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

 

More Learning Ideas

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. 

More Learning Ideas

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

 

 

More Learning Ideas

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.

More Learning Ideas

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

More Learning Ideas