How does knowledge evolve using adaptive heuristics learning in an engineering game

Abstract

In this paper, the primary focus is on the performers whose learning process originate by solving simple or complex problems and perceiving the performers interest in solving advanced problems from the knowledge obtained. An open-source puzzle like a game UNTANGLED is used in our study. The game is developed to unravel the mapping/placement problems in electrical engineering by using human instincts. Telemetry data for the two groups of performers who solved simple and complex puzzles in the first attempt is considered to investigate the Kolb's Experiential Learning Theory (KELT) and fathom the adaptive heuristics for building knowledge from experience. From analysis performed it is evident that a similar learning process is followed by both performers who played initial and complex puzzles in first attempt. Also, results illustrate that the players who first played initial level puzzles are more interested in playing next level puzzles than the one who first played complex puzzles. Results illustrate that 18% of players who solved easy in first attempt played advanced puzzles in consecutive attempts. Apparently, conclusions advocate that to develop an indelible appetite to deal with advanced/complex problems, STEM education teachers need to structure the lab experiments or teach the complex concepts by starting from simple projects/concepts to complex one. By making learners to try a greater number of low-level abstraction problems will engage learners’ interest in solving high-level abstraction problems. Similarly, educational game designers can develop a game environment introducing more intermediate levels, which gives enough experience to deal with difficult levels