Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Naicker, Nalindren; Adeliyi, Timothy T.; Wing, Jeanette

doi:10.1155/2020/4761468

Cited by 37 publications

(15 citation statements)

References 20 publications

(21 reference statements)

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“…The performance of the SVM models is only sensitive to the samples that are close to the determination surface, so these models usually present good generalizability. In educational settings, Linear SVM is the model used most frequently to predict student learning performance (Naicker et al., 2020). Decision Tree (DT) separates data based on the partitioning mechanisms.…”

Section: Methodsmentioning

confidence: 99%

An Interpretable Pipeline for Identifying At-Risk Students

Pei

Xing

2021

Journal of Educational Computing Research

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This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for maintaining the consecutiveness among learning activities. To demonstrate the efficacy of our model in identifying at-risk students, we compare the weekly AUCs and averaged performance (i.e., accuracy, precision, recall, and f1-score) over each course with the baseline models (i.e., Random Forest, Support Vector Machine, and Decision Tree), respectively. Furthermore, we adopt a Top- K metric to examine the number of at-risk students that the model is able to identify with high precision during each week. Finally, the model output is interpreted through a model-agnostic interpretation approach to support instructors to make informed recommendations for students’ learning. The experimental results demonstrate the capability and interpretability of our model in identifying at-risk students in online learning settings. In addition to that our work also provides significant implications in building accountable machine learning pipelines that can be used to automatically generated individualized learning interventions while considering fairness between different learning groups.

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Section: Methodsmentioning

confidence: 99%

An Interpretable Pipeline for Identifying At-Risk Students

Pei

Xing

2021

Journal of Educational Computing Research

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“…A cubic SVM is an effective SVM approach when dealing with a memory space constraint wherein SVM locates a hyperplane in a multidimensional space that best separates the classes [94], whereas in quadratic SVM, memory utilization is low for binary classification and high for multiclass classification during its training phase. The prediction speed is likewise rapid for binary classification and slow for multiclass classification [95].…”

Section: Comparison To Other Modelsmentioning

confidence: 99%

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Jesus

Senoro

Cruz

et al. 2022

Toxics

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Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10−7 to 0.070276. The GW models recorded a range from 5 × 10−8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling–Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden’s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.

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“…Introduced by Vapnik [40], support vector machines (SVM) have gained attentions of the academic community and have become a preeminent pattern recognition approach [55,[80][81][82][83][84][85][86][87][88][89][90]. Given a data sample set S drawn from a data universe X U , a hidden target function f: X ⟶ 0, 1 { }, we first create a labeled training dataset D, where D � (x, y)|x ∈ S and y � f(x) .…”

Section: Support Vector Machinementioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Cited by 37 publications

References 20 publications

An Interpretable Pipeline for Identifying At-Risk Students

An Interpretable Pipeline for Identifying At-Risk Students

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

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