AU2020102708A4 - Student participation and performance prediction analysis technique during online classes using data mining - Google Patents
Student participation and performance prediction analysis technique during online classes using data mining Download PDFInfo
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Abstract
STUDENT PARTICIPATION AND PERFORMANCE PREDICTION ANALYSIS
TECHNIQUE DURING ONLINE CLASSES USING DATA MINING
ABSTRACT
As online classes are emerging nowadays it is crucial to predict the student's performance using
various techniques. Educational data mining and learning analytics two main factors to analyse. In
addition to analyse and provide solution based on the institutional view. Also, it gives solution
based on the instructor views. Also based on the dataset arrived it will provide the solution. The
prediction can be made possible using the methods like decision tree, neural network, nave Bayes,
K-Nearest neighbour and support vector machine. Decision tree is the simplest one to predict, the
small and big data. It is easier to convert to the IF-THEN rules. Neural network predicts regardless
of the dependent or independent variable. This too predicts in a better manner of the psychometric,
GPAs in a better way and yields a better accuracy. Nave Bayes too make a good prediction with
comparison about the student's performance. K-Nearest neighbour produces a good accuracy and
it predicts faster than the other algorithms. support vector machine also used for mainly
classification, but it predicts too. It is also faster than the other techniques. In addition to the other
tools like Probabilistic Soft Logic (PSL), logistic regression, ID3, Classification and Regression
Tree (CART) algorithm can be used. This will analyse and produce the accuracy.
11 P a g e
STUDENT PARTICIPATION AND PERFORMANCE PREDICTION ANALYSIS
TECHNIQUE DURING ONLINE CLASSES USING DATA MINING
Drawings
Figure 1: Overall architecture for gathering information
1| P a g e
Description
Drawings
Figure 1: Overall architecture for gathering information
1| P a g e
Description
Field of the Invention:
This invention relates to predicting the student's performance using various techniques. Educational data mining and learning analytics two main factors to analyse. In addition to analyse and provide a solution based on the institutional view. Also, it gives a solution based on the instructor's views. Also based on the dataset arrived it will provide the solution. The prediction can be made possible using the methods like decision tree, neural network, naive Bayes, K-Nearest neighbour, and support vector machine. The decision tree is the simplest one to predict, the small and big data. It is easier to convert to the IF-THEN rules. The neural network predicts regardless of the dependent or independent variable. This too predicts in a better manner of the psychometric, GPAs in a better way and yields better accuracy. Naive Bayes to make a good prediction with a comparison of the student's performance. K-Nearest neighbour produces good accuracy and it predicts faster than the other algorithms. support vector machine is also used for the main classification, but it predicts too. It is also faster than other techniques. In addition to the other tools like Probabilistic Soft Logic (PSL), logistic regression, ID3, Classification, and Regression Tree (CART) algorithm can be used. This will analyse and produce accuracy.
Background of the invention:
Cristobal Romero and Sebastian Ventura surveyed data mining in education. They considered two communities educational data mining and learning analytics. In educational data mining, the data set is considered which comes from the educational environments. Learning analytics will analyse learners. As the perception of education is to analyse the learners and take action accordingly. In addition to the two communities, it will analyse and provide a solution based on the institutional view. Also, it gives a solution based on the instructor's views. Also based on the dataset arrived it will provide the solution.
11 P a g e
Amirah Mohamed Shahiri et al., suggest various methodologies used for predicting the performance of students. There are various methodologies like research questions, search strategy, Also, it explains various methods used for prediction. It suggests the methods like decision tree, neural network, naive Bayes, K-Nearest neighbour, and supports vector machine. The decision tree is the simplest one to predict, the small and big data. It is easier to convert to the IF-THEN rules. The neural network predicts regardless of the dependent or independent variable. This too predicts in a better manner of the psychometric, GPAs in a better way and yields better accuracy. Nave Bayes to make a good prediction with a comparison of the student's performance. K-Nearest neighbour produces good accuracy and it predicts faster than the other algorithms. support vector machine is also used for the main classification, but it predicts too. It is also faster than other techniques.
Sani Salisu et al., data mining, and learning analytics. In educational data mining, the data set is considered which comes from the educational environments. Learning analytics will analyse learners. As the perception of education is to analyse the learners and take action accordingly. The technique used is the Rapid Miner tool.
Kloft et al. use Support Vector Machine for classifying the students who are attending, and they are leaving. The classifier was trained using lecture video views. This achieved an accuracy rate of 72%.
Ramesh et al. use Probabilistic Soft Logic (PSL) for the prediction. It checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 72% accuracy.
Jiang et al. use logistic regression for the prediction. It checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 80% accuracy.
Glyn Hughes and Chelsea Dobbins et al. checks whether the learner completes the quizzes, assignments. They complete the course or not. It concludes that the role of technology plays an important role in the learning environment.
21Page
Mushtaq Hussai et al., explains about the virtual learning environment. The data was visualized, and the statistical analysis was made. The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made.
Brijesh Kumar Baradwaj et al., explains about the virtual learning environment. The data was visualized, and the statistical analysis was made. The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made. They used the ID3 algorithm to evaluate this.
M Krishna et al., explains the virtual learning environment. The data was visualized, and the statistical analysis was made. The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made. They used the Classification and Regression Tree (CART) algorithm to evaluate this.
Objects of the Invention:
• To predict the student's performance using various techniques. • Analyse the educational data mining and learning analytics. • Analyse and provide solutions based on the institutional view. • To provide a solution based on the instructor's views. • To provide a solution based on the dataset arrived. • To make a prediction using the methods like decision tree, neural network, nave Bayes, K-Nearest neighbour, and support vector machine. • To make use of other tools like Probabilistic Soft Logic (PSL), logistic regression, ID3, Classification and Regression Tree (CART) algorithm • To analyse and produce accuracy.
Summary of the Invention
31Page
Educational data mining and learning analytics two main factors to analyse. In addition to analyse and provide a solution based on the institutional view. Also, it gives a solution based on instructor views.
Also based on the dataset arrived it will provide the solution. The prediction can be made possible using the methods like decision tree, neural network, naive Bayes, K-Nearest neighbour, and support vector machine.
The decision tree is the simplest one to predict, the small and big data. It is easier to convert to the IF-THEN rules. The neural network predicts regardless of the dependent or independent variable. This too predicts in a better manner of the psychometric, GPAs in a better way and yields better accuracy. Nave Bayes to make a good prediction with a comparison of the student's performance. K-Nearest neighbour produces good accuracy and it predicts faster than the other algorithms. support vector machine is also used for the main classification, but it predicts too. It is also faster than other techniques.
In addition to the other tools like Probabilistic Soft Logic (PSL), logistic regression, ID3, Classification, and Regression Tree (CART) algorithm can be used. This will analyse and produce accuracy.
Probabilistic Soft Logic (PSL) checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 72% accuracy. So, this is a tool that may predict this online class too.
Logistic regression checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 80% accuracy. So, this is a tool that may predict this online class too.
The data was visualized, and the statistical analysis was made. The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made. The Classification and Regression Tree (CART) algorithm is used to evaluate this.
41Page
The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made and the ID3 algorithm is used to evaluate this.
Detailed Description of the Invention:
Educational data mining and learning analytics two main factors to analyse. In addition to analyse and provide a solution based on the institutional view. Also, it gives a solution based on the instructor's views. The steps involved in this process are depicted in figure 1.
Also based on the dataset arrived it will provide the solution. The prediction can be made possible using the methods like decision tree, neural network, nave Bayes, K-Nearest neighbour, and support vector machine.
The decision tree is the simplest one to predict, the small and big data. It is easier to convert to the IF-THEN rules. The neural network predicts regardless of the dependent or independent variable. This too predicts in a better manner of the psychometric, GPAs in a better way and yields better accuracy. Nave Bayes to make a good prediction with a comparison of the student's performance. K-Nearest neighbour produces good accuracy and it predicts faster than the other algorithms. support vector machine is also used for the main classification, but it predicts too. It is also faster than other techniques.
In addition to the other tools like Probabilistic Soft Logic (PSL), logistic regression, ID3, Classification, and Regression Tree (CART) algorithm can be used. This will analyse and produce accuracy.
Probabilistic Soft Logic (PSL) checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 72% accuracy. So, this is a tool that may predict this online class too.
Logistic regression checks whether the learner completes the quizzes, assignments. They complete the course or not. Whether there scoring is above zero, it was found that this too achieves 80% accuracy. So, this is a tool that may predict this online class too.
51Page
The data was visualized, and the statistical analysis was made. The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analyzed. The comparison was made. The Classification and Regression Tree (CART) algorithm is used to evaluate this.
The analysis was made to identify the low average learners. The data visualization was done based on the clicks per activity. The prediction based on every activity was analysed. The comparison was made and the ID3 algorithm is used to evaluate this.
61Page
Claims (9)
1. Analysing two main factors: • Educational data mining • Learning analytics
2. Providing solution based on: • Institutional view. • Instructor views. • Dataset
3. Prediction using the methods: • Decision tree • Neural network • Naive Bayes • K-Nearest neighbour
4. Decision tree • Simplest one to predict, the small and big data. SIt is easier to convert to the IF-THEN rules.
5. Neural network • predicts regardless of the dependent or independent variable. • This too predicts in a better manner of the psychometric, GPAs in a better way and yields a better accuracy.
6. Nave Bayes • Make a good prediction with comparison about the student's performance.
7. K-Nearest neighbour • produces a good accuracy and it predicts faster than the other algorithms.
8. support vector machine • used for mainly classification, but it predicts too. SIt is also faster than the other techniques.
9. Other tools: • Probabilistic Soft Logic (PSL) • Logistic regression • ID3 * Classification and Regression Tree (CART) algorithm
1 Pag e
STUDENT PARTICIPATION AND PERFORMANCE PREDICTION ANALYSIS 13 Oct 2020
TECHNIQUE DURING ONLINE CLASSES USING DATA MINING
Drawings 2020102708
Figure 1: Overall architecture for gathering information
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113705679A (en) * | 2021-08-30 | 2021-11-26 | 北京工业大学 | Student score prediction method based on hypergraph neural network |
| CN114965441A (en) * | 2022-07-28 | 2022-08-30 | 中国科学院国家天文台 | Training method of element probabilistic prediction model and element probabilistic prediction method |
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2020
- 2020-10-13 AU AU2020102708A patent/AU2020102708A4/en not_active Ceased
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113705679A (en) * | 2021-08-30 | 2021-11-26 | 北京工业大学 | Student score prediction method based on hypergraph neural network |
| CN113705679B (en) * | 2021-08-30 | 2024-05-28 | 北京工业大学 | Student achievement prediction method based on hypergraph neural network |
| CN114965441A (en) * | 2022-07-28 | 2022-08-30 | 中国科学院国家天文台 | Training method of element probabilistic prediction model and element probabilistic prediction method |
| CN114965441B (en) * | 2022-07-28 | 2022-12-06 | 中国科学院国家天文台 | Training method of element probabilistic prediction model and element probabilistic prediction method |
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