Comparative Study of KNN, SVM and Decision Tree Algorithm for Student’s Performance Prediction

Wiyono, Slamet; Wibowo, Dega Surono; Hidayatullah, M. Fikri; Dairoh, Dairoh

doi:10.12962/j24775401.v6i2.4360

Cited by 22 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We constructed these models to perform separate regression tasks to predict the degree of informational, emotional, and community support. In this study, we have examined three traditional machine learning (ML) models that have been widely used in educational research and have shown promising results: Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) (eg, Hasan et al, 2020; Rizvi et al, 2019; Wiyono et al, 2020). We also examined a state‐of‐the‐art deep learning model, Bidirectional Encoder Representations from Transformers (BERT, Delvin et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation

Xing

Leite

2022

Brit J Educational Tech

View full text Add to dashboard Cite

A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention to the safety of CAs. This study aimed to address the safety challenges of CAs constructed with educational big data to support learning. Specifically, we proposed a safety‐aware CA model, benchmarked with two state‐of‐the‐art (SOTA) models, to support high school student learning in an online algebra learning platform. We applied automatic text analysis to evaluate the safety and socio‐emotional support levels of CA‐generated and human‐generated texts. A large dataset was used to train and evaluate the CA models, which consisted of all discussion post‐reply pairs (n = 2,097,139) by 71,918 online math learners from 2015 to 2021. Results show that while SOTA models can generate supportive texts, their safety is compromised. Meanwhile, our proposed model can effectively enhance the safety of generated texts while providing comparable support. What is already known about this topic Online discussion forums have been plagued by a lack of interaction among students due to factors such as expectations to receive no response and perceptions of topic irrelevance which lead to low motivation to participate. AI‐based conversational agents can automatically support students' interactions in online discussion forums at a large scale, and their generated responses can be human‐like, contextually coherent and socio‐emotionally supportive. Unsafe discourse exchanges between students and conversational agents can be dangerous as identity attacks, aggravation and bullying behaviours embedded in discourses can disrupt students' knowledge inquiry and negatively influence student motivation and engagement. However, few educational studies have paid attention to the safety of conversational agents. What this paper adds This study proposes and synthesized strategies to build AI‐based conversational agents that automatically support online discussions with safe and supportive discourses. This study reveals the relationship between discourse safety and social support, suggesting supportive discourses can also be unsafe. This study enriches the literature on educational conversational agents by synthesizing a conceptual framework on discourse safety and social support, and by proposing concrete algorithmic strategies to improve the safety of conversational agents. Implications for practice and/or policy Researchers and practitioners can adopt strategies in this study such as generation control, open‐sourced models and public API services to evaluate students' discourse safety for early intervention or modify existing conversational agents to be safety‐aware. Practitioners can utilize...

show abstract

Section: Methodsmentioning

confidence: 99%

Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation

Xing

Leite

2022

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…This is indicated by the implementation of this task in solving problems in various fields, including the health sector [21], the economy sector [22], the data security sector [23], the education sector [24], etc. Various methods are exploited for this task, including: SVM [6,11,[25][26], K-NN [5] [10], Random Tree [27], etc.…”

Section: Related Work 21 Classificationmentioning

confidence: 99%

“…On the other hand, many transactions via the internet in the field of education have an impact on the amount of data stored, one of which is student data. Some researchers mine this data to overcome various problems in the field of education, including identifying students' academic performance [5], predicting student performance [6], predicting graduation times [7], and mapping students' behavior in the e-Learning system [8], etc. Various methods are applied for this purpose, for example, KNN [9][10] , Decision Tree [6] , SVM [11] , K-Means [12], and Fuzzy C Means [13].…”

Section: Introductionmentioning

confidence: 99%

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

2023

IJIES

View full text Add to dashboard Cite

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.

show abstract

“…Algoritma SVM memiliki nilai kesalahan yang lebih kecil dibandingkan dengan regresi logistik biner sehingga SVM dapat digunakan pada proses klasifikasi kelulusan mahasiswa tepat waktu FMIPA UNTAD. Penelitian tentang penggunaan SVM juga dilakukan oleh [12] pada penelitian ini menggunakan algoritma SVM dibandingkan dengan dua algoritma lainnya yaitu KNN dan decision tree dalam mendapatkan model terbaik untuk memprediksi mahasiswa agar lulus tepat waktu. Berdasarkan hasil pengujian didapatkan bahwa SVM memberikan nilai akurasi yang paling tinggi yaitu sebesar 95% sedangkan KNN memberikan akurasi sebesar 92% dan decision tree memberikan akurasi sebesar 93% berdasarkan dari data yang digunakan.…”

Section: Pendahuluanunclassified

Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa

Bangun

Mawengkang²,

Efendi³

2022

mib

View full text Add to dashboard Cite

The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%

show abstract

Comparative Study of KNN, SVM and Decision Tree Algorithm for Student’s Performance Prediction

Cited by 22 publications

References 14 publications

Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation

Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation

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

Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa

Contact Info

Product

Resources

About