AI becomes a good teacher, big data masters student learning

[ Instrument network instrument research and development ] Some experts said that for the current learning feedback in the classroom, artificial intelligence can understand students' knowledge of students through big data, so as to provide targeted guidance according to its different characteristics and promote precision education.

The relevant person in charge of iFLYTEK of HKUST said: “When a student reaches a stage of learning, such as completing a chapter, artificial intelligence can dynamically analyze the student’s learning situation through the knowledge graph, quickly test and find its weaknesses, and the system will plan learning for each student. Path, tailor-made learning plan to achieve precise consolidation and improvement."

What is artificial intelligence?

Artificial Intelligence (Artificial Intelligence), the English abbreviation is AI. It is a new technological science that studies and develops theories, methods, techniques and application systems used to simulate, extend and expand human intelligence.

Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, language recognition, image recognition, Natural language processing and expert systems, etc. Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the field of application has continued to expand. It is conceivable that the technological products brought by artificial intelligence in the future will be the "containers" of human wisdom. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like humans and may exceed human intelligence.

How does artificial intelligence judge students' learning?

Specifically, the system will automatically determine the number of knowledge points that students need to master, and list the relationships between the knowledge points. Students only need to answer a few questions to basically judge the learning blind spots. After students answer the questions, the system can immediately point out weak problems in the study (marked with red, yellow, and green dots). For example, the position in red means that we only focused on a learning link before. If the predecessor knowledge point is not solved, no amount of practice on this knowledge point will achieve the desired effect. Now the system will point out the knowledge points that students should learn as the first step. In response to this knowledge point, the learning system will intelligently push related courses and directly and accurately locate the segment that it should learn, and the problem can be solved in two or three minutes. After the students have finished learning, they can also check the learning effect in time through variant exercises, and check the knowledge graph again, and they will find that the knowledge points turn green, and the personalized problems can be broken.

Source: Science and Technology Daily, Encyclopedia