Intelligent Placement Model Based on Decision Tree

Cai, Congyu; Yan, Ke; Lu, Huijuan; Ye, Minchao

doi:10.1109/itme.2018.00188

Cited by 3 publications

(3 citation statements)

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“…The performance of students can be evaluated from various aspects. Several studies evaluated students in general in terms of student performance [18][19][20][21][22] and some other studies evaluated students for a specific purpose such as academic achievement [23,24], reading ability [25,26], grading [27][28][29], dropout prediction [30][31][32][33], etc. Below, some state-of-the-art research studies have been discussed for each of the mentioned tasks that evaluated the student performance from different aspects.…”

Section: Related Workmentioning

confidence: 99%

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

et al. 2021

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The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

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Section: Related Workmentioning

confidence: 99%

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

et al. 2021

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“…Since the likelihood probability is 100% for the observed state and 0% for the rest, the probability of the state only depends on the transition probability between the state to the observed state if the descendant is a leaf node. Let X denote the input data, x denote the i-th alignment site of X, τ denote the tree topology of Mi+1, Sk represent a residue state of the node under estimation, Sσi(k) and Sσj(k) represent the left and right descendant nodes, P represent the transition probabilities between nucleotide states, θ denote the parameters of the evolutionary model calculating P, a comprehensive pruning algorithm [44][45][46] is applied to calculate the likelihood probability of Sk for each arbitrary internal node:…”

Section: Fig 2 Amentioning

confidence: 99%

“…In order to improve the generalization ability, CART needs to be pruned before it is transformed into final decision rules. The bagging algorithm [44,45] (4). For each split, the optimization target is to search splitting feature and value, i.e.…”

Section: Cart and Baggingmentioning

confidence: 99%

Deep Neighbor Information Learning From Evolution Trees for Phylogenetic Likelihood Estimates

et al. 2020

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Review on Predictive Analysis of Placement of Students Using Machine Learning Algorithms

Agrawal¹,

Kadam²

2023

Advances in Computer Science Research

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Machine learning is an emerging trend which has proven to learn automatically from past data. Machine learning can be performed using various ways like supervised, unsupervised, reinforcement learning. Machine learning where system computer system learning from the data set available. Machine learning using supervised learning can be performed using either regression or classifier algorithms. In this research paper we have worked on machine learning algorithms which are SVM, Decision Tree, Logistic regression and Random Forest. Using Machine learning algorithms we can build mathematical model and make the predictions using dataset. To make prediction we can go for supervised learning where output will be in the form either yes or No, 0 or 1, in this case either student placed or not placed.

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Intelligent Placement Model Based on Decision Tree

Cited by 3 publications

References 0 publications

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

Deep Neighbor Information Learning From Evolution Trees for Phylogenetic Likelihood Estimates

Review on Predictive Analysis of Placement of Students Using Machine Learning Algorithms

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