Perceptions and New Realities for the 21st Century Learner

Perceptions and New Realities for the 21st Century Learner

Jennifer (Jenny) L. Penland (Shepherd University, USA) and Kennard Laviers (Sul Ross State University, USA)
DOI: 10.4018/978-1-7998-1461-0.ch005

Abstract

Of all the technologies emerging today, augmented reality (AR) stands to be one of, if not the, most transformational in the way we teach our students across the spectrum of age groups and subject matter. The authors propose “best practices” that allow the educator to use AR as a tool that will not only teach the processes of a skill but will also encourage students to use AR as a motivational tool that allows them to discover, explore, and perform work beyond what is capable with this revolutionary device. Finally, the authors provide and explore the artificial intelligence (AI) processors behind the technologies driving down cost while driving up the quality of AR and how this new field of computer science is transforming all facets of society and may end up changing pedagogy more profoundly than anything before it.
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Introduction

Mixed Reality (MR) the cousin to Virtual Reality (VR), is starting to gain a foothold in today's technological ecosystem. In (Penland, Laviers, Bassham and Nnochiri 2018), the use of Virtual Reality for distance learning was demonstrated on a small scale however VR while being more immersive, does not integrate with the user's environment and therefore makes it difficult to teach students with a tangible example of the subject matter. Mixed Reality (MR) is used as an independent concept or to classify the spectrum of reality technologies, as referenced in reality virtuality continuum 1994; 2007). As an independent concept, MR combines the best of both virtual reality and augmented reality. When used to classify the larger scope of reality technologies, it refers to the coverage of all possible variations and compositions of real and virtual objects.

This type of connectivity has now reached the pinnacle were technology has emerged both the quality and cost to a practical level. While this is fantastic, allowing someone to engage in a task totally unfamiliar to them such as, rebuilding a carburetor, as a pedagogical medium we propose a note of caution and suggest prior instruction with this as a continued practice strategy. If the AR or Mixed Reality can take them step-by-step through a process, we can make an argument that the students will not find it insignificant to remember or learn the process because they don't have to, the AR will do it for them (Callaghan, Gardner & Davies, 2008). In this chapter, we will explore various ways for AR to be used as a pedagogical tool and propose methods to avoid letting the student side-step the learning process.

Over time, it is likely that only a few adaptive learning software packages will prevail. Hopefully, software vendors not controlled by very large universities or companies will choose to share how their algorithms work. We have learned enough about how people learn to know that not everyone learns the same way. Beyond the seven learning styles (visual, aural, verbal, physical, logical, social, and solitary) with which many educators are familiar, modern technologies are enabling researchers to determine there may be more. In fact, one recent book by David Schwartz, Jessica Tsang, and Kristen Blair (2016), “The ABCs of How We Learn”, identifies 26 unique learning styles. As datasets of learners’ activities increase and algorithms improve their abilities to discern different styles, this higher number will likely increase.

Sophisticated software increases the potential to tease out the most effective way to help each person learn. The weakness of today’s educational system is that we often teach to the average, excluding learners on the upper and lower edges with a Bell Curve focus (Herrnstein & Murray, 1994). A learner who conforms survives, while non-conformers do not. As colleges, universities, and corporations develop and refine stronger adaptive learning algorithms, I hope they avoid the bias toward conformity.

As we embrace adaptive learning software, we have to make sure that we choose learning algorithms that work to the learners’ strengths instead of forcing them to adapt to a norm. In the end, we lose if we are all coached to think alike. One of the surest signs that a technology trigger is starting its roller-coaster ride through the Gartner’s “Hype cycle of innovation” is when the name we all call that trigger becomes a part of the public lexicon (2014).

Key Terms in this Chapter

Artificial Intelligence Processors: Are the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Graphics Processing Unit: Is on the computer's video card or cards that contain an array of up to many thousands of low-resolution processor cores able to perform many simple tasks simultaneously.

Hype Cycle of Innovation: The digitalization of education is gaining momentum. An increasing number of choices face the CIO, and the “Hype Cycle for Education, 2014” offers a concrete example of a “CIO toolbox” of crucial tools for the next five years and beyond.

Simulated Neuron: Is a computer algorithm that simulates a neuron. The brain is made up of many neurons, an estimated 250 billion in the human mind. Each neuron is connected to a bunch of neurons going into it and it has a set of outputs leading to other neurons. These connections are called Axons. A neuron is normally either off or on and when a threshold is met the neuron will either fire a signal, turning on, or it will turn off. The key element is the threshold that must be met to activate. This is what is changes when we learn how to do something and is what we simulate in our machine learning algorithms via what is known as a sigmoid function. The function has a weight value associated with it that goes up and down until it gets to the correct level that matches the input training data. Normally we send data from our input into the neuron and see if it fires on the output (forward propagation). But, when we are in a training session, the information flows from the expected output backwards (back propagation) through the networks and the weights are adjusted.

Machine Learning: Is the process of presenting the computer program with a large set of training data where the data consists of a set of variables and what the outcome was for that data. An example would be pixel values for an image of a person’s face, the outcome (or classifier) would be a unique number that represents who that was. So, the input array would have the pixels and the expected outcome that the output neurons should show is the unique identifier of the person. The machine learning algorithm would have many sets of input data for each item (like a face) it is supposed to learn.

Mixed Reality: Mixed Reality (MR) is used as an independent concept or to classify the spectrum of reality technologies, as referenced in the reality–virtuality continuum. As an independent concept, mixed reality combines the best of both virtual reality and augmented reality.

Pedagogical Medium: Is something that relates to teaching. An example of a pedagogical tool is a smartboard for instructional delivery.

Sift Features: Are features of an image that a computer algorithm can easily identify and use to track spot locations from multiple directions, distance and lighting.

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