Behaviorism In Instructional Design For eLearning: When And How To Use
What eLearning Professionals Should Know About Behaviorism In Instructional Design For eLearning
Although we may declare supporters of cognitivism or constructivism, we are all behaviorists by nature. Even if we may not realize it, as eLearning professionals, we all use behavioristic principles extensively in our instructional design for eLearning. This happens because behaviorism, no matter how much out-of-date it sounds, without any doubt, is deeply rooted in human subconscious. Instinctively, it’s part of our actions and reactions. From an educational point of view, who doesn’t want to examine learners’ observable and measurable behavior and guide them accordingly to optimize their learning? What are tests and assessment in general, other than an effort to estimate learners’ performance and adapt the instruction accordingly to the learning needs of the audience? These principles also apply in eLearning.
No. In this article, I won’t talk about Pavlov’s dogs, Skinner’s rats and pigeons, nor about the Thorndike’s Law of Effect. There is extensive bibliography about all these. Instead, I will give you examples of elements we all use today in instructional design for eLearning, without even realizing that they are based on behavioristic principles, as well as to explain for what type of learning objectives a behavioristic approach may yield better results.
Applying Behaviorism In Instructional Design For eLearning
Right-wrong, on-off, 0-1, etc. In fact, the connection of behaviorism and eLearning was clear from the very beginning. If we take a look back in the 1950s and 60s, when Computer-Assisted Instruction (CAI) first appeared, computer seemed to be the ideal tool to measure learning outcomes, on the condition of course, that there was a single correct answer learners were expected to give. Soon this evolved to an attempt for Programmed Instruction with the computer programmed to give pre-determined paths to follow, that is allowing learners to proceed only when the correct answer was given. Today programmed instruction can be perceived as a forerunner of interactive branching scenarios and adaptive hypermedia systems applied in online education, with behavioristic elements, the resemblance of which with programmed-instruction is very difficult to be ignored, despite the fact that we tend to “cover” them in modern “constructivistic” packages claiming that we give learners “freedom of choice”. The truth is that the only freedom learners are allowed is with respect to the order they choose to follow the predefined learning paths, so carefully tracked and programmed in the instructional design of really good eLearning courses.
Back to behaviorism, as the name implies, a behavioristic approach focuses on guiding learners reach pre-established learning outcomes. Learning is considered to take place when learners manage to reach these expected outcomes designed to meet the learning objectives of the eLearning course. Therefore, the aim of a behavioristic-oriented instructional design for eLearning must be to provide learners with the appropriate stimuli, that is with opportunities that help them demonstrate that they are able to express desired behaviors that prove that learning has actually taken place.
An instructional design for eLearning based on behavioristic approach starts from the basic assumption of behaviorism that knowledge is objective, meaning that there is only one correct answer to give or a specific approach to follow, respectively. Although this may sound rigid, and perhaps it is, this is where to start in order for eLearning professionals to get awareness of the type of activities that a behavioristic approach could be appropriate for. It also shows, that behaviorism may not be the appropriate approach for eLearning activities that require the user to develop higher-order skills, such decision-making or problem-solving through analysis, synthesis or evaluation of the information presented.
Objectivism is the key to remember in order to decide whether a behavioristic approach is appropriate for your eLearning activities or not. Is there a single correct answer or multiple approaches may be acceptable. Is knowledge objective? Don’t rush to answer no. Facts and standardized procedures actually are examples of “objective” knowledge. They do not change. Take advantage of this and check the learning objectives of the eLearning course. This is where you need to start.
Techniques To Be Used For The Instructional Design Of Behavioristic eLearning Activities
Once determined that a behaviorist approach is suitable to meet particular learning objectives of your eLearning course, you need to design the respective eLearning activities accordingly. The techniques you may use are the following:
Use discrimination whenever the learning objective requires learners to identify whether a concept belongs to a specific category or not. In order to do so, learners should be able to identify key characteristics and qualities of the category, and judge whether the new information shares the same qualities to belong to the group or not. Drag-and-drop exercises to classify concepts into different categories may serve as examples of developing discrimination activities in terms of eLearning course design.
Generalization is suitable whenever the learning objectives are such, that learners after identifying the attributes of an item belonging to one category are expected to assign the same attributes to all items within the category. Teaching through examples is based on an inductive approach of presenting eLearning content, during which learners after observing a series of independent online examples should be able to identify their common characteristic(s) and generalize by formulating the rule. Under this perspective, generalization is very close to what today we perceive as active learning, with an obvious direct connection to constructivism.
Although in strictly behavioristic terms, association is the typical example of conditioning, that is linking a specific stimulus to a specific response, there is a tendency in today’s eLearning to create eLearning interactions based on association whenever the learning objectives require the new information presented to be linked to specific practical applications of it. Presenting information within context can help learners create associations. This means that whenever learners encounter the same or similar information, they already know what it’s related to, as they have already built a basic frame of reference to associate this piece of information with. Although a behaviorist approach perceives this association as an automated drill task and not as a cognitive process, applied in instructional design for eLearning, matching exercises are examples of eLearning activities that could be designed to facilitate learners’ process of making associations.
Chaining is referring to learners automatic performance on specific procedures with pre-determined steps to be followed. Like a chain, one step leads learners to the next. Performing drill tasks is an example of elearning objectives that can be mastered through chaining. Creating drill tasks in eLearning involves presenting the theoretical model first and then asking learners to repeat the procedure by actually repeating the steps involved in the same order these were presented. Through repetition and online practice, learners at the end are able to reach the desired outcome by following the steps exactly as presented in the model. Sequence ordering exercises through drag-and-drop are very frequently used in quiz making templates most eLearning authoring tools provide. Such exercises may serve as typical examples of drill tasks applied in eLearning course design. In such activities, there is always a pre-defined and unique correct sequence that learners must form in order to show that they have mastered the learning objective under consideration. It’s very common for learners to reach the desired outcome through trial-and-error, another behavioristic technique quite applicable in eLearning, depending on the number of attempts allowed by the instructional designer. It sounds like Thorndike’s Law of Effect, doesn’t it? Learners do not stop pressing different levers, that is exploring alternative answers, until they find the rewarding one!
Keep in mind that for all of the above techniques, there is just a single correct answer. All quiz creation eLearning authoring tools take advantage of these behavioristic-based alternative exercise formats, as in order for the program to give an automated response and test learners performance through their test scores, that is measurable outcomes, there should be a unique correct response for each question.
Reinforcing Desired And Weakening Undesired Behavior
What is the instructor’s role in this process then? An instructional design for eLearning based on a behavioristic approach sets the type of reactions to be received by learners, after interacting with the online training material. These reactions come from an online instructor or corporate trainer in synchronous eLearning settings, or from the eLearning course itself in the case of asynchronous eLearning. Depending on the type of reaction received, learners expressed behavior may be reinforced or weakened. This highlights the importance of feedback throughout the entire learning process, not only at the end of the eLearning course but each time a learner interacts with the system. In behavioristic terms, although nobody wants to call it like this today, feedback is the simplest form of conditioning. Correct attempts are most frequently being reinforced by positive comments written on automated programmed responses. Although negative reinforcement, that is punishment and negative criticism is not quite acceptable today, and certainly not appropriate for adult learning, such behavioristic traces can still be found in some cases that negative scores are used.
An instructional design for eLearning based on the behavioristic approach would therefore imply that learning takes place when unwanted behavior is extinguished and learners reach the point of giving only the desired uniquely correct responses that express the expected learning outcomes that guarantee that the learning objectives of the eLearning course have been mastered.
Application Of Behavioristic Principles In Gamification And Game-Based eLearning
Last, but not least, nowadays, behavioristic principles are also still applicable in gamification, that is presenting the learning material employing game design elements in an entertaining way in order to motivate and engage the audience throughout the learning process. In gamification, as well as in other types of eLearning activities, reinforcement of a certain desired behavior can occur in two ways: either by eliciting from learners particular learning outcomes and rewarding these outcomes by assigning points, grades, budges, higher position in leaderboards, etc, or by removing from learners specific benefits, for example points, lives, etc, in order to make them try to avoid undesired consequences of their behaviors.
Behaviorism in Instructional Design for eLearning: Concluding Remarks
It is true that behaviorism has received too much critique during the last decades, mainly to the fact that it does not take into account other aspects of learning such as the mental processes involved or the environment in which learning takes place. The aim of this article was not to show any favoritism towards behaviorism, but rather to show that, in certain cases, it’s still a valuable approach in today’s eLearning. Of course, we cannot ignore cognitivism and constructivism as alternative approaches. Today, we know that a behavioristic perspective of certain activities in instructional design, does not mean that other approaches may not be used simultaneously for the same online course for other types of eLearning activities. Rather than following a single approach for the entire eLearning course, we should, therefore, select the most appropriate for each one of the learning objectives to be covered.
Although the focus of behaviorism may not be to examine what is going on in learners’ minds during the learning process, this does not necessarily mean that it denies cognition. It simply does not examines its. Humans are social beings; we learn from the environment, we learn from each other. We evolve all the time. We are rational beings. We are clever. If we are given the right incentives, we are able to construct knowledge in learning that meets our personal interests and needs.
As instructional designers, all the emphasis should be placed on the learning objectives as each one of them may require for eLearning activities that follow a different approach. Overall, the result in most cases is a mixed approach that combines the best of behaviorism, cognitivism and constructivism, that meets the needs of each learning objective to be mastered and offers the audience the best possible eLearning experience.
Want to learn more about adult learning? The article 9 Tips To Apply Adult Learning Theory To eLearning features 5 adult learning theory assumptions to integrate into your eLearning course design, in order to achieve maximum engagement and motivation for your audience.