Course:COGS200/2017W1/ProjectGroup6
Introduction
Summary
With virtual-reality technologies advancing and infinite information being available at our fingertips, we want to bring an adaptive learning based education software to the homes of primary school aged children.
We are designing a software program utilizing immersive virtual reality that can be downloaded onto either the child’s or parent's smartphone. This software interactively teaches elementary school children science and math through game based activities. The program would have access to the provincial science and math curriculums but also incorporate what works best across cultures providing more language and cultural-based support. The teaching style and curriculum is altered based on analysis of the child’s learning style. This is accomplished using adaptive learning systems collecting data from the student’s performance on activities and assignments. The program, using adaptive learning methods, analyzes the student by giving them a series of preliminary tests that will focus in on the BIG FIVE personality traits and also various different learning styles in order to determine what kind of teaching method to use to give the student the best learning experience and outcome. This program is meant to be a supplement to the curriculum learned at their regular school. The program will consist of interactive virtual diagrams, as well as educational games and activities. We propose to develop this product using the Google Daydream headset and motion controller, as well as the Unity Engine.
By creating an interactive and engaging learning application that adapts to an individual's learning style, we aim to address some of the issues in our current education system. These include issues related towards different learning styles as well as information processing.
Why our product is important
This project proposes solutions to many problems in our current education system. We want to provide a strong foundation of math and science for younger children. This is not only to encourage children to pursue these subjects when they are older, but also apply their knowledge to the social sciences or fine arts. We also want to address the many different styles of learning children have and create a curriculum that can adapt to each individual student’s learning style. Younger students often struggle with these subjects the most. Often these subjects are hard to grasp for students because they are difficult and the application of these skills appear to be far fetched and pointless. A student who is passionate about visual art may be discouraged when challenged by science, technology, engineering and math (STEM) related subjects. Since children may believe that STEM subjects do not apply to their own interests they may not be as interested in learning about them in school. By providing these students a more engaging and interactive system they can learn from and apply this knowledge to other disciplines as well. For example by giving a student who is artistically inclined positive exposure to STEM subjects they may be inclined to design their own graphic design software plugins for their own use in the future.
Methods
Learning Styles
Personality vs. Learning Styles: The Big Five personality traits have been found in many studies and experiments to be linked to different types of learning styles. Conscientiousness is exemplified by being disciplined, organized, and achievement-oriented. Neuroticism refers to degree of emotional stability, impulse control, and anxiety. Extraversion is displayed through a higher degree of sociability, assertiveness, and talkativeness. Openness is reflected in a strong intellectual curiosity and a preference for novelty and variety. Finally, agreeableness refers to being helpful, cooperative, and sympathetic towards others. For example, students who prefer and thrive in a more structured learning setting and intuitive processing are prone to anxiety and worry, whereas those preferring an activist and pragmatist style are more extraverted. Personality traits also have been proven to influence academic achievement. For instance, conscientiousness has consistently emerged as a stable predictor of exam performance and GPA. Combinations of Big Five traits have also been found to predict various educational outcomes. Namely, conscientiousness and openness predict course performance, and agreeableness, conscientiousness, and openness predict overall academic performance. With these analyses from the program, each student will be given a profile that shows what kind of learning methods they excel in, and what type of teaching methods should then be implemented upon these students to achieve their best academic performance. However, it's not enough that our program can simply analyze these personality traits and learning styles - our program must also know what necessary steps to take in order to enhance on these students' academic journeys, as well. That is where the research on teaching styles and learning styles come into play.
Teaching styles and Learning Styles: To be able to obtain tutoring services on STEM courses with an online program may seem unreliable or strange, but there have been more and more studies done that shoe that learners participating in personalized learning environments are more motivated and more willing to spend time in the education context than other students. If we are able to make the learning experience more interesting and interactive for students, especially with subjects that many struggle with in the classroom, then there is no doubt that they would do better exponentially and be able to absorb the material at a higher rate of understanding than if they just sat at their desks and tried to memorize everything. A research study on the adoption of different teaching styles shows that students preferred it when their teachers, instead of being an information-giver or an evaluator, took on a more passive role similar to that of a coach or a contributor to their own work. Our program aims to provide that support for the children to figure out problems by themselves using various different methods (hands-on, videos, audios, etc.), rather than just feed them information and expect them to process it. Upon analysis of each student, our program will then begin to teach the student based on the learning style that suits the student most - for example, students who are proven to be more visually inclined to learn will begin to study based off different kinds of visual exercises and activities, and students who are proven to work better in groups will begin to study in virtual study groups where they can help others as well as receive help themselves.
Virtual Reality and Learning
VR is described as “the use of computer modelling and simulation that enables a person to interact with an artificial three-dimensional (3-D) visual or other sensory environment” (Lowood).
There are different variations of VR, including Augmented Reality which imposes 3-D objects onto the existing environment being perceived. However, the focus of our project will be on immersive virtual reality. Immersive VR is what most people commonly think of when picturing VR. The user is given a headset and sensors which are tethered to a computer that runs the software. Different headsets require slightly different set-ups but the general principles are the same. Sensor tracking allows the user’s movements and actions to be tracked in physical space. This information is fead back through the software which creates the perception of a virtual world. In the case of our product, we will be using the Google Daydream headset. The Daydream utilizes the smartphone's gyroscope to orient itself in space.
VR has been around for a quite a long time. In 1929 Edward Link created the Link Trainer which was one of the first flight simulators ever created. At the time, the Link Trainer was a revolutionary device. Now, it is extremely clunky and mechanical compared to modern virtual reality flight simulators. As technology has rapidly improved so has the quality of virtual reality systems. Both the HTC Vive and the Oculus Rift are some of the most widely known top of the line commercial VR headsets you can buy today. Over the span of VR development, its application to learning has been a fairly obvious path to focus on. Today, emerging VR software has been claimed to “offer lively simulation that cannot be done in the real world; concretize abstract ideas and concepts; extend the sense of presence; enable active interactions with the content; and hence enhance knowledge acquisition and transfer” (Xu & Ke, 2016).
Of specific focus for us, is how VR can help student's learn STEM courses. In her book 'Virtual Reality and Education: Overview Across Different Disciplines', Nicoletta Melida Sala provides a great overview of some of the recent research put forth into education and VR. Sala describes a group of researchers at a university in Switzerland that used VR to “help architecture students to visualize in 3D, since this is arguably the most difficult part of understanding architecture” (Sala, 2016). The underlying concept here is that visually representing problems can make it easier for people not only to solve the problem, but to identify the problem in the first place.
In one study out of Florida University, researchers created a VR-based game-like learning environment (VRGLE) to teach math fractional knowledge to fifth-grade students. They found that “the game-control and interaction interface was a salient aspect of usability” and that a rewarding mechanism enhanced the playability of the VRGLE (Xu & Ke, 2016). In this instance, implementing mathematical problem solving into a narrative helped participant’s engage with the material they were trying to learn. In this example, participants were lead to believe they were trapped on an island and that one way to escape would be to create an S.O.S signal using fireworks. The crafting of these fireworks was the task that embedded fractional problems that the participant’s had to work through to achieve the goal. The researchers noted that it was very important that the virtual reality experience was easy to navigate because “if a learning game was not easy to use, a learner would not contribute enough attention and cognitive resources to the embedded content knowledge (Xu & Ke, 2016)
Other research regarding math education and virtual reality has shown that, when attempting to teach mathematical concepts in virtual reality, you can not teach the same mathematics as in real life. “The mathematics needs to be more engaging, more applicative, and more visual” (Patterson, Patterson, & Robertson, 2016). You can not just transfer exactly what students learn in regular classrooms; it has to be specifically catered to VR. Creating an educational VR experience is hard. In another study, VR was used to teach high school children basic chemistry concepts such as electron shells. Children could visually see a water molecule and interact with its structure. Children in the VR setting were tested on the material and compared to students in a control group that involved just watching a video. The results of this study did not shown VR to be superior to other methods of instruction which is important to recognize because there is a lot of hype around virtual reality at the moment and in reality it is no where near a perfect teaching tool. However, this experiment was done in 1996 so virtual reality has come a long way since then (Byrne 1996). Increasingly, VR is allowing us to progressively embody ourselves in virtual worlds.
It seems like to have a user’s attention fully engaged in a virtual environment there are a few key ingredients. In 2005, researchers identified a few problems connected to virtual reality including low resolution of inexpensive viewing devices which made unrealistic objects, difficulties maintaining high frame-rates on personal computers and the high costs of hardware and software devices to realize immersive VR (Sala & Sala, 2005). Technology has improved significantly since then but these fundamental issues are still being grappled with today in VR software development.
There is a reason we are using VR for our interactive system instead of a regular computer program experienced on a desktop monitor with a mouse and keyboard. This is due to the problem of extraneous load. Extraneous load is defined as mental workload generated by cognitive activities that are not directly related to the learning goal (Bujak 2013). If a student is trying to complete a set of math activities in a computer program, using a new graphical user interface (GUI), mouse or keyboard may inhibit the learner. Learning how the GUI works by navigating a set of menus with the mouse is not the intended learning goal. When young students use VR, they are using what they already know about interacting with the physical world. They can manipulate objects with their hands, look around using their head and neck, and bring objects closer to their heads in order to change scale (Bujak et al., 2013). By using a VR system and naturalized GUI we are reducing the extraneous load on the user, and allowing them to focus their attention towards their learning goals.
User Experience
Visual experiences in VR can be broken down into two main categories, interfaces and environments. Interfaces refer to menus where the user is interacting with buttons or icons similarly to how we would interact with icons on our phones. Environments refer to an immersive experience such as viewing the solar system as if you were in a spaceship or interacting with simulated chemical solutions as if you were in a laboratory.
The activities and games in this application will vary from subject to subject. As a way to more effectively replace the conventional textbook users can use VR models with accompanying descriptions. For example students studying the brain can use a nervous system model in VR. Each part of the brain can be singled out and examined by the user with an accompanying description. The description will come in the form best suited for the user (i.e. auditory, visual or both). Standalone applications exist modelling certain systems in VR but the advantage of this app is that it will adapt to the learning style of the user. By virtually interacting with a system like the nervous system, we give the user an interactive and engaging learning experience compared to using a textbook to try to achieve the same level of understanding.
An example of a more immersive environment would be an island escape example. The user is transported to a desert island where their goal is to escape. In order to escape they must perform a variety of tasks to signal for help and gather supplies. While VR has not been able to accurately replace learning in the real world, it offers a practical and cost-effective way to come close.
Adaptive Learning
Adaptive learning is a kind of educational method in which technology is used to adapt learning content according to information about a student, such as their performance, knowledge, skills, and learning style. Systems that make use of this method are called adaptive learning systems. Within such systems, the learning environment can change in a number of ways, including the way it presents concepts to a learner, the sequence in which it order concepts or tasks, and the in the difficulty of content it presents (Bilic, 2015).
The structure of an adaptive learning system typically consists of five models, originally proposed by Richard Smallwood:
- A domain model which is comprised of the all the concepts for the learner to cover
- A learner model often containing what the learner currently knows among the concepts within the domain model
- An assessment model containing the methods or items for assessing what the learner knows or understands for each concept. The model may include, for example, problems or test questions for the learner to solve/answer
- A transition model which decides the learner's next steps after learning a concept
- A pedagogical model that specifies the course of instruction or means by which the learner may master a concept (Essa, 2016)
Each of these models encode information needed to carry out the system's operations. Information about the user is collected in order to construct a learner model, which the system can use (alongside the other models) to adapt and administer learning content. The assessment model contains items that are used to assess what the user knows. After assessing the user, the system can update the learner model, and use its transition and pedagogical models to determine what the user can or should do next given what they know and other information, such as their learning style.
A learner model can be constructed in different ways, and store different information. One way an adaptive system can construct a learner model is by first gathering profile data about a student, which form the basic foundation of the model (Vagale & Niedrite, 2012). Initial assessments may be used to gather profile information, and can include information about a student's learning style (Vagale & Niedrite, 2012). Using this profile information, different types of learner models can be constructed. As noted earlier, learner models often keep track of a learner's knowledge with respect to a domain, including whether a given concept in the domain is known or unknown (Weber, 2012). Such models are known as overlay models. Other models use information about the learner to create a stereotype that can be used to adapt the content in a way that suits the learner's attributes. These models, appropriately called stereotype models, often contain information about the learner's learning style, skills, and level of mastery of a subject (Weber, 2012). A learner model that combines elements of overlay and stereotype models is also possible (Vagale & Niedrite, 2012).
Our software will have a domain model consisting of the concepts for the user to learn in a given subject. For our software's Math program(s), it will include, for example, topics such as basic division, multiplication, and adding fractions. A learner model will be established by first obtaining a profile of the user, by means of initial tests assessing their personality, preferred learning style(s), and knowledge with respect to the domain (i.e. how much they already know). Information from the user's profile will then be used to construct a hybrid overlay/stereotype learner model, that will be updated as the user interacts with system.
The program's assessment model will contain test items or problems that will be used to evaluate the user's progress in learning concepts. These items will not necessarily be presented to the user in the form of actual tests, however. Instead, assessment items will be interwoven into interactive activities or games. For example, one activity could be a game in which the student must bake a cake – a task that involves using knowledge of fractions. The assessment items could then be a series of fraction problems for the student to tackle within the game.
Through information stored in the learner model, as well as the other models outlined above, an adaptive system can employ a number of methods in order to change the learning environment. As the learner progresses, some adaptive systems may use the method known as adaptive problem solving support to analyze the learner as they solve problems, tracking their performance in order to update the learner model, and give them appropriate feedback (Weber, 2012). Other methods of adaptation include adaptive presentation and curriculum sequencing. Using adaptive presentation, the system can change the manner in which content is presented to a learner using information from the learner model, in order to target their specific learning style and knowledge level (Weber, 2012). With this method the system can, for example, present a learner with content containing more elaborate explanations if the learner is a beginner, or with more pictures if they are a visual learner (Weber, 2012). Through curriculum sequencing, the system can also specify the sequence in which learning content is administered to the learner, according to their level of mastery. The sequence may be predetermined through information already gathered about a learner from initial profiling, or it may be generated on an ongoing basis as the student interacts with the learning environment (Weber, 2012).
Our program will make use of both the adaptive presentation and curriculum sequencing methods. Its pedagogical model will outline how each concept may be learned according to knowledge level and learning style. Then, using information about a user contained in the learner model, the system will administer suitable activities to the user. For example, if the user is more of a verbal learner, then the system will give the user activities that emphasize words, or involve more reading. As the user progresses, the system will assess the user's knowledge and using its transition model, it will decide what the user might learn next, given what they know so far. In this way, the curriculum will also be sequenced in a way that corresponds to the user's knowledge and level of mastery.
Discussion
We chose the method of catering the program differently to each student because we feel that in the classroom, it is impossible for one teacher to be able to teach each student one-on-one according to their own learning needs, and while secondary or college students can better grasp their own weaknesses and strengths when it comes to understanding material, it is difficult for younger elementary school students to realize why they don't understanding a specific question or subject. Rather than have them struggle for the years to come, our program can help aid them to create a strong foundation for many important courses that they have to keep building upon in the future years of school. Using this program, the students are able to delve into the subject(s) they struggle with and learn the material in new and interesting ways that aren't offered in the classroom, making them interested in learning instead of feeling like they're being forced to. This program also looks to help students broaden their fields of knowledge towards math and science at a young age, even if they do not pursue fields of study related to those subjects in the future, so that they can feel more confident and open towards trying out different courses in their secondary and post-secondary careers.
Effectiveness of Adaptive Learning Systems
Adaptive learning systems appear to be a promising way to help deal with the issues found in traditional educational systems. By creating a personalized educational environment that takes the needs of individual students into account, adaptive learning systems can enable students to learn in a way that targets their specific learning style, skills, and level of understanding.
Some studies have been done to examine the efficacy of adaptive learning methods in improving learning outcomes. One experiment by Chung and Chen (2015) looked at the effects of using adaptive assessment on student performance in 9th grade students. The experiment had students use a multimedia learning system, and randomly assigned them to two groups: an experimental group which received an adaptive initial assessment and material that was adapted to match their level, and a control group which received a regular initial assessment and non-adapted learning material. The study had both groups take a post-test after using the learning system with the assigned conditions. The experimental group showed significant improvement on the post-test compared to their initial assessment, suggesting that adaptive learning systems improve learning outcomes (Chung & Chen, 2015).
Virtual Reality Hardware
While VR offers incredible experiences to it's users, that experience comes at a high cost. In order for our educational service to be available to a wider range of users, we will utilize the VR system Google Daydream. Daydream is Google's second VR product with it's predecessor being Google cardboard. The reduced cost of the Daydream in comparison to other VR platforms is because of its use of the computing power in your smartphone instead of a expensive desktop computer and other electronics in a standalone headset like the HTC Vive or Oculus rift. While it still comes at a price, the relatively low cost of the Daydream itself combined with using your smartphone offers a more optimal price for required hardware. By keeping the relatively cost low without sacrificing functionality and comfort we can offer a product that is effective and accessible to a large group of the student population.
We chose Google Daydream over other VR platforms for a few reasons. The first being that Google Daydream is an affordable VR platform that offers more quality and options than other affordable VR platforms. Compared to its predecessor Google Cardboard, the Daydream is slightly more expensive, but includes more features applicable to our educational product. The Daydream is made of a foam and fabric material that lets the user have a comfortable experience for a longer period of time. It also has a head-strap which allows the user's hands to be free, instead of holding the VR platform up to their face like with the Google Cardboard. In addition, the Google Daydream also utilizes a motion controller which allows our users to navigate menus and interact with games and activities. The inclusion of a controller allows the user to remain immersed in the VR experience without having to take off the headset in order to navigate the menus.
The features that the Google Daydream offers are important to making our product more effective. The users of our system will be engaged in these VR interfaces and environments for extended periods of time.
As of January 2017 the Google Daydream is available for all third-party developers like us to design a product that utilizes it.
A potential downside of this choice of VR is that it is only compatible with nine different types of android smartphones. The major downside being that IOS devices are not compatible with this system. This limits the current amount of users that could use our software immediately. Google however has announced by the end of 2017 two more android phones will be compatible with the daydream with the promise of more being included in the future. However this is still more accessible and practical than requiring our users to purchase their own standalone VR headset and accompanying high powered PC.
Software Design
We will be designing our program using the Unity engine. Unity has native support for the Google Daydream. Many of the VR experiments we have discussed in class have also been designed using the Unity engine. Unity also has multi-platform support which could be applied to other devices in the future if Google Daydream also supported them. This choice of software gives us an effective basis to develop and potentially expand our program in the future.
Evaluating the performance of our software
We can find out if our program is meeting our short term goals in a few ways. Evaluating the short term success will be relatively simple. One short term goal would be the user's enjoyment of using the system. If the user finds the system uninteresting we are not succeeding in creating an engaging platform. We expect the user to be interested and engaged with the activity the are completing.
Since this product is designed to act as a supplement to a students learning in school we can also measure the effectiveness of our product by observing if the user's performance in school has improved. This could be measured not only by grades received on assignments, but also overall interest and engagement with school subjects. If either of these things are happening, our product would be having a positive effect on the user's education experience.
Measuring the long term effects of our software will be harder to pinpoint. As described earlier we intend to provide our users with a strong foundation of skills in the STEM subjects to either pursue those subjects or apply their knowledge to other disciplines. In the future if our product was used by a large enough subject pool we could observe the effectiveness of our program by observing what our users have done. This would include academic performance, university education, occupation or personal projects the individuals have created on their own.
Conclusion
Learning Outcome
In this project we have explored the educational benefits of adaptive learning and VR. VR provides solutions to problems faced by users of educational software on regular computer systems. By using VR we are bypassing the problems of extraneous load, and proposing a natural and engaging environment for children to use. At the same time, adaptive learning systems offer some advantages in areas where traditional learning environments tend to fall short. In particular, adaptive learning systems have the ability to deliver learning content that suits students' individual needs, goals, and attributes – factors which traditional systems typically end up ignoring. By providing a curriculum that's tailored to a student, adaptive learning systems appear to be a way for students to reach their educational goals both efficiently and effectively.
We have also explored into the research of the correlation between learning styles and certain personality traits, as well as how different forms of teaching styles can affect academic performance by allowing the students to take charge in their own work. By focusing deeply on these topics, we are able to not only learn more ourselves, but also be confident that the program we have designed will have a positive effect on these students' schoolwork, providing them with a stronger foundation for STEM courses for their future endeavours. While the computer science aspect of our project is very significant, we also have a strong psychology aspect as well that provides as a backbone for the entire project - simply having the VR show cool animations for students won't work unless we are confident that the program will be able to make accurate analyses for each student and provide accurate methods of teaching these students the courses and allowing them to explore and learn in each of their own unique ways.
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