Traditional approaches to progress monitoring were built on research indicating that at least 12, but sometimes more, data points are needed prior to drawing conclusions about the effects of an intervention. Research by Christ and Desjardins (2018) indicates that it’s possible to predict how a student’s performance will progress after collecting only six data points.

**Limitations of Traditional Methods**

Teachers have always been able to use existing data to predict the future trend of a student’s progress during a specific intervention, though it has traditionally required at least 12 data points in order to do so. Most published progress monitoring tools use a method known as the Ordinary Least Squares Regression (OLSR). Prior research indicates that this method requires at least 12 data points to predict future scores. A significant limitation of this method is the amount of time necessary to collect sufficient data. For example, if at least 12 data points are needed and progress monitoring data is collected weekly, it will take at least 12 weeks to have enough data points to predict an accurate trend. If progress is monitored every other week (i.e., bi-weekly) then 24 weeks would be needed. This means that more than a quarter of the school year is needed before the effects of an intervention will be known.

**Conditional Probability**

To address the time constraints present in traditional progress monitoring trend calculations, Christ and Desjardins (2018) documented that a different method for calculating the trend of a student’s progress data is possible. This alternative uses conditional probability to estimate future outcomes and is based on the work of statistical pioneer Thomas Bayes. Using Bayesian methods, a reliable trend can be identified with as few as six data points collected either weekly or bi-weekly (i.e., every other week). This method allows teachers to interpret student data earlier in the school year and change interventions more often, if needed.

Conditional probability involves using calculations of prior data trends from large numbers of students monitored with the same measure as a way of predicting what data trend is likely for an individual student. By using prior data from large numbers of other students to predict future performance, the amount of time needed to calculate a trend line is shortened.

**FAST Projection Line™ **

Illuminate Education’s* FastBridge *is the first publisher to incorporate Bayesian analysis into its progress monitoring tools. In *FastBridge* progress monitoring reports, the Bayesian analysis is referred to as the FAST Projection Line™. Due to the necessity of a sufficient amount of prior data in order to calculate predicted scores, the FAST Projection Line™ is available for the most commonly-used progress monitoring measures, including:

- CBMmath Automaticity
- CBMreading English™
- CBMreading Spanish™
- earlyMath™
- Decomposing DC-1
- Numeral Identification NI-1
- earlyReading English™
- Decodable Words
- Letter Names
- Letter Sounds
- Nonsense Words
- Onset Sounds
- Word Segmenting
- Sight Words 150

**Progress Monitoring Reports with FAST Projection Line™ **

Below is an example of a Progress Monitoring Report with the FAST Projection Line™ shown. In this example, there are six data points. The student’s goal is shown with a dashed line, and the student’s trend based on existing scores is shown with a solid blue line. The FAST Projection Line™ is shown as a dotted line that extends after the date of the last data point. In this case, the solid blue line suggests that the student is not on track to reach the goal, but the FAST Projection Line™ indicates that the student will reach the goal. Using the FAST Projection Line™ the teacher and team decide to maintain the current intervention and continue to review the data to confirm that the student remains on track to reach the goal.

**Disabling and Enabling the FAST Projection Line™ **

As with other features of the Progress Monitoring Report, the FAST Projection Line™ can be disabled. This is done by clicking on the words FAST Projection Line™ in the legend below the graph. Here is the same graph with the FAST Projection Line™ turned off.

The FAST Projection Line™ is turned on by default for all progress graphs where it is available. If it is turned off, it can be turned back on by clicking on the same text in the legend.