Validation of an algorithm for dynamically diagnosing learning progress and innovation performance at real-time base

A dynamic real-time algorithm of Learning Progress Motivation (LPM) is validated for dynamically diagnosing engineers’ learning progress and innovation performance at a real-time base in innovation processes. One hundred and three engineers participate in the situated experiments which simulate innovation contexts are motivated by LPM. Subjects’ learning progress and innovation performance are converted into quantitative data by LPM algorithm and then represented by a LPM characteristic curve. Through analyzing the LPM characteristic curve and subjects’ process-phase records from experiments, the findings show that LPM facilitates continuous learning and innovation through four-phase cycles and the LPM characteristic curve tends to converge toward a steady-state condition in which innovation deactivation takes place. Furthermore, the navigation effect of LPM algorithm is discovered and which enhances subjects’ continuous learning and innovation. The LPM Characteristic curve is proved to be a user-friendly visualized tool for diagnosing the status of learning progress and innovation performance in innovation processes.