Student Behavior Simulation in English Online Education based on Reinforcement Learning

2021 ◽  
Author(s):  
Wenjing Wang ◽  
Shanti C. Sandaran ◽  
R. Sabitha ◽  
K. Deepa Thilak
Keyword(s):  
Reinforcement Learning ◽  
Online Education ◽  
Student Behavior ◽  
Behavior Simulation

scholarly journals Analysis of Students’ Behavior in English Online Education Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunxia Wang
Keyword(s):  
Data Mining ◽  
Online Education ◽  
Prediction Error ◽  
Student Behavior ◽  
English Education ◽  
Learning System ◽  
Processing Efficiency ◽  
High Data ◽  
Fuzzy Neural ◽  
Continuous Application

With the formation of global economic integration for better exchange and cooperation with nations around the world, mastering English is extremely essential. In the context of today’s big era with a variety of English learning methods, it is required that data mining be applied to online English education. Owing to the continuous application of data mining techniques and the improvement of the online learning system, its application in education is also more and more prevalent. In the face of a large amount of learning data and student behavior data, the traditional methods have the problems of low processing efficiency, more memory requirements, and large prediction error. Therefore, this paper proposes a student behavior analysis method of online English education based on data mining. The student behavior data is collected, and an online English education learning behavior model is established. The data mining model is built to filter the obtained behavior data through data preparation, data statistics, and analysis. Furthermore, the apriori algorithm is used to mine association rules and calculate the similarity of data followed by the application of a fuzzy neural network to mine the behavior data of English online education students. The experimental results show that this method has high data processing efficiency, takes up less space, and produces a low prediction error.

scholarly journals Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rongrong Wang ◽  
Zhengjie Shi
Keyword(s):  
Online Education ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Student Behavior ◽  
Educational Process ◽  
Classification Model ◽  
Massive Data ◽  
Emotion Classification ◽  
Clickstream Data ◽  
Instructional Videos

Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.

scholarly journals Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shu-tong Xie ◽  
Qiong Chen ◽  
Kun-hong Liu ◽  
Qing-zhao Kong ◽  
Xiu-juan Cao
Keyword(s):  
Online Education ◽  
Behavior Analysis ◽  
Student Behavior ◽  
Feature Selection Method ◽  
Multiclass Classification ◽  
Classification Model ◽  
Learning Behavior ◽  
Teaching Activities ◽  
Education Data ◽  
Output Code

In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.

scholarly journals Online Classroom Management and Motivation

2021 ◽  
pp. 23-27
Author(s):  
Svetlana Buzu ◽  
◽  
Mariana Beschieru ◽  
Keyword(s):  
Online Education ◽  
Classroom Management ◽  
Student Behavior ◽  
Online Teaching ◽  
Management Strategies ◽  
Online Classroom ◽  
Classroom Management Strategies ◽  
Single Room ◽  
Wide Range ◽  
Engaged Teachers

Teaching online has become a considerable challenge for many educators. Everybody is talking about the revolution digital classroom has brought into the education world. Keeping the generation engaged, teachers need to find innovative and versatile methods, to use technology and manage the class online. Whether learning in a physical classroom or online, effective classroom management is the key component to a productive environment. Although, students are not all present in a single room, teachers must be intentional about managing student behavior and engagement in an online setting. Some challenges can be anticipated, and online classroom management strategies effectively overcome those challenges. Online learning focuses on a wide range of technological based learning platforms, delivery methods, and the integration of educational technology components into the learning environment. This article will address online education, its strengths, limitations, online teaching tools, professional development, best practices, and an evaluation of a personal online experience.

scholarly journals ALGORITHMS FOR EXAMINATION AND VERIFICATION OF WORK PROGRAMS OF DISCIPLINES FOR USE IN INFORMATION SYSTEMS

2021 ◽  
Vol 2021 (3) ◽  
pp. 143-156
Author(s):  
Anton Govorov ◽  
Karina Babayanc ◽  
Marina Govorova ◽  
Svetlana Derkunskaya ◽  
Anastasiia Chernysheva ◽  
...  
Keyword(s):  
Information Systems ◽  
Online Education ◽  
Educational Program ◽  
Student Behavior ◽  
Educational Institutions ◽  
State University ◽  
Validation And Verification ◽  
Course Content ◽  
Academic Courses ◽  
Verification Process

The article considers a syllabus as a core component of the educational program and an essential tool for describing an academic course for both a teacher and a student. Nowadays, there is gradual automation of document flow in educational institutions, including educa-tional and methodological documentation. It is required to develop the university's information system and the algorithm itself for the formal and content-wise review to automate the syllabus validation and verification process. The syllabus review process at St. Petersburg State University, RANEPA, and HSE was considered in the study. Therefore, a universal algorithm applicable to information systems has been designed. Since March 2021, ITMO University has been widely using “Educational Program Maker” web service to manage educational program elements. The developed system utilizes educational analytics methods which are widely used in online education for student behavior patterns evaluation and education results improvement. The proposed algorithm is implemented for syllabi development and review. Introduction of the module for the syllabi creation in a standardized and unified format allows to describe the prerequisites and post requisites of academic courses and link various educational activities with learning outcomes, making it possible to reveal the course content. Introducing the verification modules made it possible to automate the process of syllabi approvement

Measurement Invariance of a Universal Behavioral Screener Across Samples From the USA and Germany

2018 ◽  
Vol 34 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Gino Casale ◽  
Robert J. Volpe ◽  
Brian Daniels ◽  
Thomas Hennemann ◽  
Amy M. Briesch ◽  
...  
Keyword(s):  
Measurement Invariance ◽  
Student Behavior ◽  
Cross Cultural ◽  
Cross Cultural Comparison ◽  
School Based ◽  
Cultural Research ◽  
Universal Screener ◽  
Confirmatory Factor ◽  
The Us ◽  
The Usa

Abstract. The current study examines the item and scalar equivalence of an abbreviated school-based universal screener that was cross-culturally translated and adapted from English into German. The instrument was designed to assess student behavior problems that impact classroom learning. Participants were 1,346 K-6 grade students from the US (n = 390, Mage = 9.23, 38.5% female) and Germany (n = 956, Mage = 8.04, 40.1% female). Measurement invariance was tested by multigroup confirmatory factor analysis (CFA) across students from the US and Germany. Results support full scalar invariance between students from the US and Germany (df = 266, χ2 = 790.141, Δχ2 = 6.9, p < .001, CFI = 0.976, ΔCFI = 0.000, RMSEA = 0.052, ΔRMSEA = −0.003) indicating that the factor structure, the factor loadings, and the item thresholds are comparable across samples. This finding implies that a full cross-cultural comparison including latent factor means and structural coefficients between the US and the German version of the abbreviated screener is possible. Therefore, the tool can be used in German schools as well as for cross-cultural research purposes between the US and Germany.

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