Teaching Decision-Making Based on Online Learning Big Data of Tobacco Courses: The Perspective of Student Portraits

Abstract

The high-quality development and technological upgrading of the tobacco industry put forward higher requirements for the overall quality of talents. In the context of the increasing popularity of blended teaching, in order to help teachers, major in tobacco, tomake better teaching decisions in the teaching process, guide college students majoring in tobacco to better complete their studies and provide timely warnings for students’ unhealthy conditions, this article proposes a method to assist teachers in teaching decision-making based on student portraits constructed based on online learning big data. First, collect basic student information and student learning information from the online learning platform. Secondly, preprocess of the data, delete data and normalize dense data. Then, collect and classify student information to form a portrait of basic student information, a portrait of learning achievements, a portrait of learning active level and a portrait of learning status. Analyze the portrait to guide and assist students in their learning and to give early warning of bad learning conditions. At last, analyze the student portraits according to different rules and put forward corresponding suggestions according to the characteristics of different groups of college students. According to the learning situation of learners majoring in tobacco, the article constructs the student portrait label system and portrait model. According to the constructed student portrait, it puts forward learning suggestions for individual students and student groups respectively. In the field of tobacco teaching, it has certain reference significance and application value in providing decision-making reference for differentiated and individualized teaching and assisting teaching decision-making.