Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Karalar, Halit; Kapucu, Ceyhun; Gürüler, Hüseyin

doi:10.1186/s41239-021-00300-y

Cited by 41 publications

(27 citation statements)

References 47 publications

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“…Many scholars analyzed students' achievements and published insightful data analysis [34][35][36][37]. However, very meager work has been done in simulating the relationship between students' emotions (frustration, stress, etc.)…”

Section: Prior Students' Performance Prediction Approachesmentioning

confidence: 99%

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Ahmad

Aoun

ElAffendi

et al. 2022

Computational Intelligence and Neuroscience

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Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students’ performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students’ emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students’ frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students’ frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students’ performance via two modules. First, frustration is divided into four outer layers. Second, the students’ performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures.

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Section: Prior Students' Performance Prediction Approachesmentioning

confidence: 99%

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Ahmad

Aoun

ElAffendi

et al. 2022

Computational Intelligence and Neuroscience

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“…The predictive performance of this model is evaluated using the accuracy metric. In Karalar et al (2021), the authors proposed a classifier to identify students at-risk of failure during the IJILT 39,5 last pandemic. During the performance evaluation process, only the specificity measure has been used.…”

Section: Related Workmentioning

confidence: 99%

“…The vast majority of existing research works (Bañeres et al. , 2020; Karalar et al. , 2021; Adnan et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, despite the importance of the temporal dimension when assessing such systems, none of the traditional metrics measures the precocity of the correct predictions or reflects the stability of predictions over time. The vast majority of existing research works (Bañeres et al, 2020;Karalar et al, 2021;Adnan et al, 2021;Lee and Chung, 2019;Akçapınar et al, 2019) neglect the temporal dimension and only assess prediction precision statically. Indeed, the use of time-dependent metrics to study systems performance over time is more developed in other research fields (Wang et al, 2019;Nath et al, 2020;Sch€ afer and Leser, 2020;Guidotti and Ruggieri, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Time-dependent metrics to assess performance prediction systems

Soussia

Labba

Roussanaly

et al. 2022

IJILT

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PurposeThe goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners.Design/methodology/approachThe authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the key earliness points in the prediction process. Authors propose an algorithm for computing earliness. Furthermore, the authors propose using an earliness-stability score (ESS) to investigate the relationship between the earliness of a classifier and its stability. The ESS is used to examine the trade-off between only time-dependent metrics. The aim is to compare its use to the earliness-accuracy score (EAS).FindingsStability and accuracy are proportional when the system's accuracy increases or decreases over time. However, when the accuracy stagnates or varies slightly, the system's stability is decreasing rather than stagnating. As a result, the use of ESS and EAS is complementary and allows for a better definition of the point of earliness in time by studying the relation-ship between earliness and accuracy on the one hand and earliness and stability on the other.Originality/valueWhen evaluating the performance of PPS, the temporal dimension is an important factor that is overlooked by traditional measures current metrics are not well suited to assessing PPS’s ability to predict correctly at the earliest, as well as monitoring predictions stability and evolution over time. Thus, in this work, the authors propose time-dependent metrics, including earliness, stability and the trade-offs, with objective to assess PPS over time.

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“…Of course, this condition can lead to failure in achieving International Journal of Intelligent Engineering and Systems, Vol. 16, No. 1,2023 DOI: 10.22266/ijies2023.0228.…”

Section: Introductionmentioning

confidence: 99%

Detecting Students’ Behavior on the E-Learning System Using SVM Kernels - Based Ensemble Learning Algorithm

2023

IJIES

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The corona pandemic has changed learning methods from face-to-face to online. However, the application of online learning creates difficulties for teachers in monitoring student behavior because of the reduced direct interaction. This problem causes the learning process to be less optimal. Moreover, students may fail to achieve learning objectives. This research addresses this problem by building a model to detect student behavior in this online learning. It focuses on finding an optimal model by exploring the ensemble learning-stacking method based on a combination of SVM kernels (Linear, Polynomial, RBF, Sigmoid). After the model was built, it was evaluated using two performance measurement techniques, namely: cross-validation and percentage split, and several performance measures, namely: AUC, Accuracy, F1, Precision, and Recall. The evaluation results show the superiority of the models applying ensemble learning over those without it. In terms of accuracy, the highest result in the cross-validation technique is 98.4%, achieved by three models employing stacking. Those three are with base learners combination of linear-polynomial-sigmoid kernel (LinPolSig_Stack), a combination of linear-RBF-sigmoid kernel (LinRBFSig_Stack), and a combination of all kernels-linear, polynomial, RBF, sigmoid (AllKernels_Stack). In the percentage split technique, the highest performance is 97.4%, achieved by two models implementing ensemble learning-stacking with base-learners combination of RBF-sigmoid kernel (RBFSig_Stack) and combination of linearpolynomial-sigmoid kernel (LinPolSig_Stack). Finally, the highest performance of these models is equivalent to the minimum error in detecting student behavior. Detection errors were only three students in the three models in the cross-validation technique and only six in the two models in the percentage split technique.

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Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Cited by 41 publications

References 47 publications

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Time-dependent metrics to assess performance prediction systems

Detecting Students’ Behavior on the E-Learning System Using SVM Kernels - Based Ensemble Learning Algorithm

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