Notice of Retraction: Research and Application of the Data Mining Technology on the Quality of Teaching Evaluation

2010 ◽  
Author(s):  
Zhang Yihua
Keyword(s):  
Data Mining ◽  
Teaching Evaluation ◽  
Mining Technology ◽  
Quality Of Teaching

Evaluation Model of College English Teaching Based on the Theory of Cloud Gravity Center

2014 ◽  
Vol 644-650 ◽  
pp. 6087-6092
Author(s):  
Liu Qun Yang ◽  
Lei He ◽  
Gang Li
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Teaching Evaluation ◽  
Gravity Center ◽  
College English ◽  
English Teaching ◽  
Qualitative And Quantitative ◽  
Quality Of Teaching

College English teaching evaluation is an important step to improve the quality of teaching and to promote students' development and assessment of teachers' performance, which is characterized by many factors and multidimensional structure. The current college English teaching evaluation method faces such problems: fuzzy evaluation criteria, subjective judgment and the difficulty to quantify evaluation results. Based on cloud theory, this article will explore college English teaching evaluation, and establish a comprehensive evaluation model based on cloud gravity center by using weight deviation to measure the quality of teaching. This article will also build an inter-relationship mapping between qualitative and quantitative evaluation, which supports the whole process of comprehensive evaluation with the combination of qualitative and quantitative indexes. Finally this article gives an example of specific application of this method.

scholarly journals Teaching Quality Evaluation: Online vs. Manually, Facts and Myths

10.28945/3904 ◽  
2017 ◽  
Vol 16 ◽  
pp. 277-290
Author(s):  
Esmael A. Salman
Keyword(s):  
Online Teaching ◽  
Evaluation Method ◽  
Cost Savings ◽  
Teaching Quality ◽  
Teaching Evaluation ◽  
Future Research ◽  
Male And Female ◽  
Quality Of Teaching ◽  
Online Feedback

Aim/Purpose: This study aimed to examine whether there is a difference between manual feedback and online feedback with regard to feedback quality, respondents’ percentage, reliability and the amount of verbal comments written by students. Background: The quality of teaching is an important component of academic work. There are various methods for testing the quality of teaching; one of these methods is through students’ feedback. Methodology: This study used a quantitative approach, including the quantification of qualitative verbal data collected through an open question in the questionnaire. A sample of 180 courses was randomly chosen, 90 courses were evaluated manually and 90 were evaluated online. The number of students ranges from 7 to 60 students per course. In total 4678 students participated in the study. Contribution: The findings show that there is almost an identical pattern of feedback of manual and online course teaching evaluation. These findings encourage a continued use of this evaluation method. Findings: No significant differences were found between manual feedback and online feedback in the students’ evaluation of the lecturer/course. The percentage of respondents was significantly higher in the manual feedback than in the online feedback. The number of qualitative comments was significantly greater in the online feedback than in the manual feedback. Impact on Society: The findings of this study refute the claims with regard to the unreliability of an online teaching evaluation. These findings reflect the advantages of using online feedback, such as cost savings, granting more time to students in order to provide feedback, and reducing disturbance during lectures. Future Research: The gender aspect was not taken into account in the study. Therefore, we recommend conducting a follow-up study that will examine gender differences in directions of- difference between male and female lecturers, and differences between male and female students in teaching evaluation.

scholarly journals Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yong-tong Ma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Education ◽  
Information Fusion ◽  
Teaching Methods ◽  
Evaluation System ◽  
Evaluation Process ◽  
Teaching Evaluation ◽  
Evaluation Index ◽  
Mining Technology

The purpose is to enrich the evaluation system of physical education (PE) teaching in colleges and universities and to improve PE teaching methods and improve teaching quality. Based on big data information fusion and data mining technology, firstly, the related theories of teaching evaluation are analyzed and expounded, as well as the characteristics and principles of the construction of college PE teaching evaluation system. Secondly, from the perspective of evaluation index system of sports teachers’ teaching and students’ sports teaching, the content and evaluation index of college sports teaching evaluation are analyzed under the background of big data information fusion and data mining by questionnaire survey. Combined with model test, the results show that traditional college sports teacher pays more attention to the design and teaching methods of PE and ignore the learning process of students. The evaluation process of PE ignores the individual differences of students, the feedback method lacks openness, and the evaluation process is isolated. Based on the big data technology and teaching evaluation theory, the evaluation index is designed for PE teaching in colleges and universities. The average value of the first layer indexes is above 4, and the coefficient of variation is less than 0.2, which can basically reflect the content of PE teaching evaluation and provide some reference for the research of PE teaching evaluation.

scholarly journals Penerapan Data Mining dalam Meningkatkan Mutu Pembelajaran Menggunakan Metode K-MEANS Clustering

2016 ◽  
Vol 2 (3) ◽  
pp. 31-40
Author(s):  
Koko Handoko
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Community College ◽  
Clustering Methods ◽  
Parental Income ◽  
Cluster Group ◽  
Quality Of Teaching ◽  
Quality Of Learning ◽  
Data Tables

Abstract— This research applies data mining using clustering methods to improve the quality of learning in Higher Education Institutions in the Program TKJ Community College South Solok. The algorithm used is K-Means Clustering is a process of grouping a number of data or object into a cluster (group) so that each cluster will contain the data that is as similar as possible and different from the objects in other clusters. Testing is done with RapidMiner 5.3 applications that generate clusters in improving the quality of learning. The samples used were taken from the data tables of students who have ditrasformasi. Where the variables are defined as the first test four variables, namely: IP students, distance students, attendance and parental income. Where the students will present data with the quality of teaching is very good, good, good enough, and less good.

Research on English score analysis system based on improved decision tree algorithm and fuzzy set

2020 ◽  
Vol 39 (4) ◽  
pp. 5673-5685
Author(s):  
Weiying Zhang
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Reference Value ◽  
Mining Technology ◽  
Factors Affecting ◽  
Main Work ◽  
Score Analysis ◽  
Analysis System

At present, the data mining technology is introduced into the analysis of English scores, the data is deeply explored and analyzed reasonably, and the analysis results are used to guide the smooth development of teaching, which is conducive to improving the quality of English teaching. The main work of this thesis is based on the background of this study: taking the academic performance of college students as the application background, this paper first introduces the basic theoretical knowledge of data mining and the application status of data mining technology in education field. Secondly, this paper establishes a student performance database and uses data mining technology to carry out in-depth mining of the established performance database. Finally, the mining results are analyzed, and the factors affecting students’ academic performance are obtained. These analysis results have important reference value for the future improvement of teaching work in colleges and universities.

scholarly journals Monitoring Indicators of the Flipped Classroom Learning Process based on Data Mining – Taking the Course of “Virtual Reality Technology” as an Example

2019 ◽  
Vol 14 (03) ◽  
pp. 166 ◽  
Author(s):  
Yang Ji ◽  
Yu Han
Keyword(s):  
Data Mining ◽  
Virtual Reality ◽  
Curriculum Reform ◽  
Learning Process ◽  
Flipped Classroom ◽  
Process Data ◽  
Virtual Reality Technology ◽  
Learning And Teaching ◽  
Quality Of Teaching

In recent years, due to the superiority of its teaching effect, the flipped classroom has attracted more and more attention from colleges and universities. In the information age, MOOC and micro-lectures have gradually entered the college classroom and become new ways of learning and teaching. Considering the further requirement on the quality of teaching, it is not enough to monitor merely the learning results. Instead, the curriculum reform requires more attention to the learning process. Data mining was combined with MOOC and micro-lectures in this study for the case study of the course of “Virtual Reality Technology”. Academic performance, learning input and learning behavior were selected as the indicators to monitor the process of learning “Virtual Reality Technology’. Relevant data were collected and analyzed. The analysis results were used as the basis to provide ideas and approaches for the implementation of reliable and effective teaching intervention methods in the process of teaching “Virtual Reality Technology”, so as improve the quality of teaching and provide reference for other related courses.

The Application of Data Mining Technology in the Teaching Evaluation in Colleges and Universities

2017 ◽  
Vol 14 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Xiaoqi Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Evaluation System ◽  
Information Gain ◽  
Original Data ◽  
Teaching Evaluation ◽  
Personal Factors ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
College Work

As the teaching management informationization level is higher and higher, Network based teaching evaluation system has been widely used, and a lot of evaluation of the original data has been accumulated. This research, taking recent five years teaching evaluation data of the college work for as basis, analyzes teachers’ personal factors and teaching operation factors respectively with the data mining technology of decision tree ID3 algorithm. By calculating the factors of information entropy and information gain value, the corresponding decision tree is gained. The teaching evaluation results are made use of really rather than become a mere formality, and thus provide powerful basis for the effectiveness and scientificalness of teaching evaluation.

scholarly journals The Multiple Forms of Teaching Evaluation, Improve Teachers' Teaching Ability

2017 ◽  
Vol 1 (1) ◽  
pp. 42
Author(s):  
Wuyi Liu
Keyword(s):  
Teaching Evaluation ◽  
Education Quality ◽  
Multiple Forms ◽  
Quality Of Teaching ◽  
Teaching Ability ◽  
Solid Foundation

This article in view of the college practical connotation construction, takes many forms of teaching evaluation, solid foundation, improve teachers' teaching coaching ability construction, promote the improvement of education quality of teaching and talent training.

scholarly journals Research on college English teaching based on data mining technology

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jinhui Duan ◽  
Rui Gao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Conversion ◽  
Tree Model ◽  
Mining Technology ◽  
Processing Technologies ◽  
College English ◽  
English Teaching ◽  
School Teaching

AbstractTo improve the efficiency and quality of college English teaching, we analyzed the feasibility and application process of data mining technology in college English teaching. The entire process of data classification mining was fully realized. A new teaching program was proposed. The object and target of data mining were determined. Online surveys were used to collect data. Data integration, data cleaning, data conversion, data reduction and other pre-processing technologies were adopted. The decision tree was generated by using the C4.5 algorithm, and the pruning was carried out. The result analysis decision tree model was completed. A detailed survey of the students' English learning in University was made in detail. The results showed that the qualified rate of students' English performance was increased from 20–30% to 50–60%. Therefore, the classification rules provide theoretical support for the school teaching decision. This method can improve the quality of English teaching.

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