26,650 research outputs found
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs
Massive Open Online Course (MOOC) systems have become
prevalent in recent years and draw more attention, a.o., due to the coronavirus
pandemic’s impact. However, there is a well-known higher chance
of dropout from MOOCs than from conventional off-line courses. Researchers
have implemented extensive methods to explore the reasons
behind learner attrition or lack of interest to apply timely interventions.
The recent success of neural networks has revolutionised extensive Learning
Analytics (LA) tasks. More recently, the associated deep learning
techniques are increasingly deployed to address the dropout prediction
problem. This survey gives a timely and succinct overview of deep learning
techniques for MOOCs’ learning analytics. We mainly analyse the
trends of feature processing and the model design in dropout prediction,
respectively. Moreover, the recent incremental improvements over
existing deep learning techniques and the commonly used public data
sets have been presented. Finally, the paper proposes three future research
directions in the field: knowledge graphs with learning analytics,
comprehensive social network analysis, composite behavioural analysis
Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach: Student Performance Prediction using Hybrid Approach
Data mining offers effective solutions for a variety of industries, including education. Research in the subject of education is expanding rapidly because of thebigquantityof student data that can be utilized to uncover valuable learning behavior patterns. This research presents a method for forecasting the academic presentation of students in Portuguese as well as math subjects, and it is describing with the help of 33 attributes. Forecasting the educationalattainment of students is the most popular field of study in the modern period. Previous research has employed a variety of categorization algorithms to forecast student performance. Educational data mining is a topic that needs a lot of research to improve the precision of the classification technique and predict how well students will do in school. In this study, we made a method to predict how well a student will do that uses a mix of optimization techniques. BFO and ALO-based popular optimization techniques were applied to the data set. Python was used to process all the files and conduct a performance comparison analysis. In this study, we compared our model's performance with various existing baseline models and examined the accuracy with which the hybrid algorithm predicted the student data set. To verify the expected classification accuracy, a calculation was performed. The experiment's findings indicate that the BFO-ALO Based hybrid model, which, out of all the methods, with a 94.5 percent success rate, is the preferred choice
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