scholarly journals Research on incomplete data mining and filling algorithm during depth learning process

2017 ◽  
Author(s):  
Liping Wang
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Incomplete Data ◽  
Depth Learning

scholarly journals A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

2021 ◽  
Vol 13 (4) ◽  
pp. 2141
Author(s):  
Kyungyeul Kim ◽  
Han-Sung Kim ◽  
Jaekwoun Shim ◽  
Ji Su Park
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Technology Literacy ◽  
Early Prediction ◽  
Ict Literacy ◽  
Educational Strategies ◽  
Achievement Level ◽  
Data Mining Techniques ◽  
Educational Backgrounds ◽  
Customized Learning

It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.

Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks

2017 ◽  
pp. 787-825
Author(s):  
Yevgeniy Bodyanskiy ◽  
Olena Vynokurova ◽  
Oleksii Tyshchenko
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Process ◽  
Computational Intelligence ◽  
Group Method ◽  
Learning Criterion ◽  
Adjustment Procedure ◽  
Artificial Neural ◽  
Input Variables

This work is devoted to synthesis of adaptive hybrid systems based on the Computational Intelligence (CI) methods (especially artificial neural networks (ANNs)) and the Group Method of Data Handling (GMDH) ideas to get new qualitative results in Data Mining, Intelligent Control and other scientific areas. The GMDH-artificial neural networks (GMDH-ANNs) are currently well-known. Their nodes are two-input N-Adalines. On the other hand, these ANNs can require a considerable number of hidden layers for a necessary approximation quality. Introduced Q-neurons can provide a higher quality using the quadratic approximation. Their main advantage is a high learning rate. Universal approximating properties of the GMDH-ANNs can be achieved with the help of compartmental R-neurons representing a two-input RBFN with the grid partitioning of the input variables' space. An adjustment procedure of synaptic weights as well as both centers and receptive fields is provided. At the same time, Epanechnikov kernels (their derivatives are linear to adjusted parameters) can be used instead of conventional Gauss functions in order to increase a learning process rate. More complex tasks deal with stochastic time series processing. This kind of tasks can be solved with the help of the introduced adaptive W-neurons (wavelets). Learning algorithms are characterized by both tracking and smoothing properties based on the quadratic learning criterion. Robust algorithms which eliminate an influence of abnormal outliers on the learning process are introduced too. Theoretical results are illustrated by multiple experiments that confirm the proposed approach's effectiveness.

Data Mining with Incomplete Data

2011 ◽  
pp. 293-296 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).

scholarly journals Application of Data Mining Technology in the Subject Tactical Teaching of Badminton

2018 ◽  
Vol 13 (07) ◽  
pp. 30 ◽  
Author(s):  
Delin Yang
Keyword(s):  
Data Mining ◽  
Incomplete Data ◽  
Training Programs ◽  
Point Of View ◽  
Multidimensional Data ◽  
Simulation Experiments ◽  
Mining Technology ◽  
Teaching Plan ◽  
The Subject ◽  
Case Data

In badminton field tactics teaching, there are problems of not timely, inaccu-rate and incomplete data collection of completion cases, not deep enough da-ta and too few personalized training programs for athletes' tactics. Aiming at solving the above problems, the traditional Apriori association algorithm was optimized, and the algorithm was applied to the acquisition of badminton field tactics case data. Moreover, the multidimensional data mining was con-ducted, thus providing a scientific and personalized teaching plan for bad-minton field tactical teaching. The simulation experiments showed that data mining technology applied to badminton field tactics teaching could effec-tively put forward personalized teaching plan for data support from the tacti-cal point of view, and improve the effect of badminton field tactical teach-ing.

scholarly journals Constructed Response or Multiple-Choice Questions for Assessing Declarative Programming Knowledge? That is the Question!

10.28945/4479 ◽  
2019 ◽  
Vol 18 ◽  
pp. 153-170
Author(s):  
Yolanda Belo ◽  
Sérgio Moro ◽  
António Martins ◽  
Pedro Ramos ◽  
Joana Martinho Costa ◽  
...  
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Multiple Choice ◽  
Added Value ◽  
Future Research ◽  
Declarative Programming ◽  
Multiple Choice Questions ◽  
Constructed Response ◽  
Programming Knowledge ◽  
Teaching Learning

Aim/Purpose: This paper presents a data mining approach for analyzing responses to advanced declarative programming questions. The goal of this research is to find a model that can explain the results obtained by students when they perform exams with Constructed Response questions and with equivalent Multiple-Choice Questions. Background: The assessment of acquired knowledge is a fundamental role in the teaching-learning process. It helps to identify the factors that can contribute to the teacher in the developing of pedagogical methods and evaluation tools and it also contributes to the self-regulation process of learning. However, better format of questions to assess declarative programming knowledge is still a subject of ongoing debate. While some research advocates the use of constructed responses, others emphasize the potential of multiple-choice questions. Methodology: A sensitivity analysis was applied to extract useful knowledge from the relevance of the characteristics (i.e., the input variables) used for the data mining process to compute the score. Contribution: Such knowledge helps the teachers to decide which format they must consider with respect to the objectives and expected students results. Findings: The results shown a set of factors that influence the discrepancy between answers in both formats. Recommendations for Practitioners: Teachers can make an informed decision about whether to choose multiple-choice questions or constructed-response taking into account the results of this study. Recommendation for Researchers: In this study a block of exams with CR questions is verified to complement the area of learning, returning greater performance in the evaluation of students and improving the teaching-learning process. Impact on Society: The results of this research confirm the findings of several other researchers that the use of ICT and the application of MCQ is an added value in the evaluation process. In most cases the student is more likely to succeed with MCQ, however if the teacher prefers to evaluate with CR other research approaches are needed. Future Research: Future research must include other question formats.

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