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This educational video, uploaded by Alana Snow, gives a great overview of Behaviorism. It ventures into some of the main theorists and their respective theories- Ivan Pavlov and classical conditioning, John Watson and the Little Albert experiment (which would definitely NOT be considered ethical today) and B.F. Skinner and operant conditioning. These theorist and their experiments helped develop the behaviorist learning theory.

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Behaviorism: Pavlov, Watson, and Skinner

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The "Cognitivism Learning Theory" YouTube video gives an overview of Cognitivism, which developed as a reaction to behaviorist and goes on to describe some of the differences between cognitivist and behaviorist. The video features some of the fundamental concepts of cognitivism like knowledge being symbolic mental constructs or schemata and that learning is the processing of organizing information into conceptualized models.

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The "What is Constructivism" YouTube video gives a great overview of what constructivism is and how everyone's individual experiences make their learning unique to them. I enjoyed the way this video because it also highlights the importance of understanding constructivism as an educator- each student will bring their own unique experiences into the learning environment. The video also highlights 8 specific elements and principles that shape how the theory works.

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For the overview of Connectivism I choose to do a reading instead of a video. I thought this reading gave a great overview of the history of Connectivism, the explanations of "Nodes" and "Links" as well as highlighting the 8 principles of Connectivism. This reading also talks about Connectivism within the classroom (think things like social media, gamification, and simulations).

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"Knowledge has many authors, knowledge has many facets, it looks different to each person, and it changes moment to moment. A piece of knowledge isn't a description of something, it is a way of relating to something." - Stephen Downes
It's the digital age. In a world with Google Assistant, Siri, Alexa, and other digital information assistants, people have come to rely on technology to seek answers and find information. It's no different for today's students. Twenty years ago, students might go to an encyclopedia for answers; now they can simply ask their smartphones or type the question into Google.

It's clear that technology is changing how students learn in and out of the classroom. Rather than learning from teachers and textbooks, smartphones and laptops serve as hubs of information for today's students. In fact, according to a 2015 study, 87% of college students reported that they used a laptop every week for schoolwork, while 64% reported using their smartphone for schoolwork.

The increasing use of technology as an educational tool has changed the learning landscape. With it came gaps in traditional ideas of teaching and the need for new methods to keep up. The theory of connectivism seeks to be the modern-day solution to those gaps. Whether you're already a teacher or aspire to be one, understanding this theory can give you additional tools and strategies to create a learning environment that sets your students up for success. This guide will help you dive deeper into the connectivism learning theory and provide tips on how to implement it in your own classroom.

Connectivism is a relatively new learning theory that suggests students should combine thoughts, theories, and general information in a useful manner. It accepts that technology is a major part of the learning process and that our constant connectedness gives us opportunities to make choices about our learning. It also promotes group collaboration and discussion, allowing for different viewpoints and perspectives when it comes to decision-making, problem-solving, and making sense of information. Connectivism promotes learning that happens outside of an individual, such as through social media, online networks, blogs, or information databases.
History of Connectivism Learning Theory

Connectivism was first introduced in 2005 by two theorists, George Siemens and Stephen Downes. Siemens' article Connectivism: Learning as a Network Creation was published online in 2004 and Downes' article An Introduction to Connective Knowledge was published the following year.

The publications address the important role technology plays in the learning process and how the digital age has increased the speed at which students have access to information. Since then, both Siemens and Downes have continued to write and speak on the subject. However, each has slightly different viewpoints. While Siemens tends to focus on the social aspects of connectivism, Downes focuses on non-human appliances and machine-based learning.

According to connectivism, learning is more than our own internal construction of knowledge. Rather, what we can reach in our external networks is also considered to be learning. From this theory, two terms-nodes and links-have been commonly used to describe how we gain and connect information in a network.

In connectivism, students are seen as "nodes" in a network. A node refers to any object that can be connected to another object, like a book, webpage, person, etc. Connectivism is based on the theory that we learn when we make connections, or "links," between various "nodes" of information, and we continue to make and maintain connections to form knowledge.

Connectivism builds on already-established theories to propose that technology is changing what, how, and where we learn. In their research, Siemens and Downes identified eight principles of connectivism.

Those main principles of connectivism are:

Learning and knowledge rests in the diversity of opinions.

Learning is a process of connecting.

Learning may reside in non-human appliances.

Learning is more critical than knowing.

Nurturing and maintaining connections are needed for continual learning.

The ability to see connections between fields, ideas, and concepts is a core skill.

Accurate, up-to-date knowledge is the aim of all connectivist learning.

Decision-making is a learning process. What we know today might change tomorrow. While there's a right answer now, it might be wrong tomorrow due to the constantly changing information climate.

Before these principles came on the scene, many theories positioned students solely as receivers of information. However, connectivism supports the theory that knowledge is distributed across networks where connections and connectedness inform learning.

It's one thing to understand what connectivism is and another to actually incorporate it in the classroom in learning activities. Remember that in a connectivist viewpoint, the new learning responsibilities shift from the teacher to the learner. Unlike traditional teaching methods and other theories like constructivism or cognitivism, the educator's job is to guide students to become effective agents for their own learning and personal development. In other words, it's up to the learner to create their own learning experience, engage in decision making, and enhance their learning networks.

Connectivism relies heavily on technology, so the first step to creating a connectivist classroom is to introduce more opportunities for digital learning-like online courses, webinars, social networks, and blogs.

Here are more ways to incorporate connectivism in the classroom:

One way teachers implement connectivism is through the use of classroom social media. For example, a class Twitter account can be used to share information, engage in discussion or announce homework tasks. This can help boost class engagement and open the lines of discussion among students and teachers.

Gamification takes assignments and activities and puts them into a competitive game to make learning more of an interactive experience. There are many learning-based apps and instructional technologies teachers can use to add an element of gamification to the classroom. One example is DuoLingo, an online learning tool that helps students learn languages through fun, game-like lessons. Teachers can track students' progress while students can earn "points" for progressing through lessons. Other examples include apps like Brainscape, Virtual Reality House, and Gimkit, just to name a few.

Simulations engage students in deep learning that empowers understanding as opposed to surface learning that only requires memorization. They also add interest and fun to a classroom setting. Take, for example, a physics class where students create an electric circuit with an online program. Instead of being instructed via a book or classroom lecture, they're learning about physics by simulating an actual physical setup.

Incorporating some or all of these examples is a great way to allow your students more control over the pacing and content of their learning. It also provides opportunities for individualized learning to match each student's unique needs and strengths.

Both the student and the educator can benefit from connectivism in the classroom. If you're considering adopting this theory in your current or future classroom, consider the following benefits:

Within connectivism, learning occurs when peers are connected and share opinions, viewpoints, and ideas through a collaborative process. Connectivism allows a community of people to legitimize what they're doing, so knowledge can be spread more quickly through multiple communities.

Connectivism shifts the learning responsibilities from the teacher to the student. It's up to the learner to create their own learning experience. The role of the educator then becomes to "create learning ecologies, shape communities, and release learners into the environment" (Siemens, 2003).

Connectivism supports individual perspectives and the diversity of opinions, theoretically providing for no hierarchy in the value of knowledge.

If you're a current educator or aspiring to be one, it's important to understand how different learning theories can benefit your classroom and help your students find success. Get more tools and knowledge about teaching and education with a degree from WGU.

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"Knowledge has many authors, knowledge has many facets, it looks different to each person, and it changes moment to moment. A piece of knowledge isn't a description of something, it is a way of relating to something." - Stephen Downes It's the digital age.

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This article written by Alexander Salas for The Learning Guild was written a few years ago now, but I thought the emphasis on how microlearning is actually "cognitive overload reduction" was very interesting! I also like that it highlighted that microlearning isn't always the solution but can be really effective when paired with formal instruction.

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Microlearning: What It Is Not and What It Should Be : Learning Solutions | The Learning Guild

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If you are a learning professional, it’s almost impossible not to have recently heard about the wonders of microlearning. Apart from being a genius marketi…

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This article by Sharon Boller begins as defense of gamification based on a Twitter exchange that happened as a result of a study. The article then goes on to speak about how to use gamification effectively in learning and highlights 5 tips for bringing gamification into learning- one tip that really stood out to me was "Reward players for performance, not completion" which I believe really speaks to the behaviorist learning theory!

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I'm a big fan of Twitter. I use it to curate content and pay attention to people whose opinions I care about and business trends. I believe in hashtags (#GBL, #gamification, #UX, #ATD) because they help me easily curate content.

Last week, on the #ATD "back channel" (conversation that gets created when you diligently use hashtags to share info on a particular topic, person, or event), the topic of learning myths was hot and heavy, largely due to Clark Quinn's newly-released (and excellent) book release on the same topic. Although gamification is not in Clark's book, gamification came up on the #ATD back channel as part of a discussion on learning myths; people were suddenly questioning whether it's a myth.

Whoa! As someone who has been immersed in the arena of learning games and the gamification of learning, I was understandably concerned by this line of conversation.

A Gamification Myth

During a recent Twitter conversation, it emerged that some of the "myth" comments about gamification stemmed from a single study that one astute Twitterer, Shannon Tipton, called "full of holes as Swiss cheese." The study used a proprietary platform to conduct a controlled, randomized comparison of two lessons that teach students how to divide fractions. The study authors did not provide any information about the two lessons being compared except for this:

the lessons were supposed to teach students how to divide fractions

one was called "basketball divide fractions" lesson and the other was simply called "divide fractions."

The study suggests (but does not directly say) that one lesson was gamified in some fashion, but we aren't told how. The other lesson was not gamified. The results overwhelmingly favored the non-gamified lesson. Kids learned faster and performed better on subsequent fractions tests that required them to divide fractions. The gamified approach resulted in lower test scores, even though kids spent longer in the lesson by choice. The authors indicate this increased amount of time spent on the lesson was 100 percent voluntary. The assumption the study's authors seem to want readers to have is that students had more fun doing one lesson than the other.

From that single study, which omits tons of relevant information, some folks conclude that gamification of instruction doesn't work.

What's Missing From This Study?

Let's think about what we don't know:

Quality of the game/gamification design. How many game elements did the designers use? Were they used optimally? Was the gamified solution too complex, forcing students to expend too much brain power to figure out rules and too little to learn how to factor fractions? Or maybe the gamified elements were motivating the wrong behavior (rewarding progress versus mastery)? In scenarios that focus on rewarding progress, learners earn points simply by completing a problem; getting the problem correct is not a criteria for earning a reward. Learners also could have been rewarded for speed, which would push them to guess the right solution rather than truly make an effort to solve for the right answer.

Equivalency of instructional design quality across both solutions. Were both lessons, in fact, teaching identical principles and using some of the same techniques? Did they each have solid instructional design? For example, did they both employ worked examples, which reduces cognitive load and increases learning efficiency?

What the study illustrated was the basketball dividing fractions was less effective in helping students perform well on a fractions test than the lesson that did not include the gamified elements used in the basketball lesson. If I were a game designer on that lesson, I would want to go back to it and figure out what I'd done wrong in gamifying the lesson. I would not be ready to assume that gamification itself was the problem.

How to Effectively Use Games and Gamification

Bad gamification or game design is going to yield bad results, just as bad instructional design is going to lead to poor results. That doesn't mean gamification or games don't work.

So how do you use games and gamification effectively? Here are some principles that will help you maximize efficacy.

Keep game complexity simple, particularly when you are using a game to support relatively short lessons. Do not over-complicate a learning game or gamified lesson with lots of game mechanics (aka rules) or game elements. Elements are things such as rewards, scoring, chance, strategy, resources, cooperation, competition, aesthetics, theme, and story. Doing so increases the cognitive load on your learner and makes it harder-not easier-to learn. (Check out this explanation of cognitive load theory, which was first identified by John Sweller in 1988.)

Reward players for performance, not completion. If you are going to award points within the game, those points need to come from demonstrating knowledge or skill, not just progress. (For more on this, see Karl Kapp's book, Gamification of Learning and Instruction. There's an entire chapter on research studies and another on how to use rewards effectively, and what not to do.)

Be cautious with leaderboards. Leaderboards can be fun, but be sure you focus on more than who is on top. Consider letting people see more about themselves rather than just what other players are doing. We are all very interested in ourselves, so we like to see our rank relative to others, improvements we've made over time, personal bests, and so forth. We also like being part of a team. So consider team-based comparisons, such as by location or role, as opposed to just head-to-head individual comparisons.

Align the game element choices you use to the learner's actual job context, as much as possible. In other words, avoid competition in a game if the job requires cooperation and collaboration. If you want to incorporate "chance" into a game (and this is a handy game element for balancing out gameplay), make sure you use it appropriately. This means matching the types of chance a player encounters in the game with the way chance occurs in the real world.

For example, if you are teaching project management, a great use of chance is to have it come up as a factor that requires the player to consider alternate strategies. Some options: "One of your teammates just called in sick. Identify two other viable strategies for meeting today's deadline." or "Your client just called to say she's going to miss her deadline. She asks, 'How can we still hit the ultimate milestone. I don't want the project to slip overall.' What do you do?" Bottom line: make the in-game goal align with the learning goal in a reasonable way that makes sense for the learners who will play your game or complete your gamified lesson. Play to Learn, the book I co-authored with Karl Kapp, outlines how to combine learning design and game design together to maximize the impact and efficacy of games.

Also, be sure to mirror job context. If the real-world scenario is to achieve certain quality ratings for a medical facility, for example, mirror that goal within the game. This reduces cognitive load on your learners, because they don't have to distinguish what's true in the game from what's true in their job context. ( A blog post on playtesting features a game where we worked hard via multiple iterations to get our game goal and rewards just right to maximize learning.)

Stop thinking you have to make the game super fun. In fact, fun doesn't really matter if the focus is on optimizing the learning outcomes. What matters is relevance and a game design that reinforces and enhances the instructional design. Granted, a game needs to be "fun enough" to keep your player/learner involved in that game. You also need to re-think what fun is. Kevin Werbach of the Wharton School of Business does a great job of breaking down what people actually find fun, and it includes things such as strategizing, problem solving, and collaborating-all skills required in many jobs.
Editor's Note: This post is adapted from the Lessons on Learning Blog.

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gamification of learning and instruction

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Does Gamification Actually Work? Yes, and Here's Why | ATD

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I'm a big fan of Twitter. I use it to curate content and pay attention to people whose opinions I care about and business trends. I believe in hashtags (#GBL, #gamification, #UX, #ATD) because they help me easily curate content.