Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning

Nespereira, Celia González; Elhariri, Esraa; El-Bendary, Nashwa; Vilas, Ana Fernández; Redondo, Rebeca P. Díaz

doi:10.1007/978-3-319-26690-9_5

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…A different focus on students' performance can be found in [20], where the main characteristics for observing performance are deduced from students' daily interaction events with certain modules of Moodle. For this purpose, RF and SVM developed the prediction models, and the best results were obtained by RF.…”

Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

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Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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“…Concurrently, machine-learning based methodologies have shown great potential for pattern recognition and predicting results for multiple types of datasets, in spite of the field using supervised algorithms for most of these works. The results of these methods can be incorporated into the decision-making process [19], even for strategic decision making at higher educational institutions [20], predicting the performance of the students in blended learning [21] or prediction of early dropout [22].…”

Section: Cfd Methods In the Learning Contextmentioning

confidence: 99%

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

et al. 2019

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Simulation activities are a useful tool to improve competence in industrial engineering bachelors. Specifically, fluid simulation allows students to acquire important skills to strengthen their theoretical knowledge and improve their future professional career. However, these tools usually require long training times and they are usually not available in the subjects of B.Sc. degrees. In this article, a new methodology based on short lessons is raised and evaluated in the fluid-mechanical subject for students enrolled in three different bachelor degree groups: B.Sc. in Mechanical Engineering, B.Sc. in Electrical Engineering and B.Sc. in Electronic and Automatic Engineering. Statistical results show a good acceptance in terms of usability, learning, motivation, thinking over, satisfaction and scalability. Additionally, a machine-learning based approach was applied to find group peculiarities and differences among them in order to identify the need for further personalization of the learning activity.

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“…Few studies have used LMS-generated data to predict student achievement. Attributes include the frequency of interaction of a student with each module on LMS [21], LMS log data, counts of hits, forum post details, counts of assessments viewed and submitted on LMS [11], start and end dates and assessment submission dates [20]. LMS data are automatically generated and stored by the LMS, which is cost-effective, and the data are accessible and relatively easy to analyze.…”

Section: Important Attributes For Predicting Student Academic Performancementioning

confidence: 99%

“…Most research datasets have been acquired from traditional face-to-face or online classroom settings [19], although several studies have used datasets obtained from BL [11,21]. The BL approach combines traditional classroom environments with online learning, starting from a 10%-25% digital to 90%-75% classroom ratio, to the reverse situation, a 75%-90% digital component.…”

Section: Important Attributes For Predicting Student Academic Performancementioning

confidence: 99%

Predicting student performance in a blended learning environment using learning management system interaction data

Fahd

Miah

Ahmed

2021

ACI

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PurposeStudent attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.Design/methodology/approachThis study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.FindingsIdentifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.Originality/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

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Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning

Cited by 21 publications

References 7 publications

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

Predicting student performance in a blended learning environment using learning management system interaction data

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