A Novel Insight into Learning Theory: The Gap between Teaching and Learning

Abstract

In our education system, teacher hopes his students to learn something specific from his demonstrations and textbook, his students try to understand his teacher's demonstration and textbook by their own learning methods. Obviously, the aim of the teacher may not be achievable for all students' learning methods. Therefore, students' final learning results are different from teacher's expectation in general, which is called the gap between teaching and learning (in short, GTL) in this paper. As the goal of machine learning is to design a computer program with learning ability, it is naturally questioned if GTL occurs in the machine learning fields. In this paper, we prove that there exists GTL in machine learning. As a common assumption in the current learning theory is that a learning algorithm usually realizes the original expectation, GTL provides a new insight into learning theory. According to the GTL Theory, the learning algorithms can be classified into four types, Type I through Type-IV. Comparison with human learning, the GTL Theory substantiates an intuitive observation: artificial intelligence can never surpass human intelligence from the learning point of view.