Role of Educational Data Mining in Student Learning Processes With Sentiment Analysis

2020 ◽  
Vol 11 (4) ◽  
pp. 31-44
Author(s):  
Amala Jayanthi M. ◽  
Elizabeth Shanthi I.
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Web Mining ◽  
Educational Data Mining ◽  
Vital Role ◽  
Research Field ◽  
Learning Processes ◽  
Education Students ◽  
Research Fields

Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics.

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

A Comprehensive Guideline for Bengali Sentiment Annotation

2022 ◽  
Vol 21 (2) ◽  
pp. 1-19
Author(s):  
Md. Saddam Hossain Mukta ◽  
Md. Adnanul Islam ◽  
Faisal Ahamed Khan ◽  
Afjal Hossain ◽  
Shuvanon Razik ◽  
...  
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Web Mining ◽  
English Language ◽  
Research Work ◽  
Bengali Language

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

The Future of Instruction Modeling

2020 ◽  
pp. 205-228
Author(s):  
George A. Khachatryan
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Foreign Language ◽  
Natural Language ◽  
Blended Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Government Policies ◽  
Free Form ◽  
Learning Programs

Instruction modeling is still in its early stages. This chapter discusses promising directions in which instruction modeling could develop in coming years. This includes increasing the richness of interfaces used in instruction modeling programs (e.g., by allowing students to enter responses in free form and have them graded via natural language processing); applying instruction modeling to subjects beyond mathematics, including English, foreign language, and science; using educational data mining to create automated “coaches” to help teachers better implement instruction modeling programs in their classrooms; creating approaches to instruction modeling that allow for rapid authorship of content; redesigning schools (in schedules as well as architecture) to optimize the use of instruction modeling; and putting in place government policies to encourage the use of comprehensive blended learning programs (such as those developed through instruction modeling).

Scientific Web Intelligence

2011 ◽  
pp. 995-999
Author(s):  
Mike Thelwall
Keyword(s):  
Data Mining ◽  
End Users ◽  
Research Field ◽  
Data Sources ◽  
Web Pages ◽  
Web Intelligence ◽  
Research Fields ◽  
Patterns Of Communication ◽  
The Impact ◽  
Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, Web intelligence, and scientometrics to extract useful information from the links and text of academic-related Web pages using various clustering, visualization, and counting techniques. Its origins lie in previous scientometric research into mining off-line academic data sources such as journal citation databases. Typical scientometric objectives are either evaluative (assessing the impact of research) or relational (identifying patterns of communication within and among research fields). From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end users, and the causes of the results can be found and explained.

Building Multi-Dimensional Cube to Analyze Public Examination Results: A Business Intelligence Approach

2014 ◽  
Vol 622 ◽  
pp. 11-22
Author(s):  
J. Macklin Abraham Navamani ◽  
A. Kannammal ◽  
P. Ranjit Jeba Thangaiah
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Tamil Nadu ◽  
Educational Data Mining ◽  
Public Examination ◽  
Policy Framing ◽  
Government Organizations ◽  
Research Fields ◽  
Research Activities

In recent years educational data mining and data warehouse has become one of the challenging research fields. It is used to turn the raw data available in educational field into actionable information. Educational data can provide improved understanding of students’ knowledge and better assessments of their progress. Educational data mining and warehousing could help educational government organizations in taking timely and data analysis based management decisions, thus contributing to gain competitive advantages in their successful policy framing. This paper focuses on the research activities for building data warehouse/data mart to store and analyze the public examination results of higher grade students by Directorate of Government Examinations belonging to Tamil Nadu, India. The data warehousing concept comprises of architectures, tools, and algorithms for bringing together data from various sources into a single repository and making it useful for the management to directly query and extract useful information for analysis. In this paper the need of data warehouse / business intelligence for a government educational organization has been explored.

scholarly journals An Insight on Sentiment Analysis Research from Text using Deep Learning Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 2033-2048
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Performance Metrics ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Mixed Emotions ◽  
Automatic Feature Extraction ◽  
Complex Sentences ◽  
Social Network Data

Nowadays, Deep Learning (DL) is a fast growing and most attractive research field in the area of image processing and natural language processing (NLP), which is being adopted across several sectors like medicine, agriculture, commerce and so many other areas as well. This is mainly because of the greater advantages in using DL like automatic feature extraction, capability to process more number of parameters and capacity to generate more accuracy in results. In this paper, we have examined the research works which have used the DL based Sentiment Analysis (SA) for the social network data. This paper provides the brief explanation about the SA, the necessities of the pre-processing of text, performance metrics and the roles of DL models in SA. The main focus of this paper is to explore how the DL algorithms can enhance the performance of SA than the traditional machine learning algorithms for text based analysis. Since DL models are more effective for NLP research, the text classification can be applied on the complex sentences in which there are two inverse emotions which produces the two different emotions about an event. Through this literature appraisal we conclude that by using the Convolutional Neural Network (CNN) technique we can obtain more accuracy than others. The paper also brings to the light that there is no major focus on mixed emotions by using DL methods, which eventually increases the scope for future researches.

scholarly journals Sentiment Analysis using Rapid Miner

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1589-1594
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Quality Information ◽  
Classification Techniques ◽  
Major Task ◽  
Text Document ◽  
Sentence Level ◽  
Day By Day

Now a day the data grows day by day so data mining replaced by big data. Under data mining, Text mining is one of the processes of deriving structured or quality information or data from text document. It helps to business for finding valuable knowledge. Sentiment analysis is one of the applications in text mining. In sentiment analysis, determine the emotional tone under the text. It is the major task of natural language processing. The objective of this paper to categorize the document in sentence level and review level, and classification techniques applied on the dataset (electronic product data). There is an ensemble number of classification techniques applied on the dataset. Then compare each techniques, based on various parameters and find out which one is best. According to that give better suggestions to the company for improving the product.

scholarly journals Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

2020 ◽  
Vol 12 (5) ◽  
pp. 1-13
Author(s):  
Mohammed Abdullah Al-Hagery ◽  
◽  
Maryam Abdullah Alzaid ◽  
Tahani Soud Alharbi ◽  
Moody Abdulrahman Alhanaya
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Study Finding ◽  
Vital Role ◽  
Educational Institutions ◽  
Factors Affecting ◽  
Educational Environments ◽  
Important Field ◽  
Using Data ◽  
Relationship Of

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.

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