A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques

Marwaha, Anu; Singla, Ashish

doi:10.1007/978-981-13-8618-3_15

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…However, we can see from the Table that only twenty-one (21) studies addressed this. Lastly, only fourteen (14) of the studies acknowledged that the obtained results were verified using a second dataset, on unseen data other than the training and testing sets ([16], [27], [121], [129], [130], [135], [46], [50], [61], [63], [104], [106], [117], [120]), which may imply the generalizability of results.…”

Section: Quality Assessment Resultsmentioning

confidence: 99%

“…Figure 7 presents a bar of the frequency of studies based on the sample size. It is observed that only twenty-six (26) studies used a dataset with more than or equal to 1000 instances to train the predictive models ([13], [16], [58], [65], [68], [69], [80], [89], [90], [99], [107], [112], [27], [119]- [121], [123], [127], [129], [45], [46], [50], [51], [53], [55], [56]). However, it is known that most Machine Learning algorithms require a massive amount of data to perform well and produce accurate prediction results.…”

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

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Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

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Section: Quality Assessment Resultsmentioning

confidence: 99%

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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“…Predictors Outcome [36] 116 features for the production phase (product data) and 84 for the learning phase Team performance [12] 23 factors including academic demographic, social, and behavioral factors with prior semester performance. Student at risk [37] 11 variables include socio-economic background, university entrance examination results, and CGPA.…”

Section: Refmentioning

confidence: 99%

“…In comparison to artificial intelligence computing methods, traditional approaches failed to consistently show the capacity to reach accurate predictions or classifications [9]. As a result, a third approach to predicting student achievement and academic performance indicated in recent literature uses machine learning techniques, such as Artificial Neural Network methods (ANN) and some other techniques including Decision Tree [10], Support Vector Machines [2], Bayesian algorithms [11], and Ensemble Learning [12]. This method has been successfully applied in various fields, including business, engineering, meteorology, and economics, without significant differences in the obtained results or the level of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

et al. 2022

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Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

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“…To perform such a prediction, several educational parameters can be employed to evaluate the performance of students, such as exams grades, Grade Point Average (GPA), lecture absenteeism, number of attempts to pass a course or an exam. Moreover, other demographic features such as gender, family relationship, parent profession, marital status, and personal habits [1,2]. Predicting students' performance for educational organizations has been conducted by many scientific communities.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

et al. 2021

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The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

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A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques

Cited by 12 publications

References 13 publications

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

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