I layer – Define and analyze the real-life problem to choose the appropriate task sequence
3. Sequence the classes' conditions
In this step, you will decide how to sequence the conditions. The main idea is to give students a chance to train in the skill hierarchy in different conditions. The conditions for the program's complexity consist of two layers — the complexity of the context (from easy to complex/real) and the degree of involvement of the hierarchy of skills / algorithm of problem solution (minimal, partial, complete). Then, students will be able to learn how to adjust skill hierarchy to condition and will be able to adjust skill hierarchy to the new conditions that they will meet after the program. Ideally, the program should start with conditions manageable for the initial level of your students and end with a condition that you strive to prepare students for.
Here, we have different combinations of context complexity and the degree of involvement of the hierarchy of skills / algorithm of problem solution for different sizes of the target problem situation:
- For small problem situations, a "whole-task" sequence is used — the entire hierarchy of skills is involved from the first simulations in all contexts.
- For medium problem situations, whole-part or part-whole sequences are used. Their specificity is that the student does not immediately face simulations involving the entire hierarchy of skills. However, at a certain point in the program, students face a simulation where they need to use the entire hierarchy of skills.
- For large problem situations where the student cannot use the entire hierarchy of skills in the program, it is proposed to divide the situation into "substitutions". And then, for each situation, build a program according to the above-mentioned sequences.
If your real-life problem is small
If your real-life problem is small, you can choose the easy, medium, and complex conditions based on your context complexity factors and create for each condition several problem simulations in the following steps. This sequencing is called " whole-task."
In such sequencing, students could work with the whole skill hierarchy from the beginning.
In such sequencing, students could work with the whole skill hierarchy from the beginning.
If your real-life problem is big
Suppose your target problem situation is too big. That is, students will not be able to cope with it at the very beginning of the program, even in the simplest context; then, you can simplify it by focusing on part of the skill hierarchy/solution algorithm. You will need to solve part of the problem for the students and ask them to finish the rest. Simplification occurs because students do not need to apply all the skill hierarchy; instead, they analyze how someone performed the part of the skill hierarchy for them and apply only another part of the skill hierarchy. Gradually, you will do less for students so they can apply more skills by themselves. This sequence is called the "backward chaining", as the student learns to apply the skills / algorithm of the solution from the end, gradually completing the task.
It is important that you understand that students will be able to solve the whole problem after some preparation. If the problem is so big that its solution takes a very long time in reality, then this way is not ours — we follow the third way of fragmentation into subproblems. In whole-part and part-whole sequences, the student still faces simulations where the entire hierarchy of skills needs to be involved. They face such holistic simulations not at the beginning of the program, as in the whole-task sequence, but in the middle or at the end.
It is important that you understand that students will be able to solve the whole problem after some preparation. If the problem is so big that its solution takes a very long time in reality, then this way is not ours — we follow the third way of fragmentation into subproblems. In whole-part and part-whole sequences, the student still faces simulations where the entire hierarchy of skills needs to be involved. They face such holistic simulations not at the beginning of the program, as in the whole-task sequence, but in the middle or at the end.
Next, I will describe two ways to mix the complexity levels of context and the degree of involvement of the hierarchy of skills/algorithm of the solution (minimal, partial, complete) to implement a whole-part or part-whole sequence to the program.
The first way is a "whole-part" sequence.
The key idea of this sequence is to start by training a small subset of skills and end by training a complete hierarchy of skills within each context (easy, medium, difficult). So, there will be two layers of complication of the program conditions (the upper layer is the complexity of the context, and the lower layer is the degree of involvement of the hierarchy of skills / solution algorithm).
First, we choose the complexity of the context: easy, medium and difficult. This will be your top-level layer to complicate the program conditions. Then, within each context (easy, medium, difficult), we add a lower layer based on the subgroup of skills that will be involved in the series of simulations:
The key idea of this sequence is to start by training a small subset of skills and end by training a complete hierarchy of skills within each context (easy, medium, difficult). So, there will be two layers of complication of the program conditions (the upper layer is the complexity of the context, and the lower layer is the degree of involvement of the hierarchy of skills / solution algorithm).
First, we choose the complexity of the context: easy, medium and difficult. This will be your top-level layer to complicate the program conditions. Then, within each context (easy, medium, difficult), we add a lower layer based on the subgroup of skills that will be involved in the series of simulations:
— minimal inclusion of a hierarchy of skills / algorithm for solving the problem (steps A and B are done for the student; the student needs to complete step C) for the first series of tasks within the selected context, for example, an easy one. That is, most of the problem have already been solved for the students; they practice only at the final stages of problem-solving (a small part of the entire hierarchy of skills is involved). Ideally, students should be familiar with how the first steps were taken for them as a case study.
— partial inclusion of the hierarchy of skills / algorithm for solving the problem (step A is done for the student, the student needs to complete steps B and C) for the second series of tasks within the selected context, for example, easy. That is, a smaller part of the problem has already been solved for the students; students practice only a few steps of problem-solving (most of the entire hierarchy of skills). Again, ideally, students should be familiar with how the first steps were taken as an example.
— the full inclusion of the hierarchy of skills / algorithm for solving the problem (the student does everything — A, B and C) for the third series of tasks within the selected context, for example, easy. That is, students work out all the stages of solving the problem (the entire hierarchy of skills).
We repeat the same thing for medium and complex contexts. Thus, within each context (easy, medium, complex), you have a division into a series of tasks with minimal, partial and full use of the skill system / algorithm for solving the problem. In this case, students engage the entire hierarchy of skills at the end of each context.
— partial inclusion of the hierarchy of skills / algorithm for solving the problem (step A is done for the student, the student needs to complete steps B and C) for the second series of tasks within the selected context, for example, easy. That is, a smaller part of the problem has already been solved for the students; students practice only a few steps of problem-solving (most of the entire hierarchy of skills). Again, ideally, students should be familiar with how the first steps were taken as an example.
— the full inclusion of the hierarchy of skills / algorithm for solving the problem (the student does everything — A, B and C) for the third series of tasks within the selected context, for example, easy. That is, students work out all the stages of solving the problem (the entire hierarchy of skills).
We repeat the same thing for medium and complex contexts. Thus, within each context (easy, medium, complex), you have a division into a series of tasks with minimal, partial and full use of the skill system / algorithm for solving the problem. In this case, students engage the entire hierarchy of skills at the end of each context.
The second way is a "part-whole" sequence. As in the previous case, you need to choose the complexity of the context (easy, medium and difficult) and the degree of involvement of the skill hierarchy (minimal, partial and full use of the skill hierarchy/problem-solving algorithm). The difference with the previous version is that there is an upper-level layer.
In other words, within each high-level condition (minimal, partial, and complete use of the skill hierarchy), students go through a series of problem simulations with easy, medium, and difficult contexts. In this case, the complete amount of skills are trained only at the end of the program, but still, students have the opportunity to train the whole problem and the complete skill hierarchy in an easy, medium, and difficult context.
In other words, within each high-level condition (minimal, partial, and complete use of the skill hierarchy), students go through a series of problem simulations with easy, medium, and difficult contexts. In this case, the complete amount of skills are trained only at the end of the program, but still, students have the opportunity to train the whole problem and the complete skill hierarchy in an easy, medium, and difficult context.
Dividing problem to subproblems. The previous two variants are the ideal compromises where the students do not train the whole skill hierarchy in every simulation from the early beginning of the program; Still, they have an opportunity to do it sooner or later during the program. However, in some cases, the real problem is so big that students will be overwhelmed if we ask them to solve it sooner or later during the program. In this case, you will probably need to split your problem into several subproblems. The best way will probably be to ask the practitioner whether his / her job represents a hierarchy of problems. Do not forget to strive for the ideal — to allow students to practice the hierarchy of skills, that is, to solve the whole problem as much as possible.
No matter what variant you choose, you will need to create so many simulations! That is why I suggest you start with the easiest condition. First, focus on it, and finish all four components for the first condition. You will still need it at the start of the program; then, you will gradually increase the number of conditions and simulations.
What we strive for:
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Fidelity
Task conditions represent the smooth transition from working in a safe simulated task environment, via task environments with increasingly higher fidelity, to real-life practice. The first class represents the easiest condition that students could manage based on their initial level. The last class represents the goal condition, that is, the condition for which the program aims to prepare students.
The algorithm for 4C/ID model from my experience
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I — Define and analyze the real-life problem to choose the appropriate task sequence.
1. Define the real-life problem's border and size
2. Define the problem's complexity conditions
3. Sequence the classes' conditions
1. Define the real-life problem's border and size
2. Define the problem's complexity conditions
3. Sequence the classes' conditions
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II layer — Design, develop, and prototype one task class.
4. Create learning tasks for one class
5. Improve the skill hierarchy and design the other three components for one class
6. Design the performance assessment for one class
7. Plan the development and prototyping of one task class
4. Create learning tasks for one class
5. Improve the skill hierarchy and design the other three components for one class
6. Design the performance assessment for one class
7. Plan the development and prototyping of one task class
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III — Improve the four components for one class.
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