Adults learning online with computers

What is learning?

Part I: Acquiring knowledge

What makes for learning?

What is good learning like?

What types of learning are there?

Depending on your answer, your solutions in your learning program may differ drastically.

Take a minute, and think about how you would define learning? Is the first thing that comes to mind, the increase in the amount of knowledge? What about the ability to use a new skill? Do you think about being able to take part in a new community? Would you go as far as thinking learning is about building knowledge or creating new solutions?

Learning is one of those concepts for which an all-encompassing definition will never be reached. Rather, it is best approached with metaphors. Anna Sfard’s article On Two Metaphors of Learning describes the discussion around the topic around 20 years ago.

There is more to the question about the nature of learning than just semantics. Therefore it is a fruitful starting point for designing your learning program.

Learning as an acquisition

The idea that learning means gaining or increase in the amount of knowledge is a self-evident one. When I know something today that I didn’t yesterday, I can say that I have learned something in between. The amount of information in my head has increased. The acquisition metaphor sees the human mind as a container, which can be filled with materials. Learning is about becoming an owner of these materials.

Researchers have presented different frameworks and mechanisms for the process of learning. While earlier theories talked about the passive reception of information, later the move was towards active construction by the learner and the importance of social interaction. At the moment, learning is more or less seen as an endless, self-regulating process taking place in the interaction with other learners, teachers, and learning materials.

Engaging the learners

The challenges of education are to engage learners, take into consideration their prior knowledge, and to offer different kinds of learners the right kind of support. To make things interesting, learners should be offered ways to operate meaningfully, use their earlier knowledge, measure their competence, and apply what they have learned. Things like bite-size learning, applications, and the ability to discuss with peers can make things more interesting.

Essentially, this is what most learning programs are about: whether it is a compliance training, induction to tasks, introducing new tools, or teaching skills. The goal is to teach the learner information or skills they don’t have. The role of the teacher or the learning program is to help the student attain knowledge by delivering, facilitating, mediating, and finally measuring learning. Once attained, knowledge like any other can be applied to a given task and shared with others.

The pathway towards the learning goal

When you design a learning program, you start with a container of knowledge either held by an expert or within documents. Processing the learning program includes structuring the information to what the learners are supposed to acquire. And the path towards that learning goal is designed in a way that it offers them meaningful activities and ways of interacting.

This is what most learning programs cover. They are designed to increase the amount of knowledge and skills. But taking the definition of learning a bit further might help you to rethink your learning programs. 

topi litmanen

About the Author

Dr. Topi Litmanen worked as a Chief Educational Scientist in Claned. He was responsible for ensuring, that the pedagogical aspects of Claned are based on evidence-based learning research.


An alternative to discussion forums in online learning

An alternative to discussion forums in online learning

Discussions are an essential part of online learning. Engaging with other learners is both pleasant and productive. Conversations create divergent thinking and help to develop thinking skills. Moreover, they increase motivation and engagement. Learners enjoy getting feedback from their personal views and reading what others think.

But not all discussions are alike. There is a difference between ignorant comments and rigorous argumentation. You can do a lot with well-designed assignments, but the platform has as an effect as well.

The effect of the platform

Yanyan Sun and Fei Gao examined the effect of a threaded discussion forum and a contextual social annotation in their article in The Internet and Higher Education journal. First, they assigned course participants randomly to groups. The groups did the one part of a course on a discussion forum and another with a contextual annotation tool.

The results indicated that the two environments impacted student participation, knowledge construction processes, and the nature of discussions. Participants posted more comments in the contextual discussion compared to the discussion forum, and the discussion was more specific and precise. Comments on the discussion forum were closer to general commentary. Learners reported that it was easier for them to exchange ideas on the contextual discussion platform because the comments were located next to the place they wanted to comment on.

It is clear that choosing the right tool is essential. Digitally mediated communication is just that: highly mediated.

Discussions in Claned

In Claned we have chosen to put discussions alongside the learning materials. You can tag any part of a video or highlight any text in content, and tag comments that specific point in the article. Notifications alert and direct users to the location of learning material for anyone following a discussion. The discussions build in the margins of any learning material engaging learners with the content.

The feedback from our users is similar to what Sun and Gao reported: learners create more spontaneous comments and there is more discussion. Furthermore, skilled educators have come up with ways to use the contextual discussion feature to engage their learners. You can easily instruct learners on what to pay attention to when going through your materials. This lets you guide discussions on topics that are important for the learning program.

Interested in learning more about Claned learning platform? Do you want to know how to make your online learning more engaging?

Email us at

topi litmanen

About the Author

Dr. Topi Litmanen worked as a Chief Educational Scientist in Claned. He was responsible for ensuring, that the pedagogical aspects of Claned are based on evidence-based learning research.


Creating engagement with social learning

Your learners are social by nature. Utilize it to make your courses more engaging.

Providing social features is a key ingredient for engagement in any online service – let alone in a learning service. Everyone using the internet has some experience with social media and has connected with other people or content when using it. Although the web around us is highly social, it is hardly leveraged in online learning. I will give you examples of how to make your courses more social and engaging, for optimal learning results.

Let’s enable social learning

Normally, in online learning environments, you will find deserted or heated discussion forums. Even if there is engagement, not all discussions are alike.

There is a difference between trivial comments and rigorous argumentation. Just having a discussion for the sake of having it is not a good goal. Discussions should be used to leverage learning. You can do a lot with well-designed assignments, but a learner-centric platform has impact as well and delivers real results.

Let’s create engagement

When creating engaging online courses the beginning of it is always important. Because the course introduction is the first impression your learners will get. To make the participants feel welcome and more comfortable in collaborating with each other, you should put effort into the introductions. At the beginning of the course, ask your participants to introduce themselves. An easy way to get all participants to introduce themselves is to ask learners to do it while watching an introduction video or reading an introduction document.

If you want your participants to put more effort into their introductions, you can design it to be an assignment. Make them describe their background and prior experience with the topic. Or if you want, you can ask them to do short videos about themselves. Once the participants have all uploaded their introductions, encourage them to comment on their peers’ intro videos.

Let’s initiate collaboration

One way to make your current materials more engaging is to design collaborative tasks participants could do while studying. Instead of just reading or watching a video, you can ask them to actively work and explicate their prior conceptions, experiences, and views. Contextual discussion within a document has clear benefits: The discussions are more active and spontaneous.

Let’s encourage participation

It is not enough to design collaborative content to spark engagement. You also need to be there for your learners. As an instructor, you can provide opportunities for learners to show their knowledge to others and highlight active contributors. Remember to encourage participation and constructive criticism and discussions, not only continuous fact-checking. Lead with an example, share, and participate and give positive feedback to all those who share.

When designing for collaboration, try not to create rigidly scheduled processes of collaborative learning. Learners will end up feeling rushed into having an opinion or coming up with too many comments. Avoid negative highlighting and punishment for inactivity as these methods rarely create an engaging and safe environment for collaboration.

Let’s enhance your courses

If all this seems new to you, keep calm and consider seeking assistance from people who have more experience in social online learning. This way you can avoid the most common pitfalls and benefit from the lessons learned by others. The fact that you want to create more user-friendly and socially engaging courses means that you are already on the right track.

P.S. We have created workshops especially to help our customers to tackle the challenges in social learning. You can contact and we will help you make your courses more social.

topi litmanen

About the Author

Dr. Topi Litmanen worked as a Chief Educational Scientist in Claned. He was responsible for ensuring, that the pedagogical aspects of Claned are based on evidence-based learning research.


Three key benefits to demand from learning analytics

The best thing about learning analytics is that it informs on issues surrounding learning usually hidden from the naked eye. That is, it provides ways to peek into the details of the learning process. More importantly, analytics provides new ground for evidence-based learning interventions. Analytics doesn´t take away the need for a skilled educator but instead highlights the importance of pedagogical expertise.

Education is largely different from many other fields, which have been revolutionized by data. As it is largely related to human interaction, decision making will remain in the hands of skilled educators. Learning analytics do not offer fixes. Instead, using it provides an effective way of improving educational programs and making effective interventions. 

That is to say, the impact of analytics links with the quality of analytics that you have access to. What if you are about to invest in a new learning environment that promises analytics? Or you are already receiving analytical views into your training process? Let’s ask some critical questions on what matters most in learning analytics.

Can I increase the engagement in my course?

The most crucial problem in online learning relates to the frustration when facing ambiguous challenges and getting stuck. Too often this is caused by a confusing or unsuitable learning passage that can hamper even the best course.

To take corrective action, learning analytics should highlight the sections of a course, which are challenging. On the other hand, analytics should also show which parts work and create engagement.

Additionally, you should be able to identify the challenges faced by all learners or just particular groups of learners. Claned uses automatic keywording of materials using natural language processing. In addition to single materials, you have the option of viewing this at a topic level. For example, you can see which of the central themes in a course are problematic.

After setting the initial goal, like engagement, learning analytics can highlight the sections not working and what works already. This offers the basis for identifying the target of improvements and measuring the effects afterward. However, no technology does or will do the actual work of designing appropriate learning activities. But with the support of analytics, the content iteration will be more successful.

Can I personalize my course for different kinds of learners?

Learners are different. They have varying amounts of experience, different strengths, and diverse interests. Some might find your course too easy while others are struggling. Just how different are they and where they differ is up for the learning analytics to reveal.

Learning analytics should be able to identify groups or individuals with specific needs or challenges. Learning analytics can identify distinctions in behavioral patterns. Therefore you can focus on how these relate to performance, quality of social interaction. You can focus even on background variables, such as a professional role in a company.

Once you have been able to identify the differences between learners, you can start to provide tailored paths based on their interests and background. For example, by offering background information for those with less experience in a topic, or alternatively more advanced information for those that need more challenge.

You should be able to intertwine groups from two different backgrounds by first providing a path for each to prepare them for the subject, and then offer a shared part where they can share their knowledge. Imagine, all this with the help of effective learning analytics using your existing materials.

Can I see whether my training programs increase work effectiveness?

Whether you work in a company offering training programs or you are responsible for courses inside an organization, the holy grail is showing the real impact of training. That is, how does the actual performance change as a result of training.

Data is transforming industries like Finances, an area that naturally lends itself well to smart technologies. It has clear metrics and outcomes. As a result, algorithms that optimize the desired outcomes can do their magic. Learning, on the other hand, is another story.

In a learning process, the affecting factors, nature of the information, or data, and even the results can not be easily defined. However, this is slowly changing as more developed technologies. For instance, natural language processing can make sense of sets of unstructured data.

However, combining learning data with performance indicators it is possible to reveal what kind of impact the training programs are producing. These outcome metrics can include customer service, sales, or other measurable results.

Education providers can now prove what they can deliver through their training programs. This possibility will soon distinguish the best in the business.

Arm yourself with goal-oriented analytics

When we started our journey in developing Claned, we wanted to invent the new standard for learning analytics and how it provides the ability to support successful training programs. In order to do this, we had to create the best learning analytics in the business. And we did, and it is now available to enhance training programs anywhere.

We believe the investment in learning platforms should contain a clear view of ROI. Most importantly, through the number of drop-out rates, improved learning results, employee satisfaction, and improved sales.

We believe the use of learning analytics should relate to your training goals from the start.

That is why, in Claned, we focus on two main ways of using learning analytics to help education providers.

Our learning platform holds inbuilt analytics for real-time follow-up on an education program level. You will have these for every course implemented in the platform.

Secondly, we offer tailored data consultation services to answer specific challenges. Evidence-based decisions and personalization according to gathered analytics can significantly help you improve the experience for your learners.

If you want to hear more about how we can help you to achieve new heights of success for your training programs, get in touch and we tell you more.

topi litmanen

About the Author

Dr. Topi Litmanen worked as a Chief Educational Scientist in Claned. He was responsible for ensuring, that the pedagogical aspects of Claned are based on evidence-based learning research.


Preparing your organisation for AI supported learning

Supporting learning with data is a process of using analytics to empower learning and making decisions based on data-backed evidence.

Smart new technologies, such as machine learning and data mining have made significant progress in recent years, and their impact is growing in many areas of life. The finance sector is a prime example of how data and machine learning are used to optimize business processes. The field of finance naturally lends itself to data. It runs on clear metrics and has precise targets for optimization.

Organizational learning, on the other hand, is an example of a field with varied goals and fuzzy data. That is the main reason, the area of learning has largely been lacking analytics and data-powered decision making. However, technological developments are reaching a stage in which these obstacles can be overcome.

HR has a natural position to becoming a true leader in digital learning. This transition has started, and it will gain strength with time. The phases and tools of the process are described in Graph 1 and explained in the following text.

Graph 1. The phases of leading learning with data  


Scattered data
The data is already here, but where exactly?  

Most organizations already collect and hold massive amounts of learning-related data. Digital platforms, online courses, tests, competence appraisals, self-reports, education feedbacks, and other similar systems produce a wealth of information. One of the key challenges is that data is located in multiple unlinked systems. To use this data, one needs to derive it from various sources and combine it manually. As a result, HR is left with periodically producing one-off reports with cross-sectional analyses about the current state of affairs.

Manual reporting, even if partially automated to reduce the amount of work, is not real-time. It focuses on a pre-selected set of explicit variables and is rudimentary and lacks the possibilities for insights compared to more present-day solutions.

Combined data and dashboards

Once an organization combines its databases or builds interfaces for that purpose, more efficient data-enabled learning can begin to commence. Combining the learning management system, educational data, competency evaluations performance data brings insight into how all of these relate to one another.

Ideally, this information is presented in a simple dashboard providing real-time analytics about learning and development. It can bring insights into where learners are spending time, what they are engaged in, and what seems to be the evident obstacles for development. Correlating the educational variables with business results, such as sales data, provides ways to explore the progress and effects of specific programs or interventions.

For the most part, dashboards are still constrained with human deficiencies in decision making. They only show what they are planned to show. We are inclined to focus on the explicit relations between the most obvious variables. Many of the implicit reasons between causes and effects are hidden from a human investigator.

Predictive insights

Even with the ill-defined datasets related to learning and development, advancement in natural language processing and image recognition allow algorithms to make sense of contents and contexts in materials. That is, they can mine the data points for meaningful correlations that often escape the naked eye, such as finding relations between implicit, hidden variables, and draw on historical data and decisions within the organizations. They lack some of the pitfalls compared to human decision making and can outperform even the most experienced human practitioners. An effective way to gain insights into learning data is by merging the desired outcomes, such as sales results or customer feedback with the usage patterns in a learning platform. This highlights the effectiveness of different ways of engaging with the learning possibilities offered by an organization. The information can then be used to adjust the learning programs for future learners.

These systems can be assigned to identify learners who are not participating, or whose skills are in danger of lagging behind. They can also highlight some of the knowledge gaps or strengths within an organization. In parallel with helping HR professionals, the same algorithms can be harnessed to serve the learners. Indeed, the next level of learning systems is that which can make accurate recommendations for learners, educators, and HR.

Dynamic, actionable recommendations

Web stores and social media are effective in making interpretations about our interests and recommending us products or services that appeal to us. This same technology can be applied to support learning. This enables an organization to deliver truly tailored recommendations for ‘just-in-time‘ learning and personalized training programs for each employee instead of fixed courses designed for the masses.

When a system has an understanding of the needs, interests, and learning activities of employees, it has a robust set of data to conclude from. It can recommend materials, activities, and interactions to a specific learner based on identified needs. Further on, a learning platform can make accurate recommendations for future learners about which actions would be beneficial for them based on previous learners‘ activities.

The next developments in this process will be automated learning paths using materials inside an organization and automatically providing appropriate tests to measure learning and motivation.


Digitalization and data are not solutions to every problem. Leading with data is about developing new ways of operating. It is slow; it requires work and most of all, it requires a comprehensive understanding of current operations. The first step is recognizing the current state of learning data in an organization and designing the steps to take the process forward.

Data-driven systems do not replace effective competence management, but HR professionals that refuse to leverage available data for this purpose will be replaced by those who do.

This article was also posted on the EAPM Newsletter

topi litmanen

About the Author

Dr. Topi Litmanen worked as a Chief Educational Scientist in Claned. He was responsible for ensuring, that the pedagogical aspects of Claned are based on evidence-based learning research.