upon completing this, and based on the results, the learning system forwards the learner to a personalised path on the learning materials. If you scored poorly, you’ll get beginner level material. If the system perceives as you a subject matter expert, it will give you more advanced topics to deal with. Doing this, we give our employees learning content with the right difficulty level. Hence, we don’t overwhelm (too difficult) or bore (too easy) our learners. Essentially, in this model the learning architecture is more like a spider’s web rather than a straight line.

3. Give the learners the chance to personalise their own learning

Finally, a major source of learning motivation is a natural interest in the subject matter. Often, the scope of corporate learning doesn’t extend quite as far as our personal interest would take us. When finding things interesting, we would be happy to dig in extensively but the corporate eLearning only covers the basics. Of course, with limited resources, corporates can’t provide extensive material on all topics. However, we can reap the benefits of the connected ecosystem called internet.

When developing personalised learning materials, we should acknowledge our limitations. But, instead of stopping there, we should put in a little bit of extra effort to guide our learners. There are plenty of outside resources on any given topic which our learners could use to satisfy their personal interests. Off-the-shelf / open source content is seldom a good solution for corporate learning, but in this case, it can help. To help our learners, we should attempt to identify quality content which we can link to. Sure, it might be outside of the current scope of our corporate training, but it can provide a relevant learning opportunity for many. By allowing our learners to seek out subject matter they are interested in, we can positively influence their personal skills development. Even if the learning is not currently related to their scope of work, it might be soon.

Are you looking to provide more personalised learning to enable relevant learning paths across the organisation? We are happy to help and advise you on a data-driven personalisation approach. Just drop us a note here

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