Improving College English Reading Teaching Efficiency Based on Student Behavior Data Mining and Mobile Edge Computing

Abstract

The reading scope of university English reading is constantly expanding, and the teaching content is gradually increasing. Students are unable to grasp the article’s ideas while reading, resulting in an incorrect understanding of the article and the selection of incorrect topics. In English reading, teachers should begin by broadening students’ knowledge, increasing the number of new words they learn on a regular basis, gradually building reading experience, and improving reading efficiency. Data mining (abbreviated as DM) is a method of extracting hidden, unknown, but potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical application data. Based on student behavior DM and mobile edge computing, this paper investigates strategies to improve the efficiency of university English reading instruction. Teachers and students can interact more easily with the help of university English reading teaching based on student behavior DM, and good interpersonal interaction can help students better understand and master the language. It is also beneficial for teachers to provide more tailored guidance for students’ individual university English reading teaching levels and learning abilities, as well as to assist them in developing personalized efficiency improvement strategies. The goal of DM student behavior is to discover knowledge, mine information, and apply rules without making any assumptions, with unknown, useful, and effective results.

Influence Mechanism of Students' Learning Enthusiasm Based on Educational Big Data

“…With the rapid development of informationization, the digitalization and informationization in the field of education are constantly promoted and improved, and the use of educational administration application system, access control system, all-in-one card data system and library lending system is gradually popularized [1][2][3][4][5][6][7][8][9]. It's possible to mine a large number of students' behavior data stored in the above systems, and analyze the differences in behavior characteristics among students [10][11][12][13][14][15]. Students' learning enthusiasm is an important indicators to measure their learning effectiveness and teaching quality, which can be characterized by students' time and energy for their studies [16][17][18][19][20].…”