Research on Teaching Quality Evaluation Using Data Mining Technique

2014 ◽  
Vol 926-930 ◽  
pp. 4582-4585
Author(s):  
Ai Feng Li ◽  
Ying Hu ◽  
Wen Jing Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Association Rule ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Data Mining Technique ◽  
Higher Education System ◽  
Potential Factors ◽  
Rule Method ◽  
Using Data

—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation

Application of Data Mining Technology in Sports Teaching Quality Evaluation

2014 ◽  
Vol 543-547 ◽  
pp. 4694-4697
Author(s):  
Li Min Zhou
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Scientific Method ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Evaluation Index ◽  
Data Mining Technique ◽  
Mining Technology ◽  
Mining Technique ◽  
Sign System

The unreasonable phenomenon caused by the lack of effective scientific method, The essay attempts to carry on related analysis and research of combining the data mining technique with sport teaching quality evaluation by mining valid sport teaching quality evaluation index sign system, making full use of the decision tree technique to solve the unreasonableness of the sports teaching quality evaluation and putting forward technical method for sports teaching quality evaluation based on the decision tree, aimed at making it fair, just, reasonable and efficient.

scholarly journals Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm

2018 ◽  
Vol 13 (10) ◽  
pp. 146 ◽  
Author(s):  
Sujuan Jia ◽  
Yajing Pang
Keyword(s):  
Decision Tree ◽  
Quality Evaluation ◽  
Computation Time ◽  
Teaching Quality ◽  
Quality Data ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Higher Education System ◽  
C4.5 Decision Tree

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.

scholarly journals A Student Performance Prediction Model Using Data Mining Technique

2018 ◽  
Vol 7 (2.15) ◽  
pp. 61
Author(s):  
Rohaila Abdul Razak ◽  
Mazni Omar ◽  
Mazida Ahmad
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Significant Variable ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Predicting Performance ◽  
Using Data

Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students’ performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.  

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

scholarly journals Analyzing the Quality of Business English Teaching Using Multimedia Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanyan Xin
Keyword(s):  
Data Mining ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Multimedia Data ◽  
Modern World ◽  
Business English ◽  
English Teaching ◽  
Using Data ◽  
Set Up

Data continually act as a substantial role in business and industry for its daily activities to smoothly functional. The data volume is growing with the passage of time and rising of information technology. Using data mining techniques for quality evaluation and business English teaching is essential in the modern world. These technologies are introduced in the classroom, especially in online classes during the COVID-19 pandemic. To analyze the quality of business English teaching, this paper uses multimedia and data mining technologies. Initially, the multimedia data are collected during classes, and the association rule recommendation algorithm using data mining is applied. Based on collaborative filtering algorithms in association rules, indicators for teaching quality evaluation in colleges and universities are set up. Next, the actual teaching data of a university is used. Taking business English as an example, the algorithm that has been built is tested. The application of the algorithm is tested, and the teaching process of College Business English is evaluated. Finally, the conclusion is drawn that data mining technology can describe the behavior of teaching well and evaluate it, and it has the potential of popularization.

scholarly journals Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

2019 ◽  
Vol 8 (11S) ◽  
pp. 164-168
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Tamil Nadu ◽  
Vital Role ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data ◽  
Work Done

Data mining plays an essential role in the cropproduction. It is a major field for forecasting and analyzing the crop. The vital role of the cultivator is to know about the production of the crop. In the years before, forecasting was carried out by taking into account the cultivator’s previous experience on the selected area. The forecasting was the important criteria which should be solved by considering the data on hand. By using Data mining method, the enhanced selection can be done. Various Data Mining methods have been used for calculating the upcoming year's production. This investigation helps to recommend a model for forecasting the yield from the earlier data. For accomplishing and forecasting the yield association rule mining in data mining has been used. This helps to focus on implementing a system which may be used for forecasting the yield in the upcoming years. This research aims at presenting a detailed study by forecasting the yield using association rules in data mining technique for the chosen area in India. The results haveshown that the anticipated work done is working well in order to predict the production of the yield.

scholarly journals Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

2019 ◽  
Vol 1 ◽  
pp. 1060
Author(s):  
Yori Apridonal M ◽  
Febri Dristyan ◽  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Layout Design ◽  
Association Pattern ◽  
Processing Data ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Using Data ◽  
Confidence Value ◽  
The Web

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

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