Student performance prediction using Support Vector Machine and K-Nearest Neighbor

Al-Shehri, Huda; AlQarni, Amani M.; Al-Saati, Leena; Batoaq, Arwa; Badukhen, Haifa; Alrashed, Saleh; Alhiyafi, Jamal; Olatunji, Sunday O.

doi:10.1109/ccece.2017.7946847

Cited by 102 publications

(44 citation statements)

References 4 publications

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“…Specifically, a significant effort has been devoted to training traditional and Deep Neural Networks (e.g., [28,29]). In parallel, established approaches such as Support Vector Machines [30], distance-based classifiers [31], ensembles of classification methods (i.e., Gradient Boosting and Random Forest) [32], and time series forecasting methods [6] have achieved fairly high accuracy values. This paper is, to the best of our knowledge, the first attempt to use associative models to address early student performance prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Student Academic Performance by Means of Associative Classification

et al. 2021

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The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Student Academic Performance by Means of Associative Classification

et al. 2021

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“…There are a lot of classical approaches for classification problems such as Random Forest (RF), AdaBoost, k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) [33][34][35][36][37][38] but to verify the object detection output accuratelly, we propose use of a pre-trained CNN as the classifier. The authors in [17] presented MobileNet as a class of more efficient models for mobile and embedded vision applications.…”

Section: The Verification Step Based On Deep Learning Classifiersmentioning

confidence: 99%

Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

Facta Univ Electron Energ

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Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a visionbased system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not.

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“…The selected characteristics include, among others: past marks, number of school absences, student's willingness to pursue higher education, student age, number of past failures. In [17] the authors compare the results of the two classifiers Support Vector Machine (SVM) and KNN on the data set provided by the University of Minho in Portugal, in order to predict student's final grade. The data set concerns students' mathematical performance and consists of 395 data samples.…”

Section: State-of-the-artmentioning

confidence: 99%

Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

Assielou¹,

Théodore²,

Tra³

et al. 2020

IJACSA

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Current Intelligent Tutoring Systems (ITS) provide better recommendations for students to improve their learning. These recommendations mainly involve students' performance prediction, which remains problematic for ITS, despite the significant improvements made by prediction methods such as Matrix Factorization (MF). The present contribution therefore aims to provide a solution to this prediction problem by proposing an approach that combines Multiple Linear Regression (Modelling Emotional Impact) and a Weighted Multi-Relational Matrix Factorization model to take advantage of both student cognitive and emotional faculties. This approach takes into account not only the relationships that exist between students, tasks and skills, but also students' emotions. Experimental results on a set of pedagogical data collected from 250 students show that our approach significantly improves the results of Student Performance Prediction.

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Student performance prediction using Support Vector Machine and K-Nearest Neighbor

Cited by 102 publications

References 4 publications

Predicting Student Academic Performance by Means of Associative Classification

Predicting Student Academic Performance by Means of Associative Classification

Wind turbine tower detection using feature descriptors and deep learning

Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

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