Using Decision Trees and Random Forest Algorithms to Predict and Determine Factors Contributing to First-Year University Students’ Learning Performance

Huynh-Cam, Thao-Trang; Chen, Long-Sheng; Le, Huynh M.

doi:10.3390/a14110318

Cited by 23 publications

(16 citation statements)

References 39 publications

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“…Student ability to learn (SL), student knowledge (SK), video conference application (VC), students' perceptions, attitudes toward VC, intention to use VC, and actual use of VC were considered for analysis when attempting to identify and predict students' academic performance in VCAOL during Covid-19 [3][4][5]. Our nding lled the gap while several previous studies have investigated RF, SVM, and GNB for academic prediction in students, generating better results than ours, but they did not develop model-agnostic ML to evaluate classi cation models for comprehending predictive features [11][12][13][14][15][16][17].…”

Section: Discussionmentioning

confidence: 83%

“…Thao-Trang Huynh-Cam [13] RF, C5O, CART, and multilayer perceptron (MLP) algorithms. CART outperforms C5O, RF, and MLP algorithms with accuracy 80%.…”

Section: Related Workmentioning

confidence: 99%

“…Many previous studies focused on developing prediction models and evaluating results using the ML technique, with little attention focused on comprehending classi cation models for understanding predictive features [6][7][8][9][10][11][12][13][14][15][16][17][18]. Understanding the black-box output of a machine-learning model was crucial for computing and examining the in uence of features on individual and overall predictions, as well as evaluating useful features and investigating their interpretability and characteristics.…”

Section: Barnabásmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

Miranda,

Aryuni,

Rahmawati

et al. 2023

Preprint

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The Covid-19 pandemic had an immediate impact on higher education. Although online technology has made contributions to higher education, its adoption has had a significant impact on learning activities during the Covid-19 pandemic. This paper proposed a predictive model for predicting students’ academic performance in video-conference-assisted online learning (VCAOL) during Covid-19 pandemic based on machine learning approach. We investigated: Random Forest (RF), Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). There were 361 data gathered as a dataset from September 2022 to January 2023. The overall result revealed RF outperformed SVM and GNB with accuracy score of 60.27%, precision 59.46%, recall 60.27%, F1-score 59.51% and ROC AUC 87%. Understanding a machine learning model's black-box output was crucial for providing predictions that explain why and how they were developed. SHAP value of global interpretability to visualize global feature importance revealed that students' performance while using VCAOL (Performance) was the most critical attribute for predicting students' academic performance. The SHAP local interpretability bar plot revealed that ‘student academic performance was still well achieved during the learning process, despite using video conferencing during the Covid-19 pandemic’ (Performance), when Performance decreased it contributed negative impact on students’ academic performance in VCAOL during Covid-19.

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Section: Discussionmentioning

confidence: 83%

“…Thao-Trang Huynh-Cam [13] RF, C5O, CART, and multilayer perceptron (MLP) algorithms. CART outperforms C5O, RF, and MLP algorithms with accuracy 80%.…”

Section: Related Workmentioning

confidence: 99%

Section: Barnabásmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

Miranda,

Aryuni,

Rahmawati

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, Ref. [20] used family background variables, and [21] used data from the interaction of the students to build predictive models of student learning performance. Moreover, Ref.…”

Section: Related Workmentioning

confidence: 99%

Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

et al. 2022

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The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university.

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“…The members of the result set become more similar to one another with each division series. The members of the result set become more similar to one another with each division series [7][8][9]. The C4.5 method is a decision tree formation technique that calculates the gain value, with the highest gain serving as the first node or root node [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

Muhasshanah¹,

Tohir²,

Ningsih³

et al. 2023

CommIT (Communication and Information Technology) Journal

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Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.

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Using Decision Trees and Random Forest Algorithms to Predict and Determine Factors Contributing to First-Year University Students’ Learning Performance

Cited by 23 publications

References 39 publications

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

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