Current, Similar, Subtask (CSS) and Criticality, Difficulty, Frequency (CDF) Models
Current, Similar, Subtask (CSS) and Criticality, Difficulty, Frequency (CDF) ModelsEdit
Decision trees are a common instrument used to aid in decision making when selecting tasks and subtasks. Although they can be used for learning at all levels, they are probably better suited for tasks and subtasks that are in the lower domains. They are also used when an instrument is needed that is fast and easy to use.
Decision trees allow the user to follow a predetermined path by answering a series of questions based on established principles or rules. Options and risks are designed into the tool so all the user has to do is answer the questions until a decision is reached. Decision trees are not normally modified by the end-user.
|Decision Tree - Advantages|
|Easy to use|
|Fast to complete|
|Decision tree - Disadvantages|
|Can only be used to evaluate selected, specific criteria|
|The rating scales only provide limited descrimination|
|Not easily adaped to special circumstances / Not flexible|
For this discussion, we will review two different decision trees:
CSS Model for Training Content SelectionEdit
This decision tree is often the first tool used when evaluating Task Analysis data. It is used to evaluate the target audience's previous experiences and determine if they are already capable of perfoming the same or simialar tasks. An important tenet in training adults is to avoid teaching somebody something they already know how to do. This decision tree is used to determine the trainee's experiences by asking a series of questions that require a yes or no answer.
- Does the target audience currently perform the complete task?
- Has the target audience performed a similar task in the past?
- Have all the subtasks been evaluated?
DCF Model for Training Content SelectionEdit
Once it is determined that the trainee doesn't already know how to perform the task, the DCF Model is used to ask a series of questions to evaluate the difficulty, criticality and frequency for each of the tasks. The DCF Model uses a four tier decision tree to evaluate three main criteria: Difficulty Level, Criticality, and Frequency of Performance
- Tier 1 evaluates the Difficulty Level. The task Difficulty Level is rated as being either High, Average, or Low
- Tier 2 evaluates the Criticality. The task Criticality can be either Critical (Yes) or Not Critical (No)
- Tier 3 evaluates the Frequency of Performance. When evaluating Frequency, you rate it as High, Average, or Low
- Tier 4 is the decision level. There are three choices: to Train (T), Not train (NT), or Over Train (OT)
CSS Job AidEdit
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