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Ultimate guide to design student behavior for Instructional Designers

Matteo Rinaldi
|
September 29, 2020

Learning is the final outcome of a well crafted course design. However, students learn in many different ways. When creating a course and laying down its foundations, there are many things that instructional designers (IDs) and instructors have to keep in mind.

One of the most important, perhaps, is how to design a course that can elicit the best behaviours - learning-wise - of students. Many behavioural researchers dedicated their life to the topic of learning processes and how students can be successfully engaged.

This article outlines the most important factors worth considering when designing online courses, in order to initiate students' positive learning behaviors.

A practical, step-by-step guide on how to design online university course, with the best practices for both teachers and instructional designers when creating an online course design, together with the latest tools.
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Bloom’s Taxonomy of learning

Image Source: Vanderbilt University Center for Teaching

Bloom’s taxonomy of learning, first created in 1956 and then revised in 2001, is a pivotal framework to categorize educational goals. In this article, we will refer to the different levels an instructional designer can refer to for each of the points listed below.

At the basis of this taxonomy lays the knowledge of 4 different types:

• Factual Knowledge: knowledge of terminology and elements

• Conceptual Knowledge: classification, theories, models, and principles

• Procedural Knowledge: knowledge of specific skills and when it is appropriate to use them

• Metacognitive Knowledge: self-reflection, debugging, knowledge about one's own knowledge and skills

Each of the following paragraphs are linked to one of these kinds of knowledge and one of the cognitive processes in the Bloom’s taxonomy pyramid.

Knowing, considering, and evaluating pre-instructional levels

University courses are renowned to be highly structured, and perhaps even too rigid. Over the last decade, a great deal of debate and discussion - more or less enlightening as well as confusing or too simplifying - have tackled the very core of college-like education, even arriving to ask if universities are as useful as we have always thought them to be.

It is indeed complex and extremely challenging to allow flexibility into courses. However, online courses - either being implemented in a blended format or a full-online one - might just do the trick. The inner characteristics of the digital world might take into account all of those individual differences that are the foundation of students’ critical and original thinking. After all, how can we expect them to grow if we do not account for their specific strengths?

Research states that, to best facilitate learning behavior, the learner should be provided with instructional procedures and environmental conditions that are able to stimulate them [1].

A way to do this is to understand the learners' characteristics. the characteristics of the learners. More specifically, when creating a course, there are three pre-instructional variables that should be considered:

• What the learner already knows: the student may have some knowledge about the topic at hand, coming from informal learning or popular culture, especially when it comes to socio-scientific or humanistic based courses. How to use this: a brief survey at the beginning of the course or a few weeks before might give some insights into students’ minds, particularly what they might consider more interesting, motivating, or more difficult about the subject.

• What the learner acquired as prerequisites for the actual learning objectives: such knowledge or skills may come from looking at the previous courses or lectures attended by the student. A sweep through the curriculum or syllabus of the last semester could be of great help.How to use this: it could be useful to design a course with content relating to the knowledge obtained from in other courses. This would not only instill a sense of progression and a return-of-investment kind of feeling within the students, but  also boost their explorative behaviour (as explained in the next sections).

• Which antecedent skills or knowledge the learner has that could facilitate or interfere with new learning: both of the pre-instructional levels described above can be helpful or detrimental to new learning. Some incorrect but fossilized knowledge or understanding of  sense  over some factual information or previous experience with a tool, software, or resource might hinder learning, especially if the instructor is having a quite different approach to it. How to use this: as mentioned above, a survey could go a long way. On the other hand, the course might include a brief session during the first lecture to introduce    the fundamental resources and the softwares, debunking the existing misconceptions for a smoother start.

In this sense, it is crucial to understand learners’ skills and knowledge, in order to adjust the learning environment to pre-instructional behavior capabilities [1]

Understanding what the entering behavior [1] of a learner is (e.g. learnt responses to instructions, prerequisites for learning those responses, and antecedent learning) can be a really subtle task. However, once these “entry levels” are known, a course can be modified accordingly by using specific tools or their features.

For example, an Interactive Video or Document might be employed by asking questions and underlining different parts of the text  with different learners in mind, which could activate thus activating students’ pre-knowledge about parts of courses taken in the past - students might have learnt a specific skill or acquired certain knowledge, but they also may have forgot it or become a little “rusty”: re-activating those knowledge and skills is the best way to form a connection between the old and the new content.

For example, quiz them on a part of the video that would push them out of their comfort zone, making them reflect on the section of a paper that will deepen their knowledge or prepare them for the successive material. By having live data of your student performance, you will be more than able to understand if their previous knowledge is “still there”, while making sure everyone gets to the same level of understanding when students come from different backgrounds.

All of this will be extremely beneficial when it comes to carrying out the instructional process itself, where you will want to set up new forms of students’ behavior and add new stimuli in the future.

Understanding pre-instructional levels has to do with all of the knowledge levels laid out by Bloom’s taxonomy. Understanding what a learner knows will tackle their metacognitive abilities, together with their factual, conceptual, and procedural knowledge.

Progression in the instructional process

When the entering level of the students is known and the objectives of the course have been decided, it is time to implement the instructional process.

The emphasis, here, is indeed on the word “process”. As such, students are expected to learn in a sequence of rising complexity and number of topics. For example, if a student is trying to learn multiple regression methods, but they do not have knowledge about what a correlation is, they must first be taught this.

The instructional process can be divided in three main parts, with the first two of special importance when designing a course:

• Setting up new behaviors: for example, by teaching a new skill or new reasoning patterns (e.g. a new code function, a new theory, a new statistical model, a new critical framework).
The aim here is to increase precision of students’ responses. At first, it is normal to tolerate more crude and general responses and reasoning, but more rigorous standards and criteria must be taken into account to bring students towards mastery. A way to achieve this is to contract performance tolerances, by bringing standards towards more stringent measures and tolerance levels, in order to achieve mastery. As the saying goes, “Do not do it until you do it right, but until you do not do it wrong anymore”.

• Setting up new stimulus control: adding new ways to check how and if students are learning, by adding new exercises with new difficulty levels, while trying to connect new knowledge to previous one. For example, students might be taught how to use previous learned skills in response to new stimuli [1]

• Maintaining certain behaviors: a stage to maintain certain skills in students. However, if the first two steps are well implemented, previous knowledge will be integrated in the new skills learned automatically.

Indeed, research has pointed out that the content is secondary to the process through which knowledge is obtained [1].

One interesting approach to enhance the learning process is via Activation of prior knowledge.

This is a useful technique in teaching when new knowledge is associated with the previous one that the student already knows and that has had many opportunities to use and deploy. If, for example, a student is well versed in the political situation of a country, it will be easier for them to learn a content analysis collection design about media outlets coming from that country.

Activation of prior knowledge and progression in the instructional process recall the evaluating section of Bloom’s taxonomy, where the student has to defend, argue, and critique his own standards as well as getting evaluated by the instructors. It is crucial to give space for such an evaluation by using tools such as Peer Review, Group Member Evaluation, or other ways to evaluate interactively.

Learning from errors

Students will make mistakes. And they are allowed to. Indeed, without mistakes there would not be opportunity for growth since only those who are working on something - especially something new - make mistakes.

However, it is important to guide students to learn from their mistakes. There are two ways that can be implemented and depends on the type of knowledge that students are trying to learn.

• Formal knowledge: formal knowledge is usually the most rigid version of knowledge. What is the formula for the gravitational attraction? When did the French revolution happen? There are no “almost-right” or debatable answers to these questions. This kind of static knowledge is usually brought in the form of multiple-choice items, which research has shown to be a situation where students would be more prone to repeat their error in the future [1]. To avoid this, it is good practice to have explicit feedback which directly informs students of their errors.  

In an online setting, this could be achieved by inserting feedback messages whenever an answer is selected to explain why the choice is right or wrong. The Interactive Document does exactly this by enabling teacher-student discussion over specific sections of the paper. Such continuous feedback processes could provide input for students to the multiple-choice questions.

• Thematic knowledge: thematic knowledge is the knowledge that is not as rigid as the formal one, and is well exemplified by open questions, presentations, or oral examinations. There is not only one way to express this knowledge: the discourse could take any leads or point of views, although it still needs to be coherent with the questions posed as well as in itself. To make students more aware of errors in these instances, it is good practice to use a design that allows more corrective feedback. In this sense, the error reveals where the student’s reasoning is lacking and allows for a better correction and future learning.

Also in this case, Bloom’s taxonomy tells us we are operating inside a cognitive level of evaluation. Elaborating on their errors, students engage in self-assessment via metacognitive processes and instructional designers should always think about setting up a space throughout the LMS and the course when they can do that.

References

[1] Glaser, Robert, & Reynolds, James H. (1964). Instructional Objectives and Programmed Instruction: A Case Study. Defining Educational Objectives. (Edited by C. M. Lindvall.) Pittsburgh: University of Pittsburgh Press, 1964. pp. 47-76. Source

[2] Suppes, P., & Ginsberg, R. (1960). APPLICATION OF A STIMULUS SAMPLING MODEL TO CHILDREN'S CONCEIT FORMATION OF BINARY NUMBERS.

[3] Siegler, R. S. (1998). Children’s thinking (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.

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