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Signal Detectors

yidanedencao edited this page Jun 12, 2024 · 17 revisions

Comparison Signal Detector

Concept

The comparison signal detector function compares the performance level of a feedback recipient for a specfic measure against the level of a benchmark, such as the peer performance benchmarks or the goal value for that particular measure. This function evaluates differently for both types of gaps, both positive and negative, relative to the performance level and the list of comparator values that are pre-defined by the pipeline. When the recipient's performance level is above that of a comparator, the comparison _detect method denotes that there exists a positive gap in performance level, which is a kind of motivating information. When this detector is activated, the positive or negative gap motivating information is used to influence the rank of corresponding precision feedback message templates.

Example

Below are examples of performance data that leads to the detection of a comparison signal.

Month Performance Level Peer Average 75th Percentile Benchmark 90th Percentile Benchmark MPOG Goal
Oct 85% 85 88 92 90
Nov 85% 85 88 92 90
Dec 91% 85 88 92 90
  • For the month of December, this performance data will generate a positive gap signal relative to the 50th and 75th peer performance percentile benchmarks, as well as a negative gap signal between the performance level and the 90th percentile benchmark.

Implementation

Trend Signal Detector

Concept

The trend motivating information detector function compares the performance level of a feedback recipient for a specfic measure as it changes over time. The trend detector currently evaluates over a three month window, and detects monotonic and non-monotonic performance trend information. The signal detects when the performance level has a positive or negative slope over time, and extracts the magnitude of teh trend as motivating information to be used as a moderator.

Example

Below are examples of performance data that leads to the detection of a trend signal.

Month Performance Level Peer Average 75th Percentile Benchmark 90th Percentile Benchmark MPOG Goal
Oct 85% 85 88 92 90
Nov 85% 85 88 92 90
Dec 91% 85 88 92 90
  • For the month of December, this performance data will generate a positive trend signal. The magnitude of the trend will be evaluated as 3, the formula below denotes how this magnitude is determined:
    • trend magnitude = (month t0 - month t2)/2
    • trend magnitude = (91 - 85)/2 = 3

Implementation

The method operates with the current formula: Month t0 performance - Month t2 performance / 2

Causal pathway models Message templates Prioritization algorithms

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