scholarly journals MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6629
Author(s):  
Xiaoliang Zhu ◽  
Yuanxin Ye ◽  
Liang Zhao ◽  
Chen Shen
Keyword(s):  
Academic Performance ◽  
Behavior Change ◽  
Performance Prediction ◽  
Prediction Performance ◽  
Free Access ◽  
Good Prediction ◽  
Relevant Research ◽  
Log Data ◽  
Research Fields ◽  
Behavioral Features

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

Latin learning and instruction as a research field

2019 ◽  
Vol 18 (1-2) ◽  
pp. 35-59
Author(s):  
Suzanne Adema
Keyword(s):  
Second Language Acquisition ◽  
Language Acquisition ◽  
Child Development ◽  
Text Comprehension ◽  
Research Field ◽  
Relevant Research ◽  
Translation Research ◽  
Research Fields ◽  
Dictionary Use ◽  
Learning And Instruction

Abstract Empirical research on the learning and instruction of Latin is still scarce. In this article, relevant research is surveyed, along with publications that report experiences of classics teachers or provide teaching suggestions. An overview is presented of where to find publications on the learning and instruction of Latin, as well as a brief introduction to several relevant research methods. The article is organized by reference to various research fields relevant to the learning and instruction of Latin. These fields are classics and Latin linguistics, second language acquisition, vocabulary acquisition and dictionary use, reading and text comprehension, translation research and pedagogy, child development and psychology.

scholarly journals Design and Optimization of a Fuzzy Logic System for Academic Performance Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Juan A. Rojas ◽  
Helbert E. Espitia ◽  
Lilian A. Bejarano
Keyword(s):  
Academic Performance ◽  
Performance Prediction ◽  
Teaching Methods ◽  
Continuous Monitoring ◽  
Educational Institution ◽  
Educational Institutions ◽  
Educational Support ◽  
Design And Optimization ◽  
Monitoring Process ◽  
Logic System

Currently, in Colombia, different problems in education exist; one of them is the inconvenience in tracing and controlling the learning trajectories that decide the topics taught in the country’s educational institutions. This work aims to implement a logic-based system that allows teachers and educational institutions to carry out a continuous monitoring process of students’ academic performance, facilitating early corrections of errors or failures in teaching methods, to promote educational support spaces within the educational institution.

scholarly journals Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

2021 ◽  
Author(s):  
Shermain Puah
Keyword(s):  
Academic Performance ◽  
Educational Research ◽  
Performance Prediction ◽  
Predictive Accuracy ◽  
Lasso Regression ◽  
Machine Learning Methods ◽  
Large Numbers ◽  
Unseen Data ◽  
Research In Education ◽  
Science Performance

Predicting students’ academic performance has long been an important area of research in education. Most existing literature have made use of traditional statistical methods that run into the problems of overfitted models, inability to effectively handle large numbers of participants and predictors, and inability to pick out non-linearities that may be present. Regression-based ML methods that can produce highly interpretable yet accurate models for new predictions, are able to provide some solutions to the aforementioned problems. The present study is the first study that develops and compares between traditional MLR methods and regression-based ML methods (i.e. ridge regression, LASSO regression, elastic net, and regression trees) to predict students’ science performance in the PISA 2015. A total of 198,712 students from 60 countries, and 66 student- and school-related predictors were used to develop the predictive models. Predictive accuracy of the various models built were not that different, however, there were significant differences in the predictors identified as most important by the different methods. Although regression-based ML techniques did not outperform traditional MLR, significant advantages for using ML methods were noted and discussed. Moving forward, we strongly believe that there is merit for using such regression-based ML methods in educational research. Educational research can benefit from adopting ML practices and methods to produce models that can not only be used for explaining factors that influence academic performance prediction, but also for making more accurate predictions on unseen data.

scholarly journals Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 234 ◽  
Author(s):  
Yuxuan Wang ◽  
Xuebang Wu ◽  
Xiangyan Li ◽  
Zhuoming Xie ◽  
Rui Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Tensile Properties ◽  
Prediction Performance ◽  
Good Prediction ◽  
Relative Significance ◽  
Value Analysis ◽  
Artificial Neural

Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

An approach to academic performance prediction in tutoring systems based on fuzzy deformable prototypes

2015 ◽  
Vol 5 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Ma-Rosario Vázquez ◽  
Francisco P. Romero ◽  
José A. Olivas ◽  
Eduardo Orbe ◽  
Jesús Serrano-Guerrero
Keyword(s):  
Academic Performance ◽  
Performance Prediction ◽  
Tutoring Systems

scholarly journals Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

2021 ◽  
pp. 1-11
Author(s):  
S. M. Abdullah Al Shuaeb ◽  
Shamsul Alam ◽  
Md. Mizanur Rahman ◽  
Md. Abdul Matin
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Huge Amount ◽  
Novel Technique ◽  
Student Academic Performance ◽  
Academic Activities ◽  
Proposed Model ◽  
Prediction Systems

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

Sign in / Sign up

Export Citation Format

Share Document