Evaluating the Performance of Teaching Assistant Using Decision Tree ID3 Algorithm

Devasenapathy, K.; Duraisamy, S.

doi:10.5120/ijca2017913658

Cited by 7 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each attribute possesses a distinct range of values. The conditional entropy of each attribute is computed individually, along with the information entropy of the remaining attributes under the condition of the classification category ( Devasenapathy & Duraisamy, 2017 ).…”

Section: English Multimedia Teaching Resource Sharing Platformmentioning

confidence: 99%

English teaching system design based on ID3 algorithm and node optimization

Jiang,

2023

PeerJ Computer Science

View full text Add to dashboard Cite

In order to optimize the integration of English multimedia resources and achieve the goal of sharing English teaching resources in education, this article reconstructs the traditional college English curriculum system. It divides professional English into learning modules according to different majors integrating public health teaching resources. How optimize the integration of English multimedia resources and achieving the goal of sharing English teaching resources (ETR) is the main direction of English teaching reform during the current COVID-19 pandemic. An English multimedia teaching resource-sharing platform is designed to extract feature items from multimedia teaching resources using the ID3 information gain method and construct a decision tree for resource push. In resource sharing, a structured peer-to-peer network is used to manage nodes, query location and share multimedia teaching resources. The optimal gateway node is selected by calculating the distance between each gateway node and the fixed node. Finally, a collaborative filtering (CF) algorithm recommends Multimedia ETR to different users. The simulation results show that the platform can improve the sharing speed and utilization rate of teaching resources, with maximum throughput reaching 12 Mb/s and achieve accurate recommendations of ETR.

show abstract

Section: English Multimedia Teaching Resource Sharing Platformmentioning

confidence: 99%

English teaching system design based on ID3 algorithm and node optimization

Jiang,

2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Devasenapathy and Duraisamy discussed how specific strategies can be adopted to increase the richness and innovation of university aesthetic education. Integrating art into university aesthetic education can significantly increase the diversity and flexibility of the content, while fully incorporating students' hands-on and imaginative skills, thus improving their hands-on creativity [20].…”

Section: E Influence Of Aesthetic Education On College Students'mentioning

confidence: 99%

Analyzing the Influence of College Aesthetic Education Teaching on College Students’ Innovation Ability and Artistic Literacy Based on Decision Tree Classification Model

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

University-level aesthetic education can help students develop their aesthetic sensibilities, enhance their artistic and creative skills, maximise their artistic and creative potential, and produce more and higher-quality works of art. Aesthetic ability is crucial for students’ quality because it helps them better appreciate the appeal of artistic creations, enhance their art education, and advance in their future growth and development. Therefore, it should place a high value on aesthetic education at the university and work at various levels to gradually cultivate students’ sentiments and enhance their aesthetic skills. A new “Big Data era” has emerged as a result of the expanding and widespread use of IT and the massive amount of data that needs to be stored and used. There are several widely used methods for classifying data, including decision trees, Bayesian classification, Bayesian networks, and neural networks. In order to more effectively develop students’ aesthetic ability in college aesthetic education teaching activities, the impact of college aesthetic education on college students’ creative ability and artistic literacy was examined. This was done on the basis of a decision tree classification model. According to the experimental results, the decision tree model significantly increased the accuracy of preference classes while maintaining the same level of overall accuracy. The F-value of the decision tree model on various data sets was improved by 0.318 and 0.221, respectively, in comparison to the SVM algorithm. The development of innovation ability in university aesthetic education is crucial for developing students’ personalities, bettering study habits, enhancing aesthetic literacy, and developing comprehensive ability. As a result, we analyze the impact of university aesthetic education on college students’ innovation ability and artistic literacy using the decision tree classification model.

show abstract

“…As a result, a Decision Tree is formed, with each branch representing a possible decision scenario and its outcome. The most widely used DT learning algorithms are Hunt's ID3, CART, CHAID, and C4.5 [6]. CART (Classification and Regression Tree) is a method or algorithm of one of the data exploration techniques, namely the decision tree technique.…”

Section: Introductionmentioning

confidence: 99%

“…The results demonstrate the ID3 algorithm for building a decision tree (Decision Tree). The algorithm's implementation describes the Teaching Assistant's performance during the summer and the regular semester [6].…”

Section: Introductionmentioning

confidence: 99%

Iterative Dichotomiser Three (Id3) Algorithm For Classification Community of Productive and Non-Productive

Ida¹

2023

j. Teknik inform.

View full text Add to dashboard Cite

One way to tackle poverty is to provide information about productive and non-productive communities in each rural. This is very beneficial for the government, especially in each rural regarding the classification of community data. This research aims to classify productive and non-productive people so that the government can prioritize assistance for people deemed necessary to be more creative in fulfilling their family's economy. The research method used is the Iterative Dichitomiser Three (ID3) algorithm to build a decision tree. The process in the decision tree is changing the shape of the data (table) into a tree (tree) and generating rules based on the highest Entropy and Gain values. The study's conclusion shows that this algorithm can be processed in a shorter time, with shorter decision rules and higher prediction accuracy, by displaying the highest gain value. The parameters used to consist of education, age, income, and employment status, which results in the following rule if higher education and high income, then the result is a productive society, whereas if high school education and low income, then the result is a non-productive society.

show abstract

Evaluating the Performance of Teaching Assistant Using Decision Tree ID3 Algorithm

Cited by 7 publications

References 2 publications

English teaching system design based on ID3 algorithm and node optimization

English teaching system design based on ID3 algorithm and node optimization

Analyzing the Influence of College Aesthetic Education Teaching on College Students’ Innovation Ability and Artistic Literacy Based on Decision Tree Classification Model

Iterative Dichotomiser Three (Id3) Algorithm For Classification Community of Productive and Non-Productive

Contact Info

Product

Resources

About