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How AR And VR Are Already Affecting Students

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How AR And VR Are Already Affecting Students

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The top app development companies are already taking the time to learn more about augmented reality and virtual reality. AR and VR are going to change the world in a number of ways. These technologies are no longer as far fetched as they may have seemed in the past.

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Abstract

In our generation the information systems evolve with new technologies: augmented reality (AR), IoT, artificial intelligence, blockchain etc. Anymore they perform information exchange by sensors. It is estimated that the systems will be in a state of extreme interaction and reach 50 billion devices connected in Internet in 2020. We know that everything around us will be in interaction and they will do everything without any need of human interference. For example, when our dishwasher is full, it will start to wash automatically, or when the run out of the gasoline, our car will drive to the nearest station, or even when a burglar is entered to our house, it will automatically be detected and be announced to the police office. In business life, the processes will be automatical in maximum level and this technology will increase productivity and efficiency. Next to mobile technology, it is thought that these new generation information systems (IS) will take the biggest place in our lives. AR also will be integrated to these systems to augment the information in real world. Humanity will augment its habitat in an innovative way thanks to these AR based IS. This paper surveys the current state-of-the-art AR systems related with aerospace & defense, industry, education, medical and gaming sectors. The connection of AR based IS and innovation is explained with a technological insight. In addition to international use cases HAVELSAN's use cases are also given that are performed from the aspect of applied open innovation strategy. This strategy is addressed specific to the implemented activities of AR based IS.

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An Innovative Technology: Augmented Reality Based Information Systems

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In our generation the information systems evolve with new technologies: augmented reality (AR), IoT, artificial intelligence, blockchain etc. Anymore they perform information exchange by sensors. It is estimated that the systems will be in a state of extreme interaction and reach 50 billion devices connected in Internet in 2020.

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11.1 VIRTUAL REALITY CONCEPT

Virtual reality technology plays an important role in realizing Telesensation. Through it, a virtual world is created that viewers can enter and walk through and where they can handle virtual objects. The virtual world allows us a stereoscopic view from front or side, depending on our viewpoint, just as in the real world. The ability to enter and walk through the virtual world and handle virtual objects using hand gestures makes VR interactive, and this is one of its most important features.

Communication can be human-human communication, human-environment communication, or human-computer communication. In the case of human-human communication, a variety of means are at our disposal. We talk together to communicate. We write letters or draw pictures and sometimes communicate using images and motion pictures. In human-environment communication, we recognize our environment via our five senses: feeling, touch, taste, vision, and smell. In human-computer communication, we interact with a computer by means of a mouse, a touch pad, or a keyboard.

Human-human communication and human-environment communication have been developed over a long history of interaction. It is desirable to provide human beings with a human-friendly environment where we can interact with computers just as easily as we interact in human-human communication or human-environment communication.

The goal of VR is to provide human beings with a virtual environment where we can interact with a computer just as we do in the real world, that is, by talking with a virtual human in a spoken language, by writing a letter, or by drawing a picture. We can grasp a virtual object by hand gesture and bring it to another place. In a human-friendly virtual environment, we can interact with a computer without any difficulties or barriers. When a virtual landscape is generated by VR technology, we can go there just as if it were a real landscape. Providing not only a 3D image of the landscape but also sound and smell helps us enjoy the scenery.

In Oita prefecture, Japan, there is a museum where visitors can experience a virtual world. Upon entering the museum, we see a large screen in front of the seats. By sitting on a seat and wearing special glasses, visitors can enter a large virtual flower and smell it.

At ATR Communication Systems Laboratories, Kyoto, Japan, a virtual space teleconferencing system was developed in 1992. This next-generation video conference system provides participants a human-friendly environment for meeting and collaborating. They can view objects stereoscopically and have front or side views of the objects depending on their viewpoint. They can handle an object by means of hand gestures. In this system, each participant is at a different location, and all sites are connected via the network. Each site has a virtual conference room with a large screen in front of seats. On the screen, 3D images of real human beings are displayed stereoscopically, and participants can have stereoscopic views of various objects displayed on screen. They can have eye contact with each other. They can conduct a meeting as if they were gathered in the same place.

The image's motion is controlled by the real human's motion. The participant wears shutter glasses and has sensors on face, hands, and body to detect motion. The shutter glasses provide a stereoscopic view. On the basis of the movement information, the images of the virtual person or object is deformed and displayed on the screen to match the viewer's perspective.

In the conventional video conference system, participants can meet face to face. However, it is very difficult to make eye contact and to have different views of objects according to the viewer's perspective. In the real world, a viewer can take a side view of an object just by moving to the side of the object. In the virtual space teleconferencing system, participants can make eye contact and take different views of the object to match their perspective. In the conventional video conference system, a participant cannot go inside the scene displayed on the screen, whereas in the virtual system he or she can enter the virtual space, walk through it, and grasp a virtual object by means of hand gesture, even feeling the heft of the object. To summarize, a viewer in the virtual world, can have a stereoscopic view of an object. This is called stereoscopic display. The viewer can enter the virtual world and walk through it. This is called walk-through. The viewer can take different views of the object according to his or her viewpoint. This is called interaction. The viewer can touch and grasp a virtual object and feel its heft. This is called force feedback. In the virtual world, even a collision can be detected.

To accomplish these functions, the viewer wears shutter glasses or a head-mounted display; this gives a stereoscopic view of the object, because the right image of the object enters the right eye and the left image enters the left eye. When the technologies of the lenticular screen and holography have been developed enough, viewers should be able to have a stereoscopic view with the naked eye. This involves detecting the viewer's viewpoint via a sensor attached near the viewer's eye and, based on this information, adjusting, the object's display on the screen. Object handling with hand gesture is accomplished by means of a data glove with sensor, that detects hand shape and hand motion. Again, this information is used to adjust display of the object. The technology for detecting hand motion and hand shape without use of a data glove is currently under study.

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URL: https://www.sciencedirect.com/science/article/pii/B9780126853513500123

11.2.1 MPS systems

MPS systems often use VR technologies. Thus, MPS systems involve resource-consuming computations because VR environments should allow human users to interact in real time with a computer-simulated environment (Kim, 2005). Advanced interaction techniques are used because MPS systems do not only integrate visual information, but also information pertinent to the sense of touch of human tissues and organs (Sorid and Moore, 2000). Thus, many MPS systems integrate complex haptic hardware systems for user-software interaction (e.g., to simulate the use of an endoscopic tool inside a human body). This is in contrast to ordinary software systems that use mouse, keyboard, or touchpads as input devices, and monitors, screens, or visual displays as output devices.

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URL: https://www.sciencedirect.com/science/article/pii/B9780124170094000119

2.6.10 Tiled displays

One limitation of older virtual reality technology is that it suffers from poor resolution and is limited in brightness. Modern head-mounted displays are an improvement, as are modern projectors. There is a trend, though, to achieve higher brightness and more pixels by tiling multiple display systems together. This can be achieved by projecting multiple projectors onto a single display surface, or by abutting multiple displays together.

Of course, achieving a good alignment and registration, as well as color and brightness matching, is still a nontrivial engineering challenge.

Tiling together multiple flatscreen displays can be problematic if the bezels and frames are obtrusive for your application. Tiling projectors together to project on a single screen (typically, rear projected) brings with it the difficulty in aligning the displays. Image alignment can be carried out mechanically (to line up the edges physically) or in software by allowing the images to overlap and using software to do edge blending.

Of course, the goal of tiling displays together is to achieve high pixel density with appropriate screen real estate. As display technology improves, there will be less incentive to tile multiple displays together.

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URL: https://www.sciencedirect.com/science/article/pii/B9780123749437000021

Electromagnetic Tracking

Electromagnetic tracking is a VR technology that has waxed and waned in usage as other technologies come into vogue or improvements are made. It is still a viable solution for some low-cost, short-range needs (Fig. 4-14). This tracking technology typically uses a transmitter to generate a low-level magnetic field from three orthogonal coils within the unit. In turn, these fields induce current in another set of coils in the smaller receiver unit worn by the entity being tracked. The current in each coil inside the receiver is measured to determine its position relative to the transmitter. The transmitter base unit is fixed at a known location and orientation so that the absolute position of the receiving unit can be calculated. Multiple receiving units are generally placed on the user (typically on the head and one hand), on any props used, and sometimes on a handheld display device.

Full 6-DOF position is measured with electromagnetic tracking by separately inducing orthogonal magnetic dipoles through each of the three orthogonal coils in the transmitter (Fig. 4-15). The receivers then have different amounts of current generated in the three separate orthogonal coils during each sequential output from the transmitter, which produces nine measurements from which the six values can be calculated providing a relative measurement from the transmitter base unit. Six values are produced for each receiving unit: x, y, z location, and the orientation ( roll, pitch, yaw).

One of the limitations of electromagnetic tracking systems is that metals in the environment that are ferromagnetic, such as iron and nickel, can induce eddy currents causing feedback interference. [Note that DC pulsed systems such as the Ascension Flock of Birds reduce some of the eddy problem by waiting as long as possible for the eddies to subside before taking measurements. The complication is that the longer the wait to improve precision, the longer the delay in measurement which increases latency (increased latency is undesirable).]

Another limitation is the short range of the generated magnetic field. The receivers will operate with reasonable accuracy only within 3-8 ft of the transmitter, depending on the specific model. The accuracy drops off substantially as the user moves toward the edge of the operating range. Using multiple transmitters to extend the range is possible, but difficult to implement. One other limitation is that the sequential nature of calculating the position data necessitates that there will be a nontrivial amount of time needed to produce the data, resulting in added tracking latency.

The major advantage is that electromagnetic systems have no line of sight restriction. The absence of this restriction allows the users to move about in a space that might have multiple non-metallic visual or sonic obstacles between them and the transmitter-obstacles that can interfere with other types of tracking devices. Wireless systems are also available, reducing encumbrances on the participant.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128009659000040

The third alternative is to use virtual reality technology. One version of virtual reality is called Augmented Reality. With this technique, virtual information is superimposed on the user's view of the real world by means of a head-mounted display. In the future, these displays will fit within normal eye glasses or even contact lenses ( Schneiderman, 1997a&b). While this information could be at a fixed point in the user's field of view, it might be more natural for it to appear on blank walls rather than to float in the air. Blank spaces set aside for that purpose could then be used by any number of individuals to view completely different information simultaneously. This is similar to the recently developed practice of displaying virtual advertising information on billboard spaces in broadcasts of athletic events. The content of that space can then be different in different geographic areas. Ultimately, it can be tailored to the individual consumer. By whatever means, we can be sure that our virtual space of information display and our physical space with real objects will merge.

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URL: https://www.sciencedirect.com/science/article/pii/S0923843399800173

Publisher Summary

This chapter covers the history, background, and terminology associated with virtual reality (VR) technology and applications. Virtual reality is a medium, a means by which humans can share ideas and experiences. A typical VR system will substitute at least the visual stimuli, with aural stimuli also frequently provided. A third, less common sense that is included is skin-sensation and force feedback, which is jointly referred to as the haptic sense. Another common technology used to display the visual part of a VR experience is to project the images onto a large screen or multiple screens that cover a sizable amount of the participant's view. There are some related technological terms that are also often used in the discourse of virtual reality technology. If one considers virtual reality to be the simulation of an environment that allows a person to experience some place and event other than where they actually are and what is actually happening around them, then flight simulators are an early example of this medium. The creation of a virtual reality system requires the integration of multiple components.

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URL: https://www.sciencedirect.com/science/article/pii/B978012374943700001X

4 Future of Dynamic Mapping

Developments in computer science and technology concerning virtual reality (VR) may further expand the concept of dynamic mapping. VR technology produces virtual environments by computer (Shneiderman 1998). The user will become part of this virtual world and may interact with it via multifarious interfaces. In cartography, spatial processes could be created with VR technology to give the user a vivid and lifelike impression. In addition, VR technology could also be used to build abstract worlds in which to present spatial concepts or data in a dynamic and highly interactive way. In this way, VR technology will add more vividness and interactivity to cartography and dynamic mapping, however, it also changes the traditional cartographic viewpoint. In contrast to the traditional method of looking at a map, using VR means being in the presentation. This entails the danger of getting lost because the spatial overview that is provided by maps is missing.

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URL: https://www.sciencedirect.com/science/article/pii/B0080430767025365

Virtual

Because of the hype that has come to be associated with VR, the word virtual is often co-opted to imply that VR technology is involved. However, calling something virtual does not necessarily mean it falls within the scholarly definition of VR.

We have mentioned that virtual can be added to the name of a computing system to indicate that some component of the system is an extension of the hardware, emulating the real thing through another source. Another use is within simulated virtual worlds where objects exist virtually in that world. Because these objects are merely images of the physical objects they represent, the word virtual can be appended to the name of each object to indicate this. We can describe a virtual table in a virtual kitchen, both existing in a virtual world. In a related usage, the domain of optics employs the phrase virtual image to refer to objects that appear to exist through a lens or mirror, a use very similar to its meaning in VR. Many people use the term VR to describe interactive spaces like Second Life or Minecraft (Fig. 1-12). However, although Second Life is an interactive virtual world that could be experienced via VR, it is actually a virtual world that is typically experienced through a non-VR interface.

Virtual: (adj) An indication that an entity mimics the characteristics of another; in the context of a virtual world, any object in that world can be said to be virtual.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128009659000015

1.2 VR Software Applications

This chapter is not going to focus on VR hardware, but instead at our recent developments in VR software. At the Calit2 center of UCSD, we have been researching and developing VR software since 2005. The VR center at Calit2 is in a unique position, as it was created, like many other laboratories at Calit2, to be a shared resource for researchers on the entire UCSD campus to allow them to utilize VR approaches without having to make the investment in expensive VR hardware themselves.

The software framework we use in all our VR systems is called COVISE [4]; its VR renderer is called OpenCOVER [5]. VR applications are written in C++ as COVISE plugins, multiple of which can run at the same time. COVISE abstracts the fact that the application is running in parallel over many machines and multiple OpenGL contexts, and automatically handles things such as stereo perspective calculations and OpenGL context management so that the application developer does not need to worry about them.

The Calit2 VR group has been working with researchers from a variety of disciplines at UCSD, but it has also conducted its own independent research into VR software applications, especially real-time immersive rendering and 3D user interfaces. In this line of work, we have been focusing on getting away from the traditional mouse and keyboard interface, toward intuitive, immersive 3D interfaces. To us, good VR software applications use surround visualization and immerse the user in the data; 3D objects can be found at all possible distances from the user, from an arm's length to infinity; and user interaction does not just mean navigation of a static scene, but it means to interact directly with individual objects in the 3D scene with as much direct, object-oriented interaction as possible, without using menus or similar abstract constructs.

This chapter reports on some of the most innovative VR software applications we have created at Calit2. We are going to present one application from each of the following five topic areas:

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VR technology: Orthogonal developments of algorithms which do not stand alone, but are used in conjunction with other applications.

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Scientific visualization: The visualization of data sets which have inherent 3D structure, so that the mapping of data to the three spatial dimensions and sometimes also time is given.

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Real-time data visualization: The real-time visualization of data acquired by sensors and sent over networks.

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Information visualization: The visualization of data sets which do not have inherent 3D structure, where the mapping of data to the three spatial dimensions and time has to be created artificially and sometimes dynamically by the user.

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Cultural heritage: Applications aiming at preserving or investigating objects and places of a country's cultural heritage.

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URL: https://www.sciencedirect.com/science/article/pii/B9780123855121000062

6.3.3 Immersive technologies in appropriateness research

Immersive technologies, where consumers experience and interact with a computer generated virtual environment, are currently a major trend in sensory and consumer science. The possibility of conducting product evaluations in a fully immersive virtual environment is clearly a hotbed for context research, and is seen as a major opportunity to enhance the external validity of CLT tests (Jaeger & Porcherot, 2017). The application of VR is dealt with extensively in this book, and the reader is referred to for in-depth discussions.

A relevant question to ask here is what does the increasing availability of immersive technologies mean for appropriateness evaluations? This is difficult to answer sensibly, because at the time of writing, the VR technology is still in its infancy, and we probably need more experience to fully understand its value in sensory and consumer science. In general, VR technologies should offer interesting possibilities for addressing two of the perceived drawbacks of appropriateness research related to the elicitation of usage contexts in response to written descriptions (see Section 6.2.2.3): the low level of immersiveness, and the potential misalignment of these mental images between consumers (as well as between consumers and researchers). In principle, the use of VR technology can remedy both.

That aside, in this author's opinion, IBU and VR do rather different things, and are likely to have different uses. The VR approach seems most useful when the goal is go in depth into one (or a few) target contexts that are of interest to the researcher or product developer, or when that context is actually the only one relevant to that product category, for example, a car cabin for evaluation of a car air freshener. Here, the VR approach can be very useful to provide results (e.g., liking, willingness to pay) with a higher external validity than a context-neutral CLT-test. From this perspective, the VR approach is more in line with the tradition of conducting situational research in natural or physically manipulated environments (e.g., Bell et al., 1994; Di Monaco et al., 2014; Petit & Sieffermann, 2007; Sester et al., 2013).

While the IBU approach cannot replace real contexts to the same degree as (in principle) VR can, it affords the possibility of evaluating a larger number of products and usage contexts in the same session. This makes it especially useful for evaluating the performance of different products across different usage contexts (do consumers understand how to use the products? Which benefits do each product deliver, which products may be replaced or repositioned, etc.), and their perceived versatility. This information is, of course, most useful for products that have a variety of applicable contexts (typically the case for food and beverages), and when one is interested in understanding the cognitive structures pertaining to product usage within a specific product category.

From a practical perspective, the IBU method overall seems much better suited for the context of current CLT tests, which, for the most part, involve actual samples, because when it comes to eating while immersed in a VR environment, reaching a high enough level of realism and usability still presents a significant challenge.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128144954000064

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Virtual reality technology plays an important role in realizing Telesensation. Through it, a virtual world is created that viewers can enter and walk through and where they can handle virtual objects. The virtual world allows us a stereoscopic view from front or side, depending on our viewpoint, just as in the real world.

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by Radu Raicea

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now.

The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Once you've read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

This guide is intended for everyone, so no advanced mathematics will be involved.

Background

The first step towards understanding how Deep Learning works is to grasp the differences between important terms.

Artificial Intelligence vs Machine Learning

Artificial Intelligence is the replication of human intelligence in computers.

When AI research first started, researchers were trying to replicate human intelligence for specific tasks - like playing a game.

They introduced a vast number of rules that the computer needed to respect. The computer had a specific list of possible actions, and made decisions based on those rules.

Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules.

ML allows computers to learn by themselves. This type of learning takes advantage of the processing power of modern computers, which can easily process large data sets.

Supervised learning vs unsupervised learning

Supervised Learning involves using labelled data sets that have inputs and expected outputs.

When you train an AI using supervised learning, you give it an input and tell it the expected output.

If the output generated by the AI is wrong, it will readjust its calculations. This process is done iteratively over the data set, until the AI makes no more mistakes.

An example of supervised learning is a weather-predicting AI. It learns to predict weather using historical data. That training data has inputs (pressure, humidity, wind speed) and outputs (temperature).

Unsupervised Learning is the task of machine learning using data sets with no specified structure.

When you train an AI using unsupervised learning, you let the AI make logical classifications of the data.

An example of unsupervised learning is a behavior-predicting AI for an e-commerce website. It won't learn by using a labelled data set of inputs and outputs.

Instead, it will create its own classification of the input data. It will tell you which kind of users are most likely to buy different products.

Now, how does Deep Learning work?

You're now prepared to understand what Deep Learning is, and how it works.

Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs. Both supervised and unsupervised learning can be used to train the AI.

We will learn how deep learning works by building an hypothetical airplane ticket price estimation service. We will train it using a supervised learning method.

We want our airplane ticket price estimator to predict the price using the following inputs (we are excluding return tickets for simplicity):

Origin Airport

Destination Airport

Departure Date

Airline

Neural networks

Let's look inside the brain of our AI.

Like animals, our estimator AI's brain has neurons. They are represented by circles. These neurons are inter-connected.

The neurons are grouped into three different types of layers:

The input layer receives input data. In our case, we have four neurons in the input layer: Origin Airport, Destination Airport, Departure Date, and Airline. The input layer passes the inputs to the first hidden layer.

The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer.

The " Deep" in Deep Learning refers to having more than one hidden layer.

The output layer returns the output data. In our case, it gives us the price prediction.

So how does it compute the price prediction?

This is where the magic of Deep Learning begins.

Each connection between neurons is associated with a weight. This weight dictates the importance of the input value. The initial weights are set randomly.

When predicting the price of an airplane ticket, the departure date is one of the heavier factors. Hence, the departure date neuron connections will have a big weight.

Each neuron has an Activation Function. These functions are hard to understand without mathematical reasoning.

Simply put, one of its purposes is to "standardize" the output from the neuron.

Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer.

Nothing complicated, right?

Training the Neural Network

Training the AI is the hardest part of Deep Learning. Why?

For our airplane ticket price estimator, we need to find historical data of ticket prices. And due to the large amount of possible airports and departure date combinations, we need a very large list of ticket prices.

To train the AI, we need to give it the inputs from our data set, and compare its outputs with the outputs from the data set. Since the AI is still untrained, its outputs will be wrong.

Once we go through the whole data set, we can create a function that shows us how wrong the AI's outputs were from the real outputs. This function is called the Cost Function.

Ideally, we want our cost function to be zero. That's when our AI's outputs are the same as the data set outputs.

How can we reduce the cost function?

We change the weights between neurons. We could randomly change them until our cost function is low, but that's not very efficient.

Instead, we will use a technique called Gradient Descent.

Gradient Descent is a technique that allows us to find the minimum of a function. In our case, we are looking for the minimum of the cost function.

It works by changing the weights in small increments after each data set iteration. By computing the derivative (or gradient) of the cost function at a certain set of weight, we're able to see in which direction the minimum is.

To minimize the cost function, you need to iterate through your data set many times. This is why you need a large amount of computational power.

Updating the weights using gradient descent is done automatically. That's the magic of Deep Learning!

Once we've trained our airplane ticket price estimator AI, we can use it to predict future prices.

Where can I learn more?

There are many other types of neural networks: Convolutional Neural Networks for Computer Vision and Recurrent Neural Networks for Natural Language Processing.

If you want to learn the technical aspect of Deep Learning, I suggest taking an online course.

Currently, one of the best courses for Deep Learning is Andrew Ng's Deep Learning Specialization. If you're not interested in getting a certificate, you don't need to pay for the course. You can audit it for free instead.

If you have any questions, or want more technical explanations of the concepts, please ask below!

In summary...

Deep Learning uses a Neural Network to imitate animal intelligence.

There are three types of layers of neurons in a neural network: the InputLayer, the Hidden Layer(s), and the Output Layer.

Connections between neurons are associated with a weight, dictating the importance of the input value.

Neurons apply an Activation Function on the data to "standardize" the output coming out of the neuron.

To train a Neural Network, you need a large data set.

Iterating through the data set and comparing the outputs will produce a Cost Function, indicating how much the AI is off from the real outputs.

After every iteration through the data set, the weights between neurons are adjusted using Gradient Descent to reduce the cost function.

If you enjoyed this article, please give me some claps so more people see it. Thanks!
You can also check out my experience of how I got my internship at Shopify!
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Want to know how Deep Learning works? Here's a quick guide for everyone.

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by Radu Raicea Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services.

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The NCES Fast Facts Tool provides quick answers to many education questions (National Center for Education Statistics). Get answers on Early Childhood Education, Elementary and Secondary Education and Higher Education here.

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Impact of gamification and industry 4.0 tools have on Education 4.0

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Impact of gamification and industry 4.0 tools have on Education 4.0

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Highlights

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Motivational effects of gamification are analysed from an educational psychology perspective.

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Gamification is not effective per se, but different game design elements can trigger different motivational outcomes.

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A self-determination theory framework was used to study the effects of different configurations of game design elements.

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Competence and autonomy regarding task meaningfulness was affected by badges, leaderboards, and performance graphs.

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Social relatedness was positively influenced by avatars, a meaningful story, and teammates.

Abstract

The main aim of gamification, i.e. the implementation of game design elements in real-world contexts for non-gaming purposes, is to foster human motivation and performance in regard to a given activity. Previous research, although not entirely conclusive, generally supports the hypothesis underlying this aim. However, previous studies have often treated gamification as a generic construct, neglecting the fact that there are many different game design elements which can result in very diverse applications. Based on a self-determination theory framework, we present the results of a randomized controlled study that used an online simulation environment. We deliberately varied different configurations of game design elements, and analysed them in regard to their effect on the fulfilment of basic psychological needs. Our results show that badges, leaderboards, and performance graphs positively affect competence need satisfaction, as well as perceived task meaningfulness, while avatars, meaningful stories, and teammates affect experiences of social relatedness. Perceived decision freedom, however, could not be affected as intended. We interpret these findings as general support for our main hypothesis that gamification is not effective per se, but that specific game design elements have specific psychological effects. Consequences for further research, in particular the importance of treatment checks, are discussed.

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How gamification motivates: An experimental study of the effects of specific game design elements on psychological need satisfaction

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Motivational effects of gamification are analysed from an educational psychology perspective. * Gamification is not effective per se, but different game design elements can trigger different motivational outcomes. * A self-determination theory framework was used to study the effects of different configurations of game design elements.

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Gamified virtual learning technology example 

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Gamified virtual learning technology example 

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The Virtual Learning Factory Toolkit (VLFT) project is a pioneering program commissioned by the European Union using virtual and augmented reality to enhance engineering education programs across Europe. Five EU partners make up the VLFT Consortium, including Estonia's Tallinn University of Technology, Hungary's Institute for Computer Science and Control, Italy's Politecnico di Milano, National Research Council of Italy, and Sweden's Chalmers University of Technology. Using Khronos® standards, the VLFT consortium has created a suite of tools to gamify learning, strengthen information and communication technology (ICT) skills, and better prepare students for jobs in 21 st century manufacturing.

Why the VLF Toolkit was created

The VLFT project is financed by the European Commission's Erasmus+ program. This program aims to help generations of Europeans become active citizens, with the skills, knowledge and experience to tackle the challenges facing society now and in the years to come. This project focuses on the development and integration of Virtual Learning Factory (VLF) tools that can be used in production management and engineering education all over Europe.

Higher education has to keep pace with modern ICT, and engineering education in particular must offer students an understanding of the complexity of 21st century industry. Global market companies have to deal with the constant evolution of products, processes, and production systems, all in parallel. All these facets of industrial production can be more easily monitored, developed, and upgraded using digital applications based on the "digital twin" paradigm. The VLFT project helps industrial engineering students learn to take advantage of various enabling technologies like data modeling, discrete event simulation, Virtual Reality (VR) and Augmented Reality (AR).

The VLFT project does not create any new curricula. Instead, it improves existing manufacturing curricula by strengthening digital skills. By developing, testing, and using the new VLF toolkit, trainers, teachers, professors, and students improve their ICT skills and better understand real-world processes and production management, including developing virtual factories.

The VLFT Gamification Environment

The VLFT includes a multi-user VR-based gamification environment. This virtual factory provides students with a realistic representation of a manufacturing system aimed to allow them to:

Analyze the processes associated with manufacturing various products

Assess the capabilities and processing times of machines

Monitor failures and analyze related statistics

Identify system bottlenecks

In addition to inspection activities, first person experiences can be set up thanks to VR. For example, VR can be used to simulate manual manufacturing activities for training purposes or to carry out ergonomic analyses.
VLFT Gamification VR Environment in Single Player Mode
Compared to traditional learning methods, digital tools have the potential to enable more interactive experiences. The VLFT Gamification Environment can realistically simulate a walkaround inside a manufacturing plant in complete safety. That eliminates the need for a physical space, an especially relevant advantage during the COVID-19 pandemic. More users can participate in the same virtual environment from different locations, and even interact with the digital items in ways that would not be possible with their physical counterparts.

Crucially, the VLFT Gamification Environment enables students from across the EU to work and learn together. Transnational migration is an established trend in the current EU labor market. Gathering in a rich VR environment, students gain an opportunity to cooperate with the foreign colleagues, clients, and suppliers who will play an important part of their future work routine. Joint lab sessions have been organized with students from the different partner universities collaborating as mixed, multinational groups towards common objectives, allowing the exchange of competencies between team members with different educational backgrounds.

Enabling Collaboration with Open Standards

The Institute for Computer Science and Control, Politecnico di Milano and the National Research Council of Italy collaborated to implement this new, innovative gamification VR app. Software engineers, manufacturing engineers, professors, teachers and 3D artists needed to work together efficiently to deliver the final product on time. Since the 3D artists and manufacturing engineers already use 3D modelling tools, the whole development chain contained external software vendors as well. Moreover, software engineers also used external rendering software libraries from other vendors. These interdisciplinary circumstances implied the need for a common language between the diverse groups of the development team.

Prior to adopting glTF™, the development phase for this project had to be extended with several time-consuming tasks, including defining a custom scene description, building a custom exporter from 3D modelers, and creating custom importers to rendering engines for geometries, materials and shaders. Ultimately, it became clear that the VLFT needed to use open-source frameworks and royalty-free interoperability standards during the implementation of the VLFT Gamification VR Environment.

The Khronos glTF file format for 3D scenes and models helped unite the different groups and disciplines within the VLFT VR Gamification Environment development team. glTF is the only royalty-free scene descriptor with photorealistic options that is supported by all VLFT Gamification Environment internal and external stakeholders. Internal stakeholders, including software engineers, manufacturing engineers, professors, teachers, and 3D artists, all use the glTF standard as a common 3D asset format. External stakeholders such as 3D modelling tools and rendering engines offer exporters and importers supporting glTF for rapid VR content generation. The glTF standard enables the interoperable use of 3D content across these different stakeholders by the efficient transmission and loading of 3D scenes and models by engines and applications.

In addition, the Khronos OpenXR™ API standard for VR and AR platforms and devices proved to be the only option to create a portable and maintainable source code that can run on Head Mounted Devices (HMDs) from multiple hardware vendors. Through using OpenXR, students can utilize their choice of HMD across multiple operating systems, and any OpenXR application need be implemented only once, as it is portable to any HMD or OS where the vendor provides an OpenXR runtime.

The VLFT uses the OpenXR plugin of the ApertusVR open-source virtual reality software library to implement the VR environment gameplay irrespective of which HMD a student is using. With the help of OpenXR, the XR development phase of the VLFT VR Gamification Environment was significantly streamlined.

What's Next for the VLFT

The VLFT Gamification multi-user VR environment is only one of the VLF tools. This multi-user VR-based gamification environment is a minimum viable product on the technology readiness level 6. The whole VLFT has room for improvements as new methods and technologies are made available and implemented.

The VLFT Consortium established fundamental requirements from the very beginning to ensure that the VLFT can be adopted as a whole or subset by other educational institutions and companies. Through leveraging Khronos open standards and developing open-source applications to reduce implementation costs and increase interoperability, the consortium is enabling widespread adoption by a large user base, which will in turn help to enrich and develop the toolkit itself.

Interested organizations are encouraged to access the VLFT Consortium's comprehensive and freely available documentation, created to allow anybody to apply the same workflow to different use cases.

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Using Khronos Standards to Gamify Engineering Education

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The Virtual Learning Factory Toolkit (VLFT) project is a pioneering program commissioned by the European Union using virtual and augmented reality to enhance engineering education programs across Europe.

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Effects of challenge-based gamification on learning

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Effects of challenge-based gamification on learning

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Highlights

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We investigated the impact of gamification on learning in statistics education.

*

We examined challenge-based gamification (points, levels, challenges, leaderboard).

*

We ran a 2 (read yes | no) x 2 (gamification yes | no) between-subject experiment.

*

Gamification had a positive impact on learning.

*

Effect was larger for females and Engineering students versus Business School students.

Abstract

Gamification is increasingly employed in learning environments as a way to increase student motivation and consequent learning outcomes. However, while the research on the effectiveness of gamification in the context of education has been growing, there are blind spots regarding which types of gamification may be suitable for different educational contexts. This study investigates the effects of the challenge-based gamification on learning in the area of statistics education. We developed a gamification approach, called Horses for Courses, which is composed of main game design patterns related to the challenge-based gamification; points, levels, challenges and a leaderboard. Having conducted a 2 (read: yes vs. no) x 2 (gamification: yes vs. no) between-subject experiment, we present a quantitative analysis of the performance of 365 students from two different academic majors: Electrical and Computer Engineering (n=279), and Business Administration (n=86). The results of our experiments show that the challenge-based gamification had a positive impact on student learning compared to traditional teaching methods (compared to having no treatment and treatment involving reading exercises). The effect was larger for females or for students at the School of Electrical and Computer Engineering.

Keywords

Applications in education

Recommended articles Citing articles (0)

Nikoletta-Zampeta Legaki is a Marie - Curie Researcher at Gamification Group, Tampere University. She pursuits her PhD in School of Electrical and Computer Engineering of National Technical University of Athens (NTUA), Greece. She has been a researcher and teaching assistant in Forecasting and Strategy Unit, School of Electrical and Computer Engineering since 2012. During this period, she has participated in various research projects about forecasting and data analytics and she has worked as a consultant in Financial Services and Risk Management, in EY, Greece. Her research interests lie on time series forecasting, business forecasting information systems, gamification and educational methods in teaching forecasting.

Dr. Nannan Xi is a Postdoctoral researcher in Gamification Group. She got her PhD in marketing management from Zhongnan University of Economic and Law (ZUEL), China and holds M.Sc. in International Business from the University of Lincoln, UK. Her dissertation was awarded as the excellent Ph.D. dissertation in ZUEL. Xi's current research focuses on gamification in marketing, especially in gamified interaction in brand management. In addition, her research interests include customer management in gamification and virtual reality/augmented reality/mixed reality in business and sharing economy.

Dr. Juho Hamari is a Professor of Gamification at the Faculty of Information Technology and Communications, Tampere University. He leads the Gamification Group. His research covers several forms of information technologies such as games, motivational information systems, new media (social networking services, eSports), peer-to-peer economies (sharing economy, crowdsourcing), and virtual economies. Dr. Hamari has authored several seminal empirical, theoretical and meta-analytical scholarly articles on these topics from perspective of consumer behavior, human-computer interaction, game studies and information systems science. His research has been published in a variety of prestigious venues.

Dr. Kostas Karpouzis is currently an Associate Researcher at the Institute of Communication and Computer Systems (ICCS) of the National Technical University of Athens (NTUA) in Greece. His research interests lie in the areas of human computer interaction, emotion understanding, affective and natural interaction, serious games and games based assessment and learning. He has participated in more than twenty research projects at Greek and European level. He is also a member of the Student Activities Chair for the IEEE Greece Section and a member of the Editorial Board for international journals.

Prof. Vassilios Assimakopoulos is a professor of Forecasting Systems at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He is the author of over than 60 original publications and papers in international journals and conferences. Moreover, he has conducted research on innovative tools for management support and decision systems design. He has participated and led numerous projects, funded by National and European institutes. He specializes in various fields of Strategic Management, Design and Development of Information systems, Business Resource Management, Statistical and Forecasting Techniques using time series.

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The effect of challenge-based gamification on learning: An experiment in the context of statistics education

# data[].attributes.attachment_link.description
We investigated the impact of gamification on learning in statistics education. * We examined challenge-based gamification (points, levels, challenges, leaderboard). * We ran a 2 (read yes | no) x 2 (gamification yes | no) between-subject experiment. * Gamification had a positive impact on learning.

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Impact of gamification in students learning

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Impact of gamification in students learning

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Smart Learning Environments 7, Article number: 3 (2020) Cite this article

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Abstract

The gamification of education can enhance levels of students' engagement similar to what games can do, to improve their particular skills and optimize their learning. On the other hand, scientific studies have shown adverse outcomes based on the user's preferences. The link among the user's characteristics, executed actions, and the game elements is still an open question. Aiming to find some insights for this issue, we have investigated the effects of gamification on students' learning, behavior, and engagement based on their personality traits in a web-based programming learning environment. We have conducted an experiment for four months with 40 undergraduate students of first-year courses on programming. Students were randomly assigned to one of the two versions of the programming learning environment: a gamified version composed of ranking, points, and badges and the original non-gamified version. We have found evidence that gamification affected users in distinct ways based on their personality traits. Our results indicate that the effect of gamification depends on the specific characteristics of users.

First part title: Studying the impact of gamification on learning and engagement based on the personality traits of students

Introduction

Gamification of education is a strategy for increasing engagement by incorporating game elements into an educational environment ( Dichev and Dicheva 2017). The goal is to generate levels of involvement equal to what games can usually produce ( Fardo 2014). The main goals of gamification are to enhance certain abilities, introduce objectives that give learning a purpose, engage students, optimize learning, support behavior change, and socialize ( Knutas et al. 2014; Krause et al. 2015; Dichev and Dicheva 2017; Borges et al. 2013).

Stimulated by the effects that game elements can produce, many researchers have looked into the influence of gamification in an educational context, getting favorable results, such as the increase of engagement, user retention, knowledge, and cooperation ( Hakulinen and Auvinen 2014; Tvarozek and Brza 2014). Despite that, some studies have shown uncertain or prejudicial results from gamification ( Christy and Fox 2014). They found that ranking affects women in various ways and may guide to unexpected opposite impact. Hanus and Fox ( 2015) informed that, in addition to not increase the results, gamification decreases pleasure and motivation. Haaranen et al. ( 2014) noticed that some users had adverse emotions about the badges.

The mix of controversial results related to the effects of gamification in learning environments yield doubts concerning the advantages of its utilization in an educational setting. Moreover, research about the effects of gamification elements on students' learning, participation, and other effects, is a broad goal. The objective should be delimited to what elements of games are efficient for a particular type of student, involved in a given activity ( Dichev and Dicheva 2017). Different layouts of elements of games, used to add gamification to diverse activities, produces different effects, hampering the process of determining which elements or collection of these elements are efficient to promote the engagement and learning for a group or type of user, doing a specific action ( Dichev and Dicheva 2017). The motivation ( Pedro 2016; Hakulinen and Auvinen 2014; Mekler et al. 2017), player profile ( Barata et al. 2014; O'Donovan et al. 2013) and personality ( Codish and Ravid 2014; Jia et al. 2016) are the characteristics and preferences that have been most investigated in gamified learning environments.

The user's personality is the set of characteristics and psychological factors that are used to understand how individuals think and interact ( Goldberg 1992). Personality traits refer to an individual's reactions to different situations, and little is known about how different elements of gamification affect engagement based on the user's traits ( Codish and Ravid 2014). Empirical studies are needed to verify whether the effect of gamification may differ depending on users' personality traits.

Codish and Ravid ( 2014) researched, through preference surveys, how extroverts and introverts received the gamification and discovered an adverse effect of the ranking on extroverted students and favorable but not substantial on introverted students; extroverts chose the badges. On the other hand, Jia et al. ( 2016) found different results, in which, also through preference surveys, identified that extroverted people are driven by points, levels, and ranking. Jang, Park, and Yi ( 2015) found that users with low agreeableness who used a non-gamified version of a system had lower learning rates than those who used the gamified one.

The previously found results were crucial for the conceptual comprehension of the effect of gamification on personality, but they were only based on users' opinions, obtained through questionnaires and in a short time. It is essential to conduct experiments to verify the real effects of gamification in learning environments and over a long duration.

In this study, we aimed to study whether the gamification affects students differently depending on their personality traits. More specifically, we aimed to investigate whether distinct components of gamification affect students' learning, their programming attitudes (trial and error behavior in the programming tasks submission for correction), and engagement depending on their personality traits (extroversion, openness, agreeableness, neuroticism, and conscientiousness) in the context of programming learning. Personality traits were selected because there is a lack of empirical studies in real environments that target this topic, and the results of the formal works are an open question. In our work, the effects of gamification for different personality traits were investigated with an empirical experiment in a real learning environment over a longer interval of time (four months). We examined participants' engagement and behavior by their activities logs on the educational system and their learning by knowledge tests.

This work is an extended version of our paper published on ICALT 2019 ( Smiderle et al. 2019). Differently from ICALT paper, in which we have focused only on the extroversion trait, in the present article, we also explore how the other personality traits (openness, agreeableness, neuroticism, and conscientiousness) interfere in the impact of gamification elements on students' learning, engagement, and programming behavior.

Method

Participants

The participants were universities student from two first-semester classes in a Computing course at a private university in the state of Rio Grande do Sul, Brazil. In total, 48 students aged between 17 and 34 years old (M =21, DP =3.74), 38 boys, and ten girls, were invited at the beginning of the semester for participation. Consent forms were delivered for all students, who agreed to join in the experiment, to sign. Only the information of the students who fulfilled the personality trait questionnaire (43 students) and agreed to participate by returning the content form (41 students) was considered at the end of the experiment, totalizing 40 students (7 girls and 33 boys). At the beginning of the experiment, the teachers explained to the students that their participation in the experiment was voluntary; they could quit at any moment, and this would not change their final grade in the class.

Measures

We verified the change in engagement by the number of logins, badges, points, and also the number of visualizations of the gamification elements. The grades in the course exams served to evaluate learning. The programming behavior was measured by the accuracy of the solutions submitted by students for programming exercises. Accuracy is the result of the total number of correct solutions divided by the total number of solutions sent. It represents the student care before submitting a solution, being the opposite of trial and error behavior, in which the student sends different solutions repeatedly until success, without seriously reflecting on them, only to get the system feedback.

Materials

Personality questionnaire - iGFP-5

To determine students' personalities, we have used the IGFP-5. IGFP-5 is a self-reported measure composed of 44 items and designed to evaluate the personality dimensions based on the Big Five Personality Factors model (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism) ( de Andrade 2008). It was validated for Brazil through a sample of 5,089 respondents from the five Brazilian regions, 66.9% female, and 79.0% higher education. According to Andrade ( 2008), individuals with high scores in Openness are generally outspoken, imaginative, witty, original, and artistic. Conscientious individuals are generally cautious, trustworthy, organized, and responsible. Extroverted individuals tend to be active, enthusiastic, sociable, and eloquent or talkative. People with high scores in agreeableness are pleasant, lovely, cooperative, and affectionate. Neurotic individuals are usually nervous, highly sensitive, tense, and concerned.

BlueJ and feeper

Feeper1 is a web-based system designed to assist students and teachers in programming classes. In the environment, the teacher can provide programming exercises, which can be solved by students, and automatically corrected by an Online Judge integrated on the platform. It matches the output of the learners' program with the output of an ideal solution provided by the teacher for a given input. It uses rules previously registered by the teacher also to give some feedback for the students based on the output of their code. A significant advantage of this type of environment is that it reduces the teacher's burden because it corrects the exercises automatically, allowing the teacher to concentrate their efforts on students who are struggling with the tasks.

BlueJ is a free Java Development Environment designed for beginners to learn the basics of programming ( Bluej 2019).

In our work, the teacher recommended students to write their code at BlueJ, which has a more straightforward interface for beginners. After solving the task on BlueJ, students should submit the final solution to Feeper to get the correction and error-feedback. Only Feeper was gamified in this study, and it was used by students to verify their progress.

System logs and grades

The information extracted from Feeper through the use of the environment consisted of the number of: logins, correct and wrong exercises, badges and points obtained, and challenges completed. We also analysed the number of users' views of the elements ranking, badges, and points. The number of badges view is different from the number of badges obtained. When we counted the number of views, we were analyzing how interested the student was in this element. A student can get many badges because she accomplished the activities successfully due to her interest in the topic, even if she is not interested in getting badges. The same is true for the Points and Ranking.

During the semester, students accomplished three exams as part of their formal evaluation process of the class. Grade A was delivered in the middle of the semester; it was comprised of problems related to topics seen until it. Grade B was the last exam, delivered at the end of the semester. When students were not able to achieve the minimum score, they could improve their grade with Grade C, which was delivered two weeks after Grade B. In this work, grade A contributed to check students' performance in programming before gamification switching on in the experimental group. The participants completed the IGFP-5 personality questionnaire and were randomly distributed into two groups, the control and the gamified. At the end of the semester, they took the final exam.

Gamification in feeper

The gamification elements implemented on Feeper for this study are points, badges, and ranking, described below. The only difference between the gamified and non-gamified versions of Feeper is that participants in the non-gamified version cannot see the gamification elements, but internally the system still scores points and badges. This score allows us to compare whether students be able to see the gamification elements engage them.

Points

Points appear to participants in two different parts of the system. When students are completing a programming task, they can see how many points they could earn if they solve it successfully. When the solution is incorrect, the score is decreased by five points for each submission (the students can lose a maximum of 70 points for each task). Students can also view their score histories for the solved exercises and the points previously earned. Students were warned that the scores obtained in the exercises would not affect their final grade on the course.

Badges

Nine distinct badges were granted to users by obtaining specific objectives, with three degrees (gold, silver, bronze), totalizing 27 badges. Badges were granted for students who have achieved a specific sum of logins, correct assignment, submitted assignments, submitted assignments with no errors, daily activity, and for whom have concluded challenges and were top of the class and the platform.

Ranking

The ranking is the sum of all points earned by students for all assignments solved. There are two distinct rankings available. The ranking of the class shows the participants with the best scores in the class; its goal is to promote local objectives for students. The second one is the general ranking, which contrasts the scores of all students of the platform who have used Feeper.

Experimental design

This experiment followed an experimental design consisting of two groups, control (21 participants) and experimental groups (19 participants), for which the students were randomly assigned with the only restriction of having the same number of participants initially in both groups. Table 1 shows the number of participants for each personality trait in each group (gamified and non-gamified).

Table 1 Number of Participants for each Personality Trait

Students in the control group used the original non-gamified version of Feeper, while learners in the experimental group used a gamified version of Feeper with points, badges, and ranking. All students started using the non-gamified variant of Feeper, and only in the second half of the semester (after the first exam, grade GA), students in the experimental group began to use the gamified version.

This type of design allows us to examine the effects of gamification on personality traits using both controlling conditions: the participant with himself (a within-subject design, by comparing the performance and engagement of students of the experimental group before and after Grade GA) and by comparing control and experimental groups after Grade GA. At the end of the semester, students completed the final exam (Grade B), involving all the content of the course. Figure 1 illustrates the phases of the experiment.

Some students have reported that they noticed their version of Feeper was different from the one used by a nearby colleague (they were able to notice the presence of points and ranking). When this occurred, teachers only reported that some new features were being tested in Feeper and were only available to some participants.

Procedure

The experiment was realized in the second part of 2018 and had a period of four months. The participants had class once a week, and each class had two hours and 38 minutes of duration. Students used Feeper in all classes, except for the first class, the three classes in which the teacher delivered the exams (Grade A, Grade B, and Grade C), and one class of topic review, totalizing 15 classes solving program tasks using BlueJ and Feeper.

In the first week of class, the teacher presented to the students the organization and some introductory notions of computer organization. In the second week, the teacher presented to students the Feeper and BlueJ environments (" Materials " section). Introductory tasks were given for the students to get used to both learning environments.

From the third week onward, students realized four exercises in each class: a worked example, two activities that were part of the final grade, and an optional exercise. The teacher began each class by solving a worked example step-by-step to teach the students how to solve a programming task involving the same concepts to be worked in the class.

Students then solved two other programming tasks with the same difficulty level as the example worked and using the same programming concepts. The students' grade was composed of these two tasks accomplishments (20%) and the score on the exams (80%). In addition to being part of the grade, these tasks served to identify students' difficulties. The optional task was an extra activity with greater difficulty and with the possibility of additional grade. The goal was to challenge the students and also verify their engagement as it was optional.

Results and analysis

This section presents the results found in this study. We conducted a Shapiro-Wilk test to verify the normality of the data. For the data that followed a normal distribution, we conducted a t-test to compare means. We also calculated the effect size for all tests, which is a simple way to quantify the difference between two groups, through the Cohen d effect. For data with non-normal distributions, we applied a Wilcoxon rank-sum test to compare means. The effect size of non-normal data was calculated by dividing the z value by the square root of the number of participants, as described by Mann-Whitney ( Pallant 2010).

Table 2 shows the results of the evaluation comparing the experimental and control groups. The level of significance was set at a <0.05 with 95% confidence interval. Only statistically significant results are shown.

Table 2 Results of gamified and non-gamified groups according personality trait

Participants who used the gamified environment had a higher average of points, badges, and the number of logins than participants of the non-gamified group. However, no statistically significant results were found to show that the gamified group was more engaged than the non-gamified group. Regarding the grades, both groups had a reduction from grade GA to grade GB, which is usual in this class as grade GB has more content and is more difficult than GA.

The gamified system can change student behavior. Gamified group participants had a significant improvement in the quality of the submitted solutions, having obtained more accuracy (number of correct solutions sent, divided by the total number of solutions sent), when comparing Grade A and Grade B. In the literature, it is possible to find papers that reported improved performance of the gamified group by increasing the score, ( Krause et al. 2015) and decreasing unwanted behaviors ( Pedro 2016). Another work that studied the effects of gamification on the performance of users on tagging pictures ( Mekler et al. 2017) has found an increase in the number of tag annotations, but without resulting in improved tag quality.

Each personality trait was verified individually. Introverted participants (in both experimental and control groups) had a higher number of points, medals, and logins than extroverted. A statistically significant difference was found in the number of points and ranking visualization between the introvert and extrovert gamified groups, thus indicating a divergence on how users with different personality traits receive the gamification effect. In addition, a statistically significant difference was found in the accuracy gain of the introvert participants in the gamified group.

Regarding the personality trait neuroticism, no differences were observed between the gamified and non-gamified groups. However, it can be noted that in both groups, people with high neuroticism had a higher number of logins. About the personality trait conscientiousness, students of the non-gamified group with low conscientiousness obtained the lowest number of points when compared to the other groups.

The correlation of all personality traits and the variables was calculated and showed in Fig. 2. They were used to identify the relationship between the dependent variables (engagement, learning, programming behavior) and personality traits. A correlation is considered strong when the Pearson coefficient is higher than 0.7, moderate when it is between 0.5 and 0.7, and weak when it is lower than 0.5.

Correlogram for the relationship between all variables of the experiment

Regarding the number of points obtained, a moderate negative correlation ( r=−0.52, p=0.01) was identified with the extroversion personality trait for the gamified group. For the nongamified group, a weak positive correlation between the points and the conscientiousness personality trait ( r=+0.41, p=0.01) was found.

Regarding the number of badges obtained, a moderate negative correlation was found with the extroversion personality trait ( r=−0.52, p=0.01).

Concerning the accuracy after the grade GA, for the gamified group, a moderate negat ive correlation was found with the extroversion trait ( r=−0.57, p<0.01) and a weak negative correlations with the openness trait ( r=−0.42, p=0.05). A positive weak correlation was found with the conscientiousness trait ( r=0.3, p=0.18). For the non-gamified group, a moderate positive correlation was identified between accuracy after GB and the conscientiousness personality trait ( r=0.5, p=0.02).

We also analyzed the correlation between the engagement of students in the gamified group with the different gamification elements (points, medals, and ranking). About the number of ranking views, we observed a strong negative correlation with the extroversion trait ( r=−0.79, p<0.01), and also a weak negative correlation with agreeableness trait ( r=−0.48, p=0.02).

Discussion

Each trait was verified individually. Introverted participants in both control and experimental groups had a higher number of points, badges, and logins. A statistically significant difference was found in the number of points and ranking views between the introvert and extrovert students who used the gamified version, thus indicating that there is a difference between how different users with different personality traits receive the effect of gamification. In addition, a statistically significant difference was found in the accuracy gain of the introverted participants who used the gamified version.

This result partially matches with the results found in ( Codish and Ravid 2014), that detected a negative effect of the ranking on extroverted participants and positive and not significant in introverted participants; extroverts preferred badges. However, unlike our work ( Jia et al. 2016) found that extroverts tend to be more motivated by points, levels, and ranking.

Regarding neuroticism, no differences were observed between the gamified and non-gamified groups. Overall, in both groups, neurotic participants had more logins. People with high neuroticism tend to be more worried and insecure, which, possibly, makes them check the platform more often for new exercises.

Regarding the grades, the gamified group with high agreeableness had the lowest grade reduction (the difference between GB and GA), while the non-gamified group with low agreeableness had the lowest grade reduction. Related to this personality trait, ( Jang et al. 2015) found that users with low agreeableness in the non-gamified group had lower learning rates than those in the gamified group, thus being more useful to these users. Differently from the mentioned work, we have not found any evidence that gamification improves learning for the students with low agreeableness.

About the conscientiousness personality trait, in both gamified and non-gamified groups, the high conscious participants were more accurate in solving the exercises, and only the low conscious students who used the non-gamified system had accuracy decreased. This decrease was significant, and thus it is possible to observe that the loss of accuracy of the non-gamified group came from low conscientious participants. A possible explanation is that people with this personality trait are more careless and negligent.

Limitations of study

When the user fills out a personality trait questionnaire, it has a score (a value ranging from 5 to 25) across five different personality traits. For example, a score on the extroversion trait means that the person tends to be more extroverted or introverted. An important issue is which score to use to classify someone as extroverted or introverted. As our sample was relatively small (only 40 participants), we have classified the participants using the median. This procedure was a provisional measure, and we intend to use a more reliable method for classifying participants in the next works. A trustworthy possibility in a larger sample would be to check only the extremes individuals of each personality trait.

Another limitation to the validity of the results is the representativeness of the sample since all participants who joined the study are from the same university, most of them young males. Therefore, it is not reasonable to generalize the results for the whole student population. Statistically, it can be solved with the replication of this experiment in distinct samples of undergraduate students.

The results were obtained through an empirical experiment involving programming tasks. It is also necessary to replicate the study for other domains such as mathematics, physics, etc.

Conclusions

This article aimed to contribute to the understanding of how gamification affects participants differently depending on their characteristics. More specifically, we investigated the effects of points, ranking, and badges in the engagement, learning, and behavior programming of students based on their personality traits. To achieve this goal, we have conducted an empirical experiment with 40 undergraduate students enrolled in a programming class who used two different versions of a programming learning environment: a gamified one and a non-gamified.

The results showed a change in the behavior of the gamified group showing a significant improvement in the accuracy of students with personality traits with low agreeableness, low openness, and introverts who used the gamified version in the second half of the course. A reduction during the semester (GA/GB) for accuracy was also verified for students with low conscientious personality who used the non-gamified system, while, in the gamified group, this reduction has not occurred, indicating that gamification may help these groups. Introverted students who used the gamified version were more engaged than extroverted students for the same version. We have also found a strong negative correlation between the extroversion trait and the number of ranking views, indicating that gamification in general and, especially the ranking element, is more beneficial to introverts.

This work contributes to the understanding of how gamified environment systems affect users based on their characteristics. Specifically, it contributed to the comprehension of how gamification affects the engagement and learning behavior of university students based on their personality traits. Future research could study the effect of gamification in various disciplines over a more extended period. It could help to verify whether, over time, gamification loses its effectiveness, to identify possible saturation points and limitations in its application.

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The first and last authors store the datasets. However, the Informed Consent Form that was used does not allow the distribution of the data fully.

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Acknowledgements

This study was financed in part by the CAPES (graduate student scholarship), FAPERGS (granting 17/2551-0001203-8), and CNPq (granting 309218/2017-9) from Brazil.

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Authors and Affiliations

1. Centro de Educação (UFAL) e Núcleo de Excelência em Tecnologias Sociais - NEES (UFAL), Maceió, Brazil

Leonardo B. Marques

2. Faculdade de Medicina (Famed/Ufal) e Laboratório de Computação Científica e Análise Numérica (LaCCAN/Ufal), Maceió, Brazil

Jorge Arthur Peçanha de Miranda Coelho

3. Programa de Pós-graduação em Computação Aplicada (PPGCA) - University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil

Rodrigo Smiderle, Sandro José Rigo & Patricia A. Jaques

Contributions

All authors have had a scientific contribution in this manuscript, with the first and last authors contributing the most to the design, implementation, analysis, and write up of the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Patricia A. Jaques.

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Smiderle, R., Rigo, S.J., Marques, L.B. et al. The impact of gamification on students' learning, engagement and behavior based on their personality traits. Smart Learn. Environ. 7, 3 (2020). https://doi.org/10.1186/s40561-019-0098-x
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DOI: https://doi.org/10.1186/s40561-019-0098-x

Keywords

Gamification

Personality

Programming Environment

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The impact of gamification on students' learning, engagement and behavior based on their personality traits - Smart Learning Environments

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The gamification of education can enhance levels of students' engagement similar to what games can do, to improve their particular skills and optimize their learning. On the other hand, scientific studies have shown adverse outcomes based on the user's preferences. The link among the user's characteristics, executed actions, and the game elements is still an open question.