In this paper, we study INTRINSIC vs. extrinsic MOTIVATION in players playing an electrical engineering gaming environment. We used UNTANGLED, a highly interactive game to conduct this study. This game is developed to solve complex mapping problem in electrical engineering using human intuitions. Our goal is to find whether there are differences in the ways anonymous players solved electrical engineering puzzles in an electronic gaming environment when motivated to play competitively, as compared to self-regulated play. For our experiments, we used puzzles from four games from UNTANGLED. A one-way analysis of variance (ANOVA) was calculated on participants’ scores, type of plays, number of plays, and time spent playing, as both self-regulated and competitive players. We also examined difference between the type of moves used by the competitive and selfregulated players. Our results support the theory of MOTIVATION as being internally embedded in learners. The results also demonstrate that a self-regulated learner does not require MOTIVATION to improve one’ s performance.

INTRINSIC MOTIVATION has garnered significant attention in recent years, empowering both living beings and robots to learn autonomously and cumulatively, even without extrinsic MOTIVATION (rewards from the environment). This concept, drawing inspiration from psychology and neuroscience, has opened up new avenues in artificial intelligence. Algorithmic architectures for INTRINSIC MOTIVATION facilitate exploration and the effective acquisition of motor skills in scenarios where environment rewards are sparse or absent. This is particularly relevant for many real-world problems, where large portions of the environment offer no explicit rewards. Consequently, INTRINSIC MOTIVATION holds not only theoretical significance for enhancing artificial intelligence algorithms, particularly in exploration tasks, but also practical implications for real-world or near-real-world applications. In this paper, we delve into the significance of INTRINSIC MOTIVATION, providing a brief overview of its origins in psychology. We then systematically categorize and examine research on INTRINSIC MOTIVATION in artificial intelligence. Additionally, we discuss the reinforcement learning method as a successful approach for incorporating INTRINSIC MOTIVATION. Finally, we explore the practical applications, limitations, and future INTRINSIC MOTIVATION research.

This study explores the MOTIVATIONal components within teacher rating systems and their impact on professional growth MOTIVATION. Employing a systematic review approach, it investigates texts pertaining to teacher rating systems, MOTIVATION, and professional development literature. By examining various texts and research findings on teacher MOTIVATION, factors influencing teacher ratings based on general, specialized, and professional competencies, as well as experience, are identified. Drawing parallels with Herzberg's MOTIVATIONal theory, this study provides recommendations to bolster teacher MOTIVATION and job satisfaction through improvements in the rating system.