Kolb’s learning styles help in understanding what works well when we are designing courses. An intricate model, Kolb’s learning cycle is often used to explain the learning process. I will share my thoughts on how Kolb’s model can be used to create better eLearning courses.
How To Use Kolb’s Learning Styles To Create Engaging Custom eLearning: Overview Of The Model
Kolb’s model suggests that all learning happens due to real-world experiences which then help people form mental models of that experience. Some people actively experiment with the new learning and create further experiences on the subject.
Let’s look at the Kolb’s learning cycle:
From the diagram, we can state that there are 4 aspects to the model, which are explained below:
- Concrete Experience
It is the first part of the model. The learner has a new experience, say for example using a machine or a tool or going to a new place for the first time. When the person uses the tools or visits a new place he has a concrete experience that sets the foundation for the subsequent processes.
- Reflective Observation
After the experience, the person reflects on the experience and tries to understand the differences between his/her prior understanding of the subject. For example, if the person has heard great things about a destination, but has contrarian experiences, then he or she makes those observations and calls or labels those experiences as bitter.
- Abstract Conceptualization
After reflecting or observing on the matter deeply, the learner comes up with new ideas. So, in our example, the person may chose never to visit that destination, or chooses another destination, or starts his own travel agency!
- Active Experimentation
After forming new ideas, the person actively works on the idea. So in our example, the learner may travel to new places, or start his own travel agency, or even develop an app that helps other travelers make informed choices.
However, not all learners do all the 4 activities. There are some who perform active experimentation, there are those who only watch and reflect on the experiences, and then there are those who form new ideas.
This leads to 4 styles of learning, which is summarized below in the diagram:
1. Accommodating (Doing And Feeling – CE/AE)
The first style is about people accommodating what others have to say. So, they go with general perceptions or with the consensus. If something appeals to them, they will do it as they like to have concrete experience and active experimentation based on gut feelings. They are basically people who like to feel things.
2. Diverging (Feeling And Watching – CE/RO)
As these people like to have a feel of things and make mental notes through observations and reflection. They are most likely to have ideas. They like to brainstorm ideas as they like to look at things from various angles. They love to gather information and use their intuition and imagination to provide solutions to problems. They are sensitive and are open to feedback from others.
3. Converging (Doing And Thinking – AC/AE)
People with converging learning style are active experimenters and like to provide practical solutions to any given problem. As they are low on the feeling and watching part, they are less likely to spend time with others. They like to spend time alone and work on various parts of a problem. As they are strong in doing and thinking, they need constant stimulus to work on. Accountants and people working on hands-on stuff have generally converging learning style.
4. Assimilating (Watching And Thinking – AC/RO)
They are the analytical people. The preferred learning style is to watch and think through things. They are adept at creating mental models and are good at science jobs. They are looking to understand the logical aspects of a problem. Like converging learning style, they too like to work alone and not be with people. They like to organize things in a clear logical manner.
Applying The Learning Styles – A Case Study
In this custom eLearning course, we were targeting the product and sales team of a pharmaceutical company. The product and sales teams preferred learning style is converging, that is they are active experimenters and are doers. In other words, they are go-getters and are action-oriented people. Thus, to teach how much time it takes for a drug to be discovered and approved for market release, we weaved around a story with a lot of facts and data points to help them understand it better. We provided a decision tree activity which showed them how selecting a potentially incorrect molecular combination can delay the drug discovery process by years.
To conclude, Kolb’s theory is a great model for understanding learning styles and applying it to create the right custom eLearning courses. I hope you found it useful.
- Create Immersive eLearning Courses With Story-Based And Scenario-Based Learning Approaches
- Take Your Mobile Learning To The Next Level With Creative Design Strategies
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Artificial Intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. Taking these cues, AI can also be applied to learning. In this article, I will be sharing my views about the ways in which AI can be used in learning.
What The Role Of Artificial Intelligence In Learning Is
Whether it’s business, IT companies, financial services or even education, Artificial Intelligence (AI) is being integrated into various industries.
AI’s digital, dynamic nature also offers opportunities for student engagement that cannot be found in often outdated documents or in the fixed environment. In a synergistic fashion, AI has the potential to propel and accelerate the discovery of new learning frontiers and the creation of innovative technologies.
Artificial Intelligence And Learning
Though yet to become a standard cult in organizations and schools, Artificial Intelligence in learning or training has been a “big thing” since AI’s uptick in the 1940s (when the first seeds of AI were sown with programmable computers). In many ways, the 2 seem made for each other. AI’s digital, dynamic nature also offers opportunities for learner engagement that cannot be found in often outdated documents or in the fixed environment.
A recent study from eSchool News discovered that the use of AI in the education industry will grow by 47.5% through 2021. The technology’s impact will exist anywhere from kindergarten through higher education to corporate training, offering the opportunity to create adaptive learning features with personalized tools to improve learners’ experience.
Examples Of Artificial Intelligence In Learning
Here are 4 ways AI is changing the learning industry:
1. Smart Learning Content
The concept of smart content is a trendy theme now as AI can create digital content with the same degree of grammatical prowess as their human doppelganger. Smart learning content creation, from digitized guides of textbooks to customizable learning digital interfaces, are being introduced at all levels, from elementary to post-secondary to corporate environments.
One of the ways to use this in an organization is when AI can condense the content in burdening troubleshooting guides into more digestible study guides with troubleshooting steps summary, flashcards, and intelligent simulations.
Smart learning content can also be used to design a digital curriculum and content across a variety of devices, including video, audio, and an online assistant.
2. Intelligent Tutoring Systems
AI can do more than condense a lecture into flashcards and smart study guides as it can also tutor a learner based on the difficulties they’re having. This involves something known as “Mastery Learning”. Mastery learning is a set of principles largely tied to the work of Educational Psychologist Benjamin Bloom in the 1970s. This supports the effectiveness of individualized tutoring and instruction in the classroom.
There are now smart tutoring systems that use data from specific learners to give them the feedback and work with them directly. For instance, an Intelligent Tutoring system called “SHERLOCK” is being used to teach Airforce technicians to diagnose electrical system problems in aircraft. Another advanced version of Intelligent Tutoring Systems is avatar-based training modules which were developed by the University of Southern California to train military personnel being sent on international posts.
While this AI application is still in its early stages, it will soon be able to work as a full-fledged digital platform that helps learners with their educational needs in just about any area of need. Also, these platforms will soon be able to adapt to a wide variety of learning styles to help every educator and learner.
3. Virtual Facilitators And Learning Environments
With AI, an actual lecturer may soon be replaced by a robot. Well, not entirely! But there are already virtual human mentors and facilitators that can think and act like humans. But, how does a virtual facilitator think or act like a human?
A new trending technology is known as the “touchless technology” or “gesture recognition technology” gives virtual facilitators the ability to respond or act like humans in a natural way, responding both verbal and nonverbal cues.
Smart learning environments and platforms use AI, 3-D gaming, and computer animation to create realistic virtual characters and social interactions. This initiative includes more than virtual facilitators as Augmented Reality may soon be a part of the training.
4. Content Analytics
Content analytics refers to AI (specifically machine learning) platforms that optimize learning modules. Through AI, content taught to learners can be analyzed for maximum effect and optimized to take care of learners needs. Content analytics enables educators and content providers to not just create and manage their eLearning content, but also gain important insights into learner progress and understanding through a powerful set of analytics.
Paving New Learning Pathways In The Coming Decade
Learning is a domain largely ruled by human-to-human interaction. The assimilation of AI has been slower to develop the necessary human-like attributes of receptivity, versatility, and understanding. Yet, there are plenty of areas where AI’s inherent strengths help fill high-need “gaps” in learning and teaching.
AI’s ability to analyze large amounts of data in real-time and automatically provide new content or specified learning parameters helps meet learners’ need for continual, targeted practice and feedback. This allows teachers or trainers to better understand the learner’s performance and orchestrate more effective personalized learning plans.
To conclude, it is the apparent fear that human educators can or will be replaced by AI technologies in the coming decade. As AI advances in education and training, it seems there is more evidence to support the idea that both intelligent systems and humans are needed to manage different aspects of learners’ academic and social competencies. I feel AI will likely not replace but will serve as a support system to the human expert!
Design thinking is a recent phenomenon in the world of eLearning and it is a method that combines empathy, ideation, and problem-solving of complex and undefined problems. In this article, I will discuss how design thinking can be applied to create better eLearning courses.
How To Use Design Thinking To Create Better Custom eLearning Solutions
In my previous blogs on Whole Brain Learning and Kolb’s learning styles, I talked about how we can improve the learning process by using certain strategies that are targeted to individual learning styles. From Kolb’s theories, we know two important styles that are converging and diverging. These two styles are used in Design Thinking which will be explained little later.
First, let’s understand what exactly Design thinking is.
Design thinking is a method for the practical, creative resolution of problems using the strategies designers use during the process of designing.
-Visser, W. 2006, The cognitive artifacts of designing, Lawrence Erlbaum Associates.
Design thinking has been developed from ideas and tools that are used in other domains such as computer science, psychology, and so on. Also, design thinking in learning context has been influenced by the influential work “Learning Organization” by Peter Senge.
Analytical Thinking vs. Design Thinking
Design thinking has evolved because of one problem with the traditional method of problem-solving. In the traditional analytical model, the focus is on the problem rather than the solution. There was an experiment that was conducted, where scientists and architects were given a problem with color blocks. Scientists tried to resolve the problem by coming up with various combinations. However, the architect group resolved the problem by creating a solution using the available resources. So, unlike scientists, the architects focused on the solution and did not focus on overanalyzing the given problem. Instead, they tried to synthesize the existing information and came up with a practical solution to the problem.
Same can be applied to the learning context. If we talk in terms of Bloom’s levels, analytical thinking is the fourth level of thinking which is all about using available information for a well-defined problem and solving it. An example of analytical thinking is say understanding urban groundwater problem. The geoscientists look at available information and suggest remedies to address the problem.
However, many real-life problems such as say addressing the issue of children constantly watching TV or immersing in video games requires a different kind of solution. Here the problem is more deep-rooted and is simply put a human level problem.
Here is where design thinking comes to the fore. Design thinking is about solving a problem that is not well defined and may have multiple solutions to begin with. The situation is ambiguous and fluctuating, and hence there needs to be a lot of synthesis work to understand and define the problem using a variety of clues, discussions, and brainstorming to come up with an appropriate solution that may work well.
Design Thinking Model For eLearning
Design thinking model has five parts or steps which are—Empathize, Define, Ideate, Prototype and Test.
When it comes to building custom eLearning courses or gamification solutions, design thinking can be quite useful, as it is iterative in structure.
Empathize is the step where Instructional Designers understand the pain points and gaps that need to be addressed. The earlier thinking was to analyze the content or audience demographics and so on, as the first step of requirement gathering and analysis. However, using the design thinking model, Instructional Designers can focus more on understanding the psychological and emotional needs of people. In other words, Instructional Designers need to understand how learners do things, why and how they think about their current situation, and what it is that they want to do to make their job meaningful.
Once Instructional Designers understand the psychological underpinnings, then they can proceed to the definition phase.
Here, Instructional Designers define what exactly the problem is that organizations and learners are facing, and how they can work on that problem.
In the ideation phase, Instructional Designers can talk to a variety of people including the SMEs, visual designers, and other stakeholders in order to come up with a variety of ideas that may lead to a solution that will work.
Sometimes, the client does not know what exactly will work. They may come up with a request to develop a level-2 course. But after understanding the psychological needs of the learners and discussing all ideas threadbare, Instructional Designers can build on those ideas and come up with an entirely different solution, such as a game.
After developing the game and seeing how it has a great impact on learners, everybody will realize that even though the time may have been spent on ideating, it was worth it. Some people may wonder why they had not come up with the concept of a game at the beginning themselves, as that would have saved a lot of time. But that’s how design thinking works.
After the ideation phase, the next 2 steps are prototyping and testing the idea. More often than not, the prototype will act as a catalyst for the subsequent phase of the project, as many issues are resolved at this phase.
Design thinking is not much different from the traditional ADDIE model; however, its strength is that there is more emphasis on the empathy and ideation part, which somehow are not much stressed in the traditional models.
A Case Study
The customer wanted to develop a level-2 course. However, after understanding and empathizing with the learners’ needs, we suggested developing a game instead. The process took some time, since there were quite a few iterations. However, the effort paid off in the end because the game had a better impact on the overall learning.
To conclude, in a brief write-up, I showed the core elements of design thinking that can be used to develop custom eLearning courses.
Suggested further reading:
- Whole Brain Learning To Enhance Your Learning Experiences
- Using Kolb’s Learning Styles To Create Engaging Custom eLearning Courses
The post Using Design Thinking To Create Better Custom eLearning Solutions appeared first on eLearning.
Over the past two years, corporations have been developing and implementing Virtual Reality training solutions. In this article, I will be talking about how we implemented Virtual Reality in corporate training.
A Case Study On Safety Training: How To Use Virtual Reality In eLearning
I was long thinking of implementing Virtual Reality in one of the eLearning courses for our customers. When we got an opportunity to implement it, we didn’t miss the chance. This article is all about how we created a Virtual Reality (VR) experience for a customer who wanted to train their employees in a fire drill.
Before we deep-dive into our journey, I would like to inform you about a few aspects of Virtual Reality in eLearningand its advantages in corporate learning. So, let’s start with the basic.
Overview Of Virtual Reality
Virtual Reality is a practice or a technique of simulating parts of our real world using computers and specially designed equipment. Technically, “Virtual Reality is a three-dimensional, computer-generated environment which can be explored and interacted with by a person”. (Source: creostorm) That person is then immersed in a virtual environment. The person can also perform actions while in the part of that reality.
Now the question arises, how does Virtual Reality (VR) technology influence corporate training?
Corporate Training With Virtual Reality (VR)
It’s no secret that an increasing number of businesses are turning to Virtual Reality in eLearning for employee training. Virtual Reality (VR) is normally implemented in situations which can be risky or hazardous. Instead of conducting drills, companies can help employees expose to concepts such as emergency response to a hazardous situation – like fire or flood or a tornado alarm –through a VR based module. Further, insurance companies can benefit from Virtual Reality in eLearning by using it to accurately assess property damages caused due to tornadoes, hailstorms or floods.
Let’s have a look at a case study for this.
Case Study: Fire Safety Training
One of our customers approached us to develop a course on fire safety for their company. The intention of the customer was to educate their employees on the safe use of fire exit facilities in one of their buildings in case of fire disaster, and also use the right equipment to fight against fire if needed. This eLearning course with Virtual Reality (VR) was also to be used to train employees at other facilities of customers, to create more awareness, and to equip them to react more effectively.
The customer requested for a better way to train their employees on how to evacuate the floor safely and provided them with the general guideline procedure that had to be followed. They also wanted the training to be rolled out for PC, tablets, and smartphones.
After hearing out the customer requirements, we began evaluating the solutions that could be used for training. We suggested a solution, that of using Virtual Reality in eLearning, as that would give the employees a realistic feel for the situation such as a fire emergency. We used the right technology to ensure training courses can be taken on PCs, tablets, smartphones and, most importantly, through the use of VR goggles wherever they are available at the customer’s office locations.
We used a 360° camera to take pictures of the office floor and the fire exit points. The storyboard and the content were curated accordingly… We, then, developed a simulation of the building floors and offices. For this simulation, we added a few hotspots to show how the employees can navigate in case of a fire emergency. This, we believe, would leave a major learning impact on the employees.
The eLearning course with Virtual Reality (VR) allows employees to choose their exit path from the building. Employees can decide to fight the fire, but they must first find the fire extinguishers, water sprinklers, and fire alarms in their workplace – which is, by the way, not an easy task when the room is filled with fire.
The employees need to know where the fire extinguishers are placed and the associated information. They also need to answer a few questions. Further, they have to find an exit that is nearest so that they can reach the assembly point safely.
After the course was over, we told the employees to sit back and allowed the course to seep in. This was known as the moment of contemplation, wherein the employee gets to recall and analyze the entire course. After that we asked for their feedback. Most of the employees said the experience with VR was quite exciting and useful, and helped them in experiencing a fire evacuation drill in real time.
Thus, the eLearning course with Virtual Reality helped learners understand the concept better.
Here is a screenshot of the course:
This was just an example of countless use cases for VR training, all of which support the transformation in how organizations train, and help their employees learn better. As is the case with any transformational tool, an investment is required, but that commitment can pay big dividends when VR is implemented correctly.
Suggested further reading:
- Game-Based eLearning: Making GBL Effective Through Avatars And Immersive Stories
- The Role Of Artificial Intelligence In Learning
- Create Immersive eLearning Courses With Story-Based And Scenario-Based Learning Approaches
The post Virtual Reality In eLearning: A Case Study On Safety Training appeared first on eLearning.
Collecting and combining data can clearly provide valuable information in designing and developing smart learning. The potential of learning analytics to enable smart learning remains a non-investigated concept. In this article, I discuss digital body language and learning analytics.
Digital Body Language And Learning Analytics
Learning and Development today should be all about the learner—what they want, what they need, and when they need it. Despite the breakthrough technologies, the current scenario of L&D is uncertain, as the need to have a clear link between training and performance increases. And eLearning is not far behind. Deriving actionable information about learners’ learning needs, pain points, and the learning intent are driving the L&D teams now. A newer concept has emerged that may have the answer to it, and that is digital body language.
So, What Is Digital Body Language?
Consider a traditional classroom. The student and the trainer are in the same environment at the same time. This makes the trainer more aware of how engaged the student is. Accordingly, if they notice the learners are disengaged, they can see their pain points and take corrective measures to increase the engagement. This is now possible in the digital environment, too.
Digital body language, or else known as DBL, a concept made famous by Steve Woods in 2009, was used to explain the science of tapping into the signals and activities customers are exhibiting that show what they want, and when they are likely to raise their hands. DBL gives electronic signposts to a person’s interests.
Decoding Digital Body Language
Digital body language represents everything that learners are online
– Lori Niles-Hofmann.
DBL is the sum of all activities taken, the number of courses taken, pages visited etc. Learners might be able to hide behind their computer monitors and mobile phones, but their actions reveal key information about their interests.
Think of it like a learner’s facial expressions. Instructional Designers need this data to accurately assess where the learner is on their learning journey so that they can adapt their strategies and approach accordingly.
Making The Data Accessible
The main use of DBL is that it can be objectively measured using data. By looking at just a few figures, L&D teams can immediately understand where the learner stands, what they are interested in, and what kind of approach or strategy can be followed. Information can be collected via feedback forms, polls, or even the number of visits to the course. These can be used to score the learning strategy.
Source Of Information
The information that can be unraveled from DBL is:
- What device or browser does your audience prefer?
- At what time or day of the week do they access content?
- What content length is optimal?
- Do they prefer videos, articles, or microlearning?
- What are the potential obstacles learners are facing?
Compiling this data—and reporting it on a regular basis—is a good way to begin to bridge the content divide. But, how can we report, record, or track data at a micro-level? The answer could lie in xAPI and LRS or even custom coding.
xAPI, Learning Record System (LRS), And Custom Coding
xAPI or Experience API, the successor to SCORM, poses unique features such as offline tracking, tracking learner experience, reducing the need for an LMS etc. This gives rise to the question of how are we going to track our course without LMS? This can be done through LRS. We can track our eLearning courses wrapped in xAPI using LRS. Now, what is LRS? LRS is the heart of any xAPI ecosystem. LRS stores learning records and enables the export of raw learning data.
Now, coming to the main query, what do LRS and xAPI have to do with DBL? Adopting these technologies will help you build the right platform to decode DBL. However, only xAPI and LRS are not enough to get the complete picture. An adaptive learning platform is required, and here is where custom coding acts as a catalyst. xAPI recognizes and records learning experiences, LRS acts as a database, and custom coding gives the ability to draw the appropriate data.
On the other hand, when it comes to learning design, we need to factor into a few things. Let’s look at this next:
Data-Driven Learning Design
Data-Driven Learning Design or DDLD gives us the evidence that we can no longer push the content we believe learners should or must digest.
Here are a few ways that DDLD can help in Instructional Design:
1. Designing The Learning Objectives
DDLD gives us insights into:
- What type of media should we use?
- Is the mobile access high or low?
- When do learners log in?
Answering these questions helps us create better courses. For example, if learners are comfortable with more visuals than text on the screen, then you can consider giving concrete examples such as combining words and visuals. Depending on the learning strategy, learning objectives can be defined.
2. Content Design Decision
The intent of DDLD is to help Instructional Designers make decisions about whether to go for self-paced eLearning or a blended solution. For example, DDLD can help Instructional Designers decide when to put a quiz, what type of introduction the course will need, and when we can place a drag-and-drop or any sort of exercise.
3. Consulting And Evaluation
DDLD gives insights into how the training can have an impact on learner performance. Consulting and evaluation along with DBL help to build courses that align course objectives with business goals.
Digital body language helps you drill down into different levels of data and information. It’s fun to observe and learn at the same time. Utilizing the insights drawn from DBL means taking on an iterative design mindset. Instructional Designers can constantly refine and improve what they do based on what they find out to increase engagement.
To conclude, data will leave you in amazement. It uncovers a veil of learners’ preferences and helps to create courses which are more learner-centric.
So, what’s your plan for understanding the digital body language of your learners?
The post Why Use Digital Body Language And Learning Analytics? appeared first on eLearning.
Successful corporate eLearning is all about engagement. Games, when used in eLearning, make online training fun, entertaining and engaging for learners. In this article, I will discuss a case study on a gamification solution that we delivered, which resulted in better learner engagement.
A Case Study On Gamification: Taking Corporate eLearning To The Next Level!
A well-crafted game can help learners remember the content thanks to its engagement quotient, and transform online training courses to enjoyable online training experiences. Regardless of your performance goals, employee needs, or online training objectives, games are a great addition to any corporate eLearning strategy.
In my previous article on 5 key benefits of enterprise gamification, I have discussed various aspects and benefits of gamification. In this article, I will be discussing a case study on gamification in corporate eLearning.
Risk Management Course Overview
Our customer, an IT major, wanted us to develop a course on risk management with a different strategy than a regular corporate eLearning solution.
Instructional Design Strategy
The main requirement of the customer was to create an engaging and interesting course. Though we initially bounced a few ideas on the best possible strategy, we agreed on implementing a gamification strategy for the course. We developed a few mockups with a scenario and gamification elements to demonstrate the flow of the strategy.
For this game, we chose the single player option, wherein the learner fills in their name and becomes the player. As the learner becomes one with the character in the game, the experience becomes more personal. Which is a truly great way to learn and retain information. The feedback for activities is personalized for each learner.
Different interactivities were built to make the gaming experience more immersive. Thus, learners tend to learn better in an experiential manner.
The course begins with an intense situation, wherein a company’s Vice President is in a pensive mood, as the company has faced a risk-related incident twice in a very short span. While browsing through a news website, he finds out about the launch of a new risk management application by another company. He finds that quite promising and contacts the company’s risk management team to help his organization. Upon receiving the information from the prospect, the team immediately meets and makes plans to implement the tool to see if it can help in auditing and containing risks.
After the scenario, the learner is informed about the mission and the various levels that they need to clear to accomplish it, through a case study. Each level takes the story forward along with a perfect blend of visuals, interactions, and reading material. These are then followed by a series of activities for the learner to apply what they have learned. Each activity is timed, and the learner will earn points for answering the questions correctly. The learner receives a badge for completing each level.
Unlike other game-based eLearning, this course has the “just right” amount of interactions that give the learner an engaging learning experience. This way, we took corporate eLearning to a higher level.
Here are a few screenshots of the game:
Once the course structure was finalized, we went about creating a storyboard and proof of concept for the customer. This included the learning approach (game-based eLearning), mockups of the User Interface, design, and concept guidelines. A small working prototype of the game was created after the mockups were approved. This was submitted to the customer for review. We basically provided the functional insight into the course that was being developed. After a number of iterations and discussions with SMEs, a final storyboard and design were decided upon.
Following the storyboarding, the game was designed and developed using the agreed authoring tools as per the standard corporate eLearning practice. We dedicated resources and experts who have a great deal of experience in developing game-based eLearning solutions to develop the content.
Once the game was developed, we took a step forward in “pilot-testing” the course to a few users. The main purpose of the pilot test was to analyze how engaging the course would be on a large scale. Most of the participants in the test found the course to be compelling and engaging. After robust testing, the course was rolled out organization-wide.
The gamification approach was used for the first time in the organization, and it increased the completion rate from an average of 32% to 67%. The customer also found that the training helped learners with better retention of the concepts and better application on the job. This was a great benefit as compared to the benefit that a standard corporate eLearning approach would have accrued.
The game-based eLearning approach was an interesting way of reaching out to such a dynamic group of learners. Not only did it achieve better adoption, it also provided a good learning experience that suited the needs of learners, as well as aligned well with the organization goal of creating impactful training.
Suggested further reading:
- 5 Key Benefits Of Enterprise Gamification
- How To Use Game-Based eLearning On Mobile Devices Effectively?
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