scholarly journals Methods of teaching educational data mining for pedagogical students

2019 ◽  
Vol 23 (3) ◽  
pp. 14-24
Author(s):  
E. A. Terbusheva
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Statistical Processing ◽  
Educational Process ◽  
Middle Level ◽  
Training Tool ◽  
Research Competence ◽  
Level Data ◽  
The Government

The aim of the article is to discuss and argue teaching educational data mining for pedagogical stu-dents and to describe the methodical system of educational data mining teaching for students with a middle level of mathematical and IT disciplines, that contributes to the development of student’s research competence. The relevance of the study is determined by the requirements for the ability of higher education graduates to analyze information and perform research using modern methods and technologies that are mentioned in the educational standards and the government order. They are associated with an increasing amount of accumulated data in various fields and the cost of the knowledge extracted from data. Materials and methods. The article describes the author’s methodical system of educational data mining teaching, which was developed rely on: analysis of requirements and expectations to the re-search competence level, data analysis skills and modern education in general; comparison and analysis of the content of educational programs, books and courses on data mining and related dis-ciplines, generalization of pedagogical experience. The main aspects underlying the methodology: a form of flipped learning, a concentric (iterative) content structure, research teaching methods, a set of practical tasks for developing research competencies and Weka software for data mining as the main technical training tool for practical tasks implementation. The effectiveness of the developed methodological system was tested by the educational process monitoring, students questioning and statistical processing of questionnaires data. Results. The study shows the relevance of educational data mining teaching for students of peda-gogical universities, studying in mathematical and informational specialization. The use of the de-scribed methodic system for senior pedagogical students allows increasing the level of research competence of students and significantly developing the competence of data analysis. Conclusion. The described methodical system can used be partially or completely by teachers and methodologists for teaching data analysis at the modern level and development of research compe-tence of students with an average level of knowledge in mathematical and IT disciplines. 

Expert survey and analysis of the results

2019 ◽  
Vol 85 (7) ◽  
pp. 73-82
Author(s):  
Vladimir O. Tolcheev
Keyword(s):  
Data Analysis ◽  
Research University ◽  
Statistical Processing ◽  
Educational Process ◽  
Expert Survey ◽  
Educational Modules ◽  
Training Modules ◽  
Engineering Institute ◽  
Good Agreement ◽  
Statistical Criteria

The issues of organizing an expert survey and carrying out statistical processing and analysis of the results are considered. The experts are the fifth-year students undergoing training at the Department of Management and Informatics «Moscow Power Engineering Institute» of the National Research University. The goal of the survey is revealing the disciplines that are most useful for employment in their specialty. We discuss the special features of the survey and a concept of «work in the specialty», with due regard for statistical reliability of the results. Data of written questionnaire gained in 2018 were processed and analyzed using cluster analysis (construction of dendrograms and application of the K-means method) and non-parametric statistical criteria (Friedman and Mann – Whitney – Wilcoxon). Data processing is implemented in the program STATISTICA. The analysis is carried out to reveal significant differences between the educational courses and assess the degree of consistency of the respondents to divide them into clusters that unite the students with similar judgments. Data analysis revealed that experts’ estimates in 2018 are in fairly good agreement with the estimates of previous studies; among the respondents there are three coalitions corresponding to the training modules «Software», «Management Theory», «Data Analysis»; the overall consistency of students in the two groups is very low (and, on the contrary, high in the identified clusters); grades are homogeneous and do not depend on training groups (and employment – unemployment of the respondents). The obtained results allow us to address a number of important questions regarding the ways of improving the educational process, e.g., to optimize yearly course hours for different educational modules.

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

scholarly journals Tools for Educational Data Mining

2016 ◽  
Vol 42 (1) ◽  
pp. 85-106 ◽  
Author(s):  
Stefan Slater ◽  
Srećko Joksimović ◽  
Vitomir Kovanovic ◽  
Ryan S. Baker ◽  
Dragan Gasevic
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Data Analysis ◽  
Text Analysis ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
User Friendliness ◽  
Descriptive Information ◽  
Price Point ◽  
Knowledge Tracing

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.

scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses

2019 ◽  
Vol 28 (04) ◽  
pp. 1940001 ◽  
Author(s):  
Georgios Kostopoulos ◽  
Sotiris Kotsiantis ◽  
Nikos Fazakis ◽  
Giannis Koutsonikos ◽  
Christos Pierrakeas
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Educational Data Mining ◽  
Predictive Performance ◽  
Educational Process ◽  
Training Set ◽  
Final Grade ◽  
Future Outcomes ◽  
Educational Field

Applying data mining methods in the educational field has gained a lot of attention among researchers in recent years. Educational Data Mining has turned into an effective tool for uncovering hidden relationships in educational data and predicting students’ learning outcomes. Several supervised methods have been successfully applied with the purpose of identifying students at risk of failing or of predicting their academic performance. Recently, the implementation of Semi-Supervised Learning (SSL) methods in the educational process indicated their superiority over the supervised ones. SSL is an emerging subfield of machine learning seeking to effectively exploit a small pool of labeled examples together with a large pool of unlabeled ones. On this basis, a small number of students’ data from previous years may be used as the training set of a learning model to predict future outcomes of current students. A number of rewarding studies deal with the implementation of classification methods in the educational field in contrast to regression, which is deemed to be a slightly touched task. In this paper, a novel semi-supervised regression (SSR) algorithm is presented for predicting the final grade of undergraduate students in a distance online course. To the best of our knowledge there is no study dealing with the implementation of SSR methods in the educational field. A plethora of attributes related to students’ characteristics, academic performance and interaction within the course online platform form the training set, while several experiments were carried out confirming the superiority of the proposed algorithm over familiar regression methods. The experiment results show that the predictive performance of the proposed algorithm is increasing significantly over time, achieving a MAE value of less than 1.2358 before the middle of the academic year, which provides the advantage of early warnings and interventions.

scholarly journals Analysis of Student's Data using Rapid Miner

2016 ◽  
Vol 4 (2) ◽  
pp. 109-117
Author(s):  
Sheena Angra ◽  
Sachin Ahuja
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Research Area ◽  
Quality Of Education ◽  
Improve Quality ◽  
Data Mining Techniques ◽  
Interesting Learning

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

Data Mining and its Application in Animal Husbandry Management System

2014 ◽  
Vol 926-930 ◽  
pp. 2525-2528
Author(s):  
Hui Wang ◽  
Xiao Ge Li ◽  
Tai Yu Liu ◽  
Qian Wu
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Large Scale ◽  
New Technologies ◽  
Rapid Development ◽  
Animal Husbandry ◽  
Management Tools ◽  
Management Level ◽  
The Government

Data will be an important resource, including Animal Husbandry, including many of the industry, and even determine the success or failure of an industry. Animal Husbandry management is the behavior of large amount of data, improve the management level of all walks of life requires a lot of long-term data analysis and preparation. The rapid development of China's Animal Husbandry ,but the level of information to stay in the primary stage, the further development of Animal Husbandry, the information necessary to overcome this problem. In this paper, data mining, data warehouse and other new technologies in Animal Husbandry management, large-scale farming, and the government has brought the latest regional regulatory and other management tools and data analysis for the industry to promote mining.

scholarly journals USING EDUCATIONAL DATA MINING IN ASSESSMENT OF STUDENT ACHIEVEMENT

2020 ◽  
Vol 8 (48) ◽  
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Alternative Assessment ◽  
Peer Assessment ◽  
Student Assessment ◽  
Educational Data Mining ◽  
Large Data ◽  
Educational Process ◽  
Assessment Instruments ◽  
Assessment And Evaluation

Education is the deliberate enculturation process in general. The concept of deliberation here emphasizes a program that does not leave expectations to coincidences and thus excludes unwanted situations. No matter how accurately and effectively this program is organized, quality control is still carried out at the end of the process with assessment and evaluation processes. The assessment and evaluation processes in education provide feedback in terms of the effectiveness of both the student and the program. This would also lead to an effective reorganization of the process. One of the problems faced during the transition from product or outcome based student assessment approaches to process-based alternative assessment approaches is the difficulty in evaluating the student data collected by more than one alternative assessment instruments. Using all the data about the student in determining the academic achievement of students affects the success of process assessment approach positively. Educational Data Mining is the computer aided search of the relations and rules that enable us to make predictions about the present and the future through the use of the massive amount of data concerning the educational process obtained from various sources. With this process, patterns, similarities and correlations that are in a large data warehouse can be determined and interpreted by using any of pattern recognition methods. Through enabling holistic evaluation of data obtained by process evaluation oriented assessment instruments such as portfolio, rubrics, self and peer assessment, performance assessment etc. it will be possible to obtain the relations concerning not only students’ academic achievement but also students, teachers, schools and courses. Keywords: Assessment, alternative assessment, data mining, educational data mining

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