A Review Paper on Prediction Analysis: Predicting Student Result on the Basis of Past Result

Nandal, Rainu; Dhamija, Priyanka; Sehrawat, Harkesh

doi:10.21817/ijet/2017/v9i2/170902226

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Existing research works based on predicting academic results were reviewed by Dhamija et al (2017). Class behavior, exam result, class attendance and extra-curricular activities were major attributes used in this work.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Academic Result Prediction System

Bhushan,

Verma,

Garg

et al. 2023

International Journal of Software Innovation

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Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the standard of their quality education. Thus, the role of the academic result prediction system comes into existence which uses semester grade point average (SGPA) as a metric. The proposed work aims to create a model that can forecast the SGPA of students based on certain traits. It predicts the result in the form of SGPA of computer science students considering their past academic performance, study, and personal habits during their academic semester using different machine learning models, and to compare them based on different accuracy parameters. Some models that are widely used and are found effective in this field are regression algorithms, classification algorithms, and deep learning techniques. The results conclude that deep learning techniques are the most effective in the proposed work because of their high accuracy and performance, depending upon the attributes used in the prediction.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Academic Result Prediction System

Bhushan,

Verma,

Garg

et al. 2023

International Journal of Software Innovation

View full text Add to dashboard Cite

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“…With the outcomes, the institution can concentrate on what to teach and how to teach. Learning patterns of the students can be captured and used to develop techniques to teach those (Nandal et al, 2017). Among all data mining in higher education is a novel research field and this area of research is gaining popularity because of its potentials to enhance-ment of educational institutes and benefit all the stakeholders in the educational system.…”

Section: Applications Of Data Mining (Rq2)mentioning

confidence: 99%

A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Nanayakkara

Kumara

Rathnayaka

2021

SL J. Soc. Sci. Hum.

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Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.

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