Mining Smart Learning Analytics Data Using Ensemble Classifiers

Kausar, Samina; Oyelere, Solomon Sunday; Salal, Y K; Hussain, Sadiq; Çifçi, Mehmet Akif; Hilčenko, Slavoljub; Iqbal, Muhammad; Zhu, Wenhao; Xu, Huahu

doi:10.3991/ijet.v15i12.13455

Cited by 33 publications

(17 citation statements)

References 24 publications

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“…Existing literatures have explored the ensemble methods in education issues. Kausar et al [12] utilized the ensemble methods to analyze the students' learning performance, and demonstrated that the ensemble methods of bagging and stacking classifiers can enhance the classification performance. Sun et al [13] proposed a multi-classification model, by combining the feature integration and the ensemble method to predict the education grants of students.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

Zhang¹,

Chen²,

Zhang³

et al. 2021

SJEDU

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With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students' achievements and other factors. In this study, the data set about the students' achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students' achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.

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Section: Ensemble Methodsmentioning

confidence: 99%

Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

Zhang¹,

Chen²,

Zhang³

et al. 2021

SJEDU

View full text Add to dashboard Cite

show abstract

“…Ghosh et al [34] made use of lazy algorithms to identify the student vulnerable of failing mathematics courses and forward the information to the corresponding instructors. Kausar et al [35] made use of ensemble techniques to examine the relationship between students' semester course and final results. Hussain et al [36] concludes decision tree as a robust solution in successfully recognizing the students who truly exhibit low-engagement during assessment activities.…”

Section: Machine Learningmentioning

confidence: 99%

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

Khan

Ahmad

Jabeur

et al. 2021

Int. J. Interact. Mob. Technol.

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One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.

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“…Kausar et al [25] applied ensemble techniques over a dataset to examine the relationship between students' features such as quizzes, assignment, exams marks and the final results. The experimental evaluation concludes Random Forest and Stacking classifiers with achieving accuracies of 77% and 78% respectively prevailing K-NN and Naïve bayes classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course

Khan

Ahmad

Jabeur

et al. 2021

Int. J. Emerg. Technol. Learn.

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The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.

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Mining Smart Learning Analytics Data Using Ensemble Classifiers

Cited by 33 publications

References 24 publications

Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course

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