Details

Autor(en) / Beteiligte

• He, Yan-ting
• Guo, Ben-Jun
• Lu, Jun
• Xu, Yuan-ping
• Gong, Mei

Titel

Research of Scratch Programming Recommendation System Based on MED and Knowledge Graph

Ist Teil von

• 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020, p.2158-2163

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• In recent years, children's programming education and primary and secondary school programming education are gradually gaining importance. After the State Council issued relevant documents on the development of artificial intelligence, the status of programming education for primary and secondary schools has been improved. At the same time, in order to improve learning efficiency, it is especially important to build a personalized programming topic recommendation system that takes into account the differences in learning ability and learning characteristics of different learners. This paper introduces a knowledge graph based on the traditional recommendation method, divides a single program block in Scratch programming into several knowledge points that are related to each other, processes the strings and calculates the editing distances according to the topics, finds out the similarities between the topics, combines the difficulty of the topics and knowledge points, and then designs a personalized recommendation system for Scratch programming topics based on the editing distances and knowledge graphs based on the learners' historical answer data, which effectively improves the recommendation accuracy.