Students' Online Behavior Patterns Impact on Final Grades Prediction in Blended Courses

Luo, Yangyang; Chen, Nan; Han, Xiao

doi:10.1109/eitt50754.2020.00034

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Blending learning has been studied as well, Y. Luo et al [17] utilized the LMS data and the administrative system to predict learning outcome. Likewise, the authors of [18] executed random forests algorithm to anticipate whether students will obtain a bachelor's degree.…”

Section: Related Workmentioning

confidence: 99%

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

Alnawas

Al-Jawad

Alharbi

2022

jeasd

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E-Learning has become an essential teaching approach during the COVID-19 pandemic. All over the world, various internet-based learning management systems (Google classroom, Moodle, etc.) were adopted to convey knowledge and enhance learning outcomes. However, measuring learning outcomes and knowledge acquisition in E-Learning environment is a controversial issue. To this end, this paper aims to predict learning outcomes using data mining techniques. Student data are collected and analyzed to construct the prediction model. The collected data covered students from various undergraduate studies. Cross-Industry Standard Process for Data Mining is used as a research model. The obtained result shows the significant of some attributes in predicting learning outcomes. Four correlation-based attributes selection schemas are applied. The selected attributes are examined using four data mining algorithms: random forest, k-nearest neighbors, Decision Tree, and neural network. The overall performance of the constructed mining models is evaluated using various performance measures: Accuracy, Precision, Recall and F1-score are calculated. Overall, an 86% accuracy is secured.

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Section: Related Workmentioning

confidence: 99%

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

Alnawas

Al-Jawad

Alharbi

2022

jeasd

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“…The study's prediction showcased the students who failed to make the grade. Luo et al [83] examined the impact of students' online behavior patterns on the final grades prediction in blended courses. They designed a predictive model that brought insight into the process of final grades.…”

Section: Introductionmentioning

confidence: 99%

Exploring Online Activities to Predict the Final Grade of Student

et al. 2022

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Student success rate is a significant indicator of the quality of the educational services offered at higher education institutions (HEIs). It allows students to make their plans to achieve the set goals and helps teachers to identify the at-risk students and make timely interventions. University decision-makers need reliable data on student success rates to formulate specific and coherent decisions to improve students’ academic performance. In recent years, EDM has become an effective tool for exploring data from student activities to predict their final grades. This study presents a case study for predicting the students’ final grades based on their activities in Moodle Learning Management System (LMS) and attendance in online lectures conducted via Zoom by applying statistical and machine learning techniques. The data set consists of the final grades for 105 students who study Object-Oriented Programming at the University of Plovdiv during the 2021–2022 year, data for their activities in the online course (7057 records), and attendance to lectures (738). The predictions are based on 46 attributes. The Chi-square test is utilized to assess the association between students’ final grades and event context (lectures, source code, exercise, and assignment) and the relationships between attendance at lectures and final results. The logistic regression model is utilized to assess the actual impact of event context on “Fail” students in a multivariate setup. Four machine learning algorithms (Random Forest, XGBoost, KNN, and SVM) are applied using 70% of training data and 30% of test data to predict the students’ final grades. Five-fold cross validation was also utilized. The results show correlations between the students’ final grades and their activity in the online course and between students’ final grades and attendance at lectures. All applied machine learning algorithms performed moderately well predicting the students’ final results, as the Random Forest algorithm obtained the highest prediction accuracy—78%. The findings of the study clearly show that the Random Forest algorithm may be used to predict which students will fail after eight weeks. Such data-driven predictions are significant for teachers and decision-makers and allow them to take measures to reduce the number of failed students and identify which types of learning resources or student activities are better predictors of the student’s academic performance.

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Student Behavior Analysis and Performance Prediction Based on Blended Learning Data

Chen

Fan

Jia

et al. 2022

Knowledge Science, Engineering and Management

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Students' Online Behavior Patterns Impact on Final Grades Prediction in Blended Courses

Cited by 4 publications

References 16 publications

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

Exploring Online Activities to Predict the Final Grade of Student

Student Behavior Analysis and Performance Prediction Based on Blended Learning Data

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