learnability matters

Decision trees or branching stories are alternate paths a learner takes as a direct consequence of decision made. The outcome is different based on the choices you make. Therefore, if you attempt the exercise more than once selecting the other choices, the outcomes may be different each time. Several educational simulations use decision trees to impart skills. A few examples are Account Challenge Sales Simulation, EAP lifescape, aids awareness, and so on.

When to use decision trees?

Decision trees are used when you want learners to understand the consequence of decision-making. The design is typically at Bloom level 3 (application). Therefore, decision trees are used when you want learners to apply their skills. Decision trees can be simple or complex.

For the following examples, I will be using the following key.

Simple decision tree:

Type 1

In the above decision tree, there are two options: correct and incorrect. If the learner clicks the incorrect option, he/she gets to witness the immediate consequence, which may not be the desirable outcome. The learner is checked at this step. When the learner selects the correct option, he/she is taken to the next scenario. But if the learner selects the incorrect option, feedback is shared immediately and the rationale for the correct answer is provided. Therefore, the learner gets to understand the impact of his decision through this feedback. He/she is then taken back to the next scenario. Again, two options are provided and the cycle continues until the outcome is reached. In this decision tree, the final outcome will always be success as the learner is checked at each step on the way. Therefore, there is no room for failure.

Type 2

As in the first type, this case also has two options: one correct and one incorrect. In this decision tree, choosing an incorrect answer results in failure (game over logic). You either start from the beginning every time you choose an incorrect answer or begin from the previous decision point (that is before the learner chose the incorrect answer. You learn from your mistakes and proceed further. The motivation to get it right will be high here as the learner will have to start all over if he or she fails. The learner has to select the right option all the time to ensure success.

Complex Decision Tree:

Type 3

In this decision tree, there are three options and three outcomes. The three options are correct, incorrect, and partially correct. As usual, correct is the most appropriate answer and incorrect is the least appropriate answer. Partially correct is the correct answer but falls short of being the most appropriate answer. Based on how you define it, this option may or may not have negative consequences. The three likely outcomes are success, failure, and pass. Pass indicates the learner has just made it. He/she has not taken the most appropriate decisions on the course. As you can see, even for an incorrect option in the first branch, the learner is given a second chance. You may give learner a chance to rectify his/her error.

Type 4

In this decision tree, different combination of correct and incorrect are used to test the learner. There is a 50% chance of either success or failure. As you can see, clicking on an incorrect option, means sudden death or failure. You do not get a second chance.

These are only a few examples of decision tree formats. You can use a combination of different options and outcomes while designing a decision tree. Let me share two decision trees logic with you.

Example 1

Node 1, 2, and so on are the branches in which the scenarios are introduced. A, B, C and D are options from which the learner has to select one. A is the correct option. On selecting this, the learner is taken to Node 2. B, C, and D are incorrect options. On clicking these, feedback is displayed in terms of a negative consequence. The learner has to restart the exercise from Node 1. This was a very short decision trees. Therefore, for every incorrect option, the learner had to begin from the first node.

Example 2

Node 1, 2, and so on are the branches. The scenario/question is included in this section. A, B, C are the three options. A is the correct option, B is the partially correct answer, and C is the incorrect answer. We used points as a reward feature. We also introduced an expert who pops up to communicate incorrect feedback. Therefore, the expert was used as a punishment feature. If you notice, we have not used negative marking as we firmly believe that this is demotivating to watch yourself loose the hard earned points.

When the learner clicks A, he/she is awarded the maximum points and the learner is taken to the next scenario. The logic being that there were no hiccups so the learner can proceed smoothly. When the learner clicks C, the points do not change and the expert pops up to warn the learner of the consequences of his/her actions. The expert shares the most appropriate action. The learner is taken to the next node/branch. If the learner chooses option B, the consequence of this decision can be a resultant serious issue or inconsequential issue. If it is not a serious issue, the learner is awarded 20 points and sent to node 2. If it is a serious issue, the leaner is given a chance to rectify the error. He/she is given two options: one right option and one incorrect option. The right answer earns him 10 points and he is then sent to node 2. The wrong answer earns him 0 points and the expert pops up to give him a warning message and feedback. The learner can score maximum points if they are able to get it right all the time. This would be a flawless performance.

Decision trees require a firm grasp of the content. It works on the ‘If-else’ principle. When designing a decision tree, keep the following in mind:

• Always remember to tie up the loose ends.
• Ensure that you have several review rounds to ensure that all the loops are flowing well and there is no repetition.
• Ensure that the logic used is understood by the learner as this will act as a motivation factor. If the learner doesn’t know why the points increased or decreased, the points feature will be useless.
• Keep it simple.
• Keep short and sweet. This will help the learner remember the steps he/she has carried out in the exercise.
• Create decision points based on learning objectives. If it not an important task, don’t make the learner answer it.
• Feedback should be tangible. The learner should be able to view the consequences of his/her actions. If you are not displaying feedback in the form of text, ensure that feedback is tangible (example: points).
• Restrict the number of options to 3 or 4. This will not confuse the learner and makes life easier for the designer and programmer as well.
• Ensure that the options are distinct from each other.

Designing a decision tree requires several discussions and reiterations. You may want to put it down in paper first as it can be quite confusing. Decision trees can be used as a powerful teaching technique for learning by doing courses or application level courses. This makes learning exciting and fun as it puts the learner in control. It makes the learner think about the decisions to make and also shows the consequence of an incorrect decision immediately. Decision tree is an effective tool that helps learner learn through his/her own mistakes.

Use them wisely!

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