Educational Data Mining Analysis Using Classification Techniques

Triayudi, Agung; Widyarto, Wahyu Oktri

doi:10.1088/1742-6596/1933/1/012061

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…e final error value can be obtained by calculating the error value of each neuron and the error value of each layer, respectively. See formula (12) for the specific solution formula.…”

Section: Methodsmentioning

confidence: 99%

“…Under the influence of the original family, this paper further discusses the influence of personal character and the psychological status of college students during college and later into society. This paper expounds in detail on the influence and relationship of four factors of character learning school, local family, and society on College Students' mental health [ 12 ]. Usually, we classify personal factors as internal factors affecting college students' mental health, including personal genetic factors, physiological factors, and psychological factors.…”

Section: Related Workmentioning

confidence: 99%

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Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

Pei¹

2022

Computational Intelligence and Neuroscience

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For an ordinary student who has just graduated from high school, interpersonal communication and performance evaluation on campus is also a huge challenge. In order to solve the future and current competition and pressure faced by contemporary college students, many college students have mental health problems. This paper evaluates, predicts, and analyzes the mental health status of contemporary college students based on a neural network algorithm. The computer technology of neural network algorithm is applied to the prediction of contemporary college students’ mental health. Data mining technology based on a neural network algorithm is used to collect data sources. Finally, the prediction results are analyzed, and the main psychological stressor factors of contemporary college students are analyzed by cluster analysis. The results show that there is no significant correlation between college Students’ inferiority complex and dependency map and the incidence of mental diseases and majors. A comprehensive physical symptom test was conducted on individuals to understand students’ psychological characteristics and behavior.

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“…e final error value can be obtained by calculating the error value of each neuron and the error value of each layer, respectively. See formula (12) for the specific solution formula.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

Pei¹

2022

Computational Intelligence and Neuroscience

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“…In this study, data mining techniques are used to find out career placement probabilities in the field of computer programming [10]. The rationale is to anticipate information concerning forthcoming students' career placement probabilities in the programming field that could help them find a better fit in programming careers or not.…”

Section: Research Objectives and Questionsmentioning

confidence: 99%

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Mahboob¹,

Asif²,

Haider³

2023

Mehran Univ. res. j. eng. technol.

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The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.

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“…In opinion mining, there are two types of sentiment classification techniques: machine learning and lexicon-based techniques [9], [10], [11], [12]. This study focuses on Machine Learning approaches in which classification data mining, which is part of Machine Learning techniques [13], [14], [15] which is used for research [16], [17]. Also, Syamala and Nalini [18] have done a number of studies using a random tree and the adaboost technique to analyze the sentiment of Amazon reviews of products.…”

Section: Introductionmentioning

confidence: 99%

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

et al. 2023

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The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

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Educational Data Mining Analysis Using Classification Techniques

Cited by 9 publications

References 8 publications

Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

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