Applications in the market have evolved, and today, educational content can be accessed through multimedia systems. Multimedia content generates interest in students, motivation, and cooperative learning.

Multimedia refers to the integration of two or more elements, such as audio, text, and images, on the same platform. This integration requires a correct combination of software and hardware to generate a multisensory information environment.

Software is essential because it is where the application and interactivity are developed, while hardware allows access to that content in a comfortable, fast, and user-friendly way.

The user should have a multimedia learning experience.

“Currently, multimedia content is commonplace, interaction with it is greater, so learning happens naturally, becoming something easily memorable that is internalized more quickly because we are more involved,” says occupational psychologist Teresita Ramacciotti.

For the multimedia experience to become a motivating agent for learning, it is first necessary for the user to be able to access the content quickly, cleanly, and without interruptions to maintain interest and start with the right disposition. In this sense, the smartphone is hardware already familiar to users, making learning through this device just one of the many activities performed with the phone.

However, the key is to deliver an outstanding experience to users, and this is achieved by combining attractive elements such as photos, audio, text, and especially videos. The latter are easy to produce and allow any message to be conveyed simply and quickly.

In addition, videos can provide more information than other channels, and by incorporating image and sound, they create a much more attractive message and increase retention because the user has to get more involved.

Games are multimedia

Playing involves continuous positive reinforcement. “Playing gives the opportunity to learn because it creates a greater connection, especially if it is with sensory multimedia content, as one can learn visually and auditorily. This makes learning easier for people from their own style, as it adapts and increases the number of stimuli for each person,” explains Ramacciotti.

Interactive multimedia materials allow the transition from hard information to something meaningful. Information, analysis, practice, and instant feedback will allow the user to correct any errors instantly. Additionally, training through a mobile device makes the results tracking process three times easier, making it more motivating to use it as a learning medium.

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However, one of the most important elements is personalization and segmentation. Not everyone is the same. Employees should be segmented into different groups and assigned specific objectives so that they have a sense of achievement of KPIs or eLearning.

Gamification does not motivate everyone in the same way.

Gamification does not generate the same motivation in everyone equally. The beauty of Closelly is that it is flexible enough to use the appropriate game dynamics for each individual or group of individuals.

Furthermore, there is a diversity of generations within a company. So, how do we personalize Gamification?

To achieve this, and be sure that the proposed solution is the best, we have to make the effort to understand the users. Not only identify them and know what role they play in the organization, but we have to know them more deeply. We need to know how they react, to what and when. We need to know what affects them and what does not motivate them at all. This way, we can design training and dynamics that allow them to motivate themselves and achieve the best results.

Types of players and games

Some people look at player types as proposed by Bartle. This view can be partially useful. Octalysis’ perspective provides other useful elements for the implementation of gamification in companies and is more focused on the goal of the game.

And what about the user profile? We are not even talking about gamification in companies like what Closelly does but about video games. Companies that create video games face exactly the same challenges as us. They want their users to be as motivated and engaged as possible, and to achieve this, they need to understand them.

A company that is a great source of game creators is Quantic Foundry. On their blog, there is a vast amount of information about what they have learned about games and their players. We love the analysis of player motivations and personality traits that we find here.

However, when designing a gamification implementation for a client, even when you can customize activities, how can we consider the differences of the participants when we choose the same narrative for everyone?

Looking for a common denominator? Strategy, not competition

Here are interesting points to highlight from Quantic Foundry’s blog.

• Strategy is the most stable motivator as players mature. Contrary to what many people might think, competition is a motivator that is not always effective for all ages. Competition is more effective as a motivator at an early age rather than by gender. On the other hand, strategy and complex thinking keep players motivated at all ages.

• Gender matters, but not as much as age. As we might expect, female players are more likely to be motivated by elements of personal expression and the customization of elements, fantasy elements, and elements that revolve around stories. Male players tend to be more motivated by competition, challenges, adrenaline, and strategy. All of this aligns with the stereotypes we have seen. The interesting thing is that as players age, these elements become less important.

These are just 2 examples among many. The point is the importance of understanding users (players and employees).

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It’s easier for the brain.

Short-term memory can only hold up to four pieces of new information, then research suggests that the information is overwritten or diverted before it can be transferred to long-term memory. Therefore, by breaking down topics into small parts, new knowledge is retained more easily.

It’s easier to create and update.

Unlike boring courses that can take weeks or months, when the course consists of a few key messages, it’s possible to design a course in the morning and distribute it in the afternoon. And if a relevant case study appears in the news the night before, it can be easily included, ensuring you always have fresh and updated information.

Microlearning is more effective.

Since employees can train through their smartphones at their own pace, technology allows for more interactive and even gamified lessons. Learners strive for a higher score by answering correctly and quickly, and if real rewards are added for the best performance, they may forget they are being trained. Microlearning courses have completion rates of over 90%.

It’s easy to distribute.

Microlearning-based training courses can be downloaded anywhere via the cloud, and since it can be accessed from their own phones, if a worker doesn’t have internet access at home or outside their workplace, they can easily download specific micro-lessons minutes before they need them.

Anyone can create a lesson.

Through the use of simple interactive templates, anyone can upload information and ask questions about it, serving as a peer learning facilitator. Experts suggest that one-third of online learning should come from peers, and it’s not hard to understand why: no external trainer will know more about the specific functions of your organization than the people who are already part of it.

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Considering that by the year 2020, millennials will make up 50% of the workforce, companies, especially those in charge of Human Resources, should take into account that the key lies in microlearning.

Microlearning, a method that uses small learning moments to drive better performance and development at work, works for all employees. However, as the first training method created by and for 21st-century learners, it is especially useful for millennials because:

• Millennials learn better in short chunks that they can quickly absorb.
• Millennials respond better to animations, videos, and other moving images – teaching tactics that are especially important when emphasizing key points.
• Because millennials grew up using mobile devices, they expect to carry everything on the go, including their lessons.

The similarity of microlearning to everyday learning and content consumption habits makes learners more predisposed to training. It has also been proven that small doses of content are much more efficient and achieve 20% more retention than long-format learning materials. This means that millennials gain a significant advantage in the workplace, not just in terms of cash, benefits, or security, but in quality development opportunities on the job.

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But why do people respond so positively when employee training programs incorporate game elements? There are three key aspects:

Engagement Mechanisms

If we think about all the elements that made Super Mario, Pac-Man, and Tetris so addictive, it’s because the game revolved around achieving goals: earning points by acquiring certain items or solving puzzles. Moreover, games reward behavior in a satisfying way, such as leveling up or earning badges after reaching certain objectives.

Game mechanics can also help your workforce stay absorbed in the learning experience. If employees enjoy the training, they are more likely to complete the course and retain knowledge, especially when working with millennials who statistically are more likely to enjoy video games in their free time.

Rankings to Enhance Interactive Learning Dynamics

Many games have public leaderboards where players can track and see who is performing better and who is falling behind.

Although the goal is not to encourage a competitive culture, managers and training and human resources personnel can use this information internally to better understand which employees grasp concepts and which ones require additional training, or to gain a better understanding of the team’s progress and competencies.

Microlearning as an Aspect of Interactive Learning

One cannot truly appreciate gamification without exploring the psychology behind microlearning. This methodology has changed the way we create e-learning training courses, benefiting everyone!

Microlearning breaks down a large concept into smaller pieces and then teaches each piece. Studies show that microlearning methods increase a learner’s knowledge retention by at least 50 percent. So, by using gamification, you are adopting the mindset of microlearning and implementing interactive learning!

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En the Google Developers Summit 2019, Jingtao Wang, Research Scientist at Google, presents the future and challenges that mobile learning faces, showcasing case studies on smartwatches and how to monitor them using artificial intelligence. Additionally, he discusses physiological studies on students’ learning.

Jingtao Wang is a research scientist at Google, specializing in intelligent mobile interfaces, machine learning, and educational technology. Wang has received various accolades, including the Microsoft Azure for Research Award, Google Faculty Research Award, and ACIE Innovation in Education.

Hello everyone!

My name is Jingtao Wang, and I am a researcher at Google. Before joining Google full-time, I was a researcher at the University of Pittsburgh.

I have two main lines of research. The first one is machine learning on electronic devices, and the second one is the application of machine learning in the context of education and human learning. Today, I would like to share some of my ongoing projects with you.

I am working on projects related to my two interests.

As we all know, human learning is one of the challenging and rewarding tasks. Imagine the opportunities that can open up if a person learns a new language, learns to play a new musical instrument, or understands the usability of emerging technologies like TensorFlow.

At the same time, learning remains equally challenging and important globally. According to surveys, more than 10 million people are registered in MOOC courses such as Coursera, but less than 7% of its users complete them. This means that learning something new is not trivial or easy.

Additionally, people need to learn not only in classrooms but also in other informal environments.

Today, I would like to show you some examples of how to use machine learning, using a digital tool on your wrist, in the palm of your hand, while walking… I’m talking about the smartwatch.

SmartRSVP

The first project I will show you is called SmartRSVP, and the idea is how we can turn your smartwatch into an intelligent learning device.

The first challenge was addressing the small size of its screen. We know that a typical smartwatch has a 1.5-inch screen and will only display two or three words at a time, making it difficult to read a sentence.

So, how can we enable effective learning?

It depends on the task. What if we take small samples and break the task into bite-sized pieces? Like the ones we enable through embedded learning on wristwatches that will notify timely reminders or provide prompts or checks on whether you understand the displayed content.

Another point we addressed is how to read long sentences on such a small watch.

The solution is a technology called RSVP, which shows one word at a time sequentially. Each word can be larger and displayed faster, and it can also be read without moving your gaze.

Here, we present two technologies to facilitate this type of rapid reading on smartwatches. The first is that we detect your attention; we only show the next word when you look at the screen, meaning we pause the screen when you look away.

The second is that we monitor the user’s heart rate in real-time to gauge their attention, and if it increases, we test at a faster pace.

Interaction in large classrooms

The other project I will share with you is about improving interaction in large classrooms.

We know that large classes are quite popular in undergraduate classes; it’s not uncommon to have a class with more than 200 students. How can we improve interaction in such an environment? The smart mobile app called Kazmir has three important features for this need:

The first feature is that it will automatically send a reminder after each lecture, asking the student about the content, applying some questions or if they have any doubts about something they would like to know more about or clarify with their instructor. This will naturally have the effect of generating dialogue that allows for deeper and more specific questions.

This system has been implemented in classrooms in the U.S. and abroad, with over 400 students using the pilot system that I have been working on for over five years.

In the case of MOOCs, Massive Open Online Courses, learning more efficiently on their mobile devices has two components:

Using a camera to track the students’ facial expressions and using the rear camera to track physiological signals, which, in this case, are difficult due to the variability of students taking them. We need to know well what students are learning when watching course videos and pay special attention to when they get distracted.

For this, we use some adaptive intervention techniques, such as ensuring the student is attentive and calm while watching the class.

Living Jiagu: Learning about language creation and evaluation with artificial intelligence

Now I will present the project that excites me the most, language learning with artificial intelligence. This project explores the intersection of cave art and artificial intelligence.

In this image, we see the language called “Oracle Bone Script” on a turtle shell, which is the origin of the Chinese language and was used by people 3,000 years ago.

This unique character language is a predecessor to most other Asian languages. When it died out, a new language emerged: modern Chinese, whose origin and evolution are in this shell.

Understanding the origin of a language can help us have a deeper understanding of languages, creating an interactive exhibition and a higher level for users who experience learning a new language with everyday objects.

Here are some technical details:

We use a multi-layer conversion of the Hercules neural network, similar between the created character and the message in the image displayed on the screen. At the same time, we also use an algorithm to determine, for example, if it was the correct meaning of each part of the character you created, and then it is animated, trying to build a connection between each object and the meaning that is the representation of the Asian language by knowing the connection.

This project was launched at Google in China in 2019 with more than 1000 participants who experienced this demonstration, including two parts, the first for large touch screens.

As a task, it is about imitating the Chinese ancestors who created hieroglyphics and then allowed the creation of characters that help you animate and compose scenes on a screen. We can build a connection between everyday objects and the corresponding meaning of those objects with our learning.

Machine learning can develop support and knowledge more efficiently and engagingly because learning does not necessarily happen in classrooms; it can also happen on your wrist or in the palm of your hand.

Thank you!

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If there is no available time, it’s unlikely that the user will start something they cannot finish in the allotted time – even with things they really want to do!

That’s why Micro Learning is designed so that we can access and consume through brief bursts of content. It has a better chance of capturing attention, considering that training rarely takes a high priority, and the user may not even consider it.

The user, having more elements to trigger actions, will retain the content better. Unconsciously, they become participants in the learning process.

Elements of microlearning

Microlearning involves many interactions. It combines videos, quizzes, games, among other elements. This higher degree of interactivity keeps the learner engaged with the content by over 50% compared to other forms. The more actively involved, the higher the chances of retaining what has been learned, reducing the forgetting curve.

According to the Journal of Applied Psychology, microlearning is 17% more efficient in knowledge transfer than long-duration content.

Incorporating a training program with a focus on microlearning allows users to acquire new skills in a short time. Additionally, it is flexible when it comes to content release. It can be rolled out in stages, and the possibility of updating it, if necessary, is three times faster than traditional approaches. This makes it an effective, agile, and adaptable tool to changing learning requirements, regardless of the audience.

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