Performance prediction of students using distributed data mining

Parmar, Krina; Vaghela, Dineshkumar B.; Sharma, Priyanka

doi:10.1109/iciiecs.2015.7192860

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…The accuracy count for each algorithm is shown in Figure 5. [38] 69 69 87 13 [27] 98 99 99 99 59 14 [28] 75 65 82 62 75 15 [16] 67 16 [40] 94 98 83 89 83 17 [55] 65 84 75 18 [56] 63 80 19 [49] 76…”

Section: Most Used Data Mining Algorithms Based On Evaluation Metricsmentioning

confidence: 99%

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Shindo,

Mjahidi,

Waziri

2023

ITLT

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Poor ICT performance in teacher training colleges makes it more difficult for the majority of teachers to successfully use ICT resources in their teaching and learning. When teachers can efficiently utilize ICT resources, it empowers them to update their knowledge through online learning, consequently enhancing the overall quality of teaching and learning. This positive outcome can be observed through improved ICT performance. The aim of this article is to identify the appropriate Data Mining algorithms for predicting student teachers’ performance in ICT. The systematic literature review that was guided by the PRISMA statement 2020 served as the study methodology. It makes for clear reporting and offers a detailed checklist and flow diagram that direct the review procedure. On November 6, 2022, about 196 scholarly articles were downloaded from three digital libraries: Science Direct (38), ACM Digital Library (72), IEEE Xplore (51), and 35 from the Google Scholar search engine. After screening and eligibility checking, 28 scholarly articles were selected and analysed through content analysis in terms of the most commonly used algorithms, the year of publication, the study purposes, and the accuracy performance metrics. Considering the specific study findings represented quantitatively, Decision Trees and Naive Bayes were found to be the most commonly used Data Mining algorithms, with a count of 20.6% each. The most recently identified articles were published between 2014 and 2022. In terms of study purposes, a large number of studies focused on predicting student performance. Furthermore, about 6 out of 8 algorithms used in previous studies were found to score 80% or above in the average percentage of the highest and lowest accuracy metrics. Therefore, considering the general findings, the study identified five Data Mining algorithms as appropriate and most commonly used for prediction of student teachers’ performance in ICT. They are Naive Bayes, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Decision Tree. The findings of this study would assist the government, college tutors, and student teachers in making better decisions to improve ICT performance for pre-service and in-service teachers.

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Section: Most Used Data Mining Algorithms Based On Evaluation Metricsmentioning

confidence: 99%

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Shindo,

Mjahidi,

Waziri

2023

ITLT

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“…Sudah banyak penelitian yang memanfaatkan data mining untuk melakukan prediksi di bidang pendidikan [6], [7], [8]. Sedangkan pemanfaatan association rule mining dalam kegiatan prediksi dilakukan oleh [9] dengan menghasilkan model yang memprediksi tingkat depresi manusia.…”

Section: A Pendahuluanunclassified

“…c = l1 ⨝ l2;// join step: generate candidates (5) if has_infrequent_subset (c,Lk-1) then (6) delete c; // prune step: remove unfruitful candidate (7) else add c to Ck; Adapun perhitungan nilai support dan confidence dapat diperoleh melalui formula berikut [5].…”

Section: Mendeskripsikan Datamentioning

confidence: 99%

Model Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining

Nirad

Kartika

Avianto

et al. 2020

ijcs

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Insternship activity is one of the core activities of every Vocational School (SMK) as the purpose of this school is to conduct education at the level of work-oriented readiness. Every SMK graduate is expected to be better prepared to enter the industrial world. However, in fact there were gaps that resulted in the unpreparedness of students after graduating from school. This research identified and analyzed the placement of student internships. The aim was to find an insternship placement pattern in order to get an overview and recommendation of an appropriate internship according to students abilities. The technique used was the association rule mining, a technique of the data mining method that was useful for uncovering the rules that were correlated to each other so that they can better organize and predict the internship placements. The results showed that the association rule mining could be applied to analyze student performance and predict internship placements in the future. This prediction could be a consideration for the teacher to determine the subjects that need to be improved to prepare students for internships.

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“…mitigating the occurrence of landslides, which frequently lead to the obstruction of river channels [3]. The variability of precipitation is a significant and intricate issue.…”

Section: Introductionmentioning

confidence: 99%

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Hussain,

Tripura,

Aslam

2024

Preprint

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Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

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Performance prediction of students using distributed data mining

Cited by 11 publications

References 5 publications

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Data Mining Algorithms for Prediction of Student Teachers’ Performance in Ict: A Systematic Literature Review

Model Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

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