scholarly journals The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongxian Yang
Keyword(s):  
Online Learning ◽  
Decision Tree ◽  
Educational Administration ◽  
Average Score ◽  
Behavioral Characteristic ◽  
Data Set ◽  
Actual Performance ◽  
Mining Algorithm ◽  
Characteristic Data ◽  
Tree Mining

With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students’ academic performance based on the learning behavior data of online learning, so as to intervene in students’ learning in advance, which is the key problem that needs to be solved at present. Taking students’ learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students’ performance, and evaluates students’ performance. Firstly, this paper collects the high-dimensional behavioral characteristic data of students’ online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students’ performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students’ achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students’ achievement, which has a practical reference value for effectively serving online learning and teachers’ teaching.

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

A systematic review of educational leadership and management research in Turkey

2019 ◽  
Vol 57 (6) ◽  
pp. 731-747 ◽  
Author(s):  
Mehmet Şükrü Bellibaş ◽  
Sedat Gümüş
Keyword(s):  
Educational Leadership ◽  
School Improvement ◽  
Mixed Method ◽  
Educational Administration ◽  
Research Approach ◽  
Recent Effort ◽  
Data Set ◽  
Leadership And Management ◽  
Content Type ◽  
School Principalship

Purpose While the current knowledge in the field of educational leadership and management (EDLM) has been primarily based on research produced in English-speaking Western societies, there have been significant efforts by other societies to contribute to the knowledge production, especially during the past decade. The purpose of this paper is to identify the contribution of Turkey to the international EDLM literature by investigating the topical focus, conceptual frameworks and research designs of papers published by EDLM scholars from Turkey. Design/methodology/approach Descriptive content analysis method was employed to examine 315 empirical, review, conceptual and commentary papers published by Turkish scholars in core educational administration and Web of Science journals. The time period of the review left open-ended. However, in practical terms, it begins in the year 1994 when the first article from Turkey was published in any of the selected sources and ends at the end of 2018. Information relevant to the research was extracted from each article and was coded to facilitate quantitative analysis. Using Excel software, descriptive statistics including frequencies and percentages were provided for each research question. Findings Results show that Turkish EDLM scholars mostly rely on survey based quantitative research approach, employing advanced statistical techniques in the analysis of the data. However, mixed method and qualitative studies are relatively less common. Organizational behavior, school leadership and emotions stand out as most frequently used topics, while Turkish scholars are not interested in analyzing the educational outcomes such as student achievement and school improvement. Consistent with the findings related to topical foci, a large number of those who were interested in correlational studies examined the relationship between leadership roles and organizational behaviors. Research limitations/implications The data set only included journal articles and excluded conference proceedings, books and theses/dissertations. Nevertheless, the authors believe this review adds significantly to previous reviews of local EDLM journals conducted by Turkish scholars. The authors concluded that the Turkish scholars should direct their future research to exploring and better understanding the practices of Turkish principals in schools by: diversifying their research topics; incorporating more qualitative and mixed-method designs; and taking into account specific features of the culture and educational system in Turkey. Practical implications Based on the current higher education context, reducing scholars’ teaching load, diversifying research funding opportunities, and modifying access to tenure tracks seem necessary interventions to support EDLM research with strong ties to practice and to the sociocultural context. In addition, policy changes aiming professionalization of administrative positions and establishing some forms of formal training for school principalship are needed. Such changes can help transfer the knowledge produced by the Turkish EDLM researchers to the practice and provide solutions to problems related to school administration. Originality/value This paper will add to recent effort to identify how a developing nation outside Western perspective approaches the field, and contributes to the global knowledge base.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

scholarly journals Contrasting syntactic and semantic units in the analysis of online discussions

2005 ◽  
Vol 21 (4) ◽  
Author(s):  
Elizabeth Murphy ◽  
Justyna Ciszewska-Carr
Keyword(s):  
Online Learning ◽  
Content Analysis ◽  
Online Discussions ◽  
Asynchronous Discussion ◽  
Review Of Literature ◽  
Data Set ◽  
Learning Module ◽  
Units Of Analysis ◽  
Online Asynchronous Discussion

<span>This paper reports on a study which contrasts results obtained using semantic and syntactic units of analysis in a context of content analysis of an online asynchronous discussion. The paper presents a review of literature on both types of units. The data set consisted of 80 messages posted by ten participants in an online learning module. Data were coded twice by two coders working independently. In the first instance, each coder divided all messages into semantic units and then coded those units. The second coding was conducted on the basis of a syntactic unit of a paragraph. Analysis at the level of the whole group showed little difference in results between the two types of coding. At the level of individual participants, those differences were greater. Results are discussed within a framework of reliability, capability of the unit to discriminate between behaviors, feasibility of different units, and their identifiability. Implications for research are discussed.</span>

scholarly journals PCA based Regression Decision Tree Classification for Somatic Mutations.

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1095-1102
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Somatic Mutations ◽  
Feature Selection Technique ◽  
Data Set ◽  
Cancer Data ◽  
Classification Feature ◽  
Decision Tree Classification ◽  
Proposed Model ◽  
Feature Selection Techniques

The analization of cancer data and normal data for the predication of somatic mu-tation occurrences in the data set plays an important role and several challenges persist in detectingsomatic mutations which leads to complexity of handling large volumes of data in classifi-cation with good accuracy. In many situations the dataset may consist of redundant and less significant features and there is a need to remove insignificant features in order to improve the performance of classification. Feature selection techniques are useful for dimensionality reduction purpose. PCA is one type of feature selection technique to identify significant attributes and is adopted in this paper. A novel technique, PCA based regression decision tree is proposed for classification of somatic mutations data in this paper.The performance analysis of this clas-sification process for the detection of somatic mutation is compared with existing algorithms and satisfactory results are obtained with the proposed model.

scholarly journals An Algorithm for Notifiable Disease Modeling and Prediction Using Artificial Intelligence Techniques: A Case of Kenya.

2021 ◽  
Author(s):  
Nicodemus Nzoka Maingi ◽  
Ismail Ateya Lukandu ◽  
Matilu MWAU
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Disease Modeling ◽  
Information Gain ◽  
Disease Outbreak ◽  
Notifiable Disease ◽  
Data Set ◽  
Great Opportunity ◽  
Binary Decision ◽  
Binary Decision Tree

Abstract BackgroundThe disease outbreak management operations of most countries (notably Kenya) present numerous novel ideas of how to best make use of notifiable disease data to effect proactive interventions. Notifiable disease data is reported, aggregated and variously consumed. Over the years, there has been a deluge of notifiable disease data and the challenge for notifiable disease data management entities has been how to objectively and dynamically aggregate such data in a manner such as to enable the efficient consumption to inform relevant mitigation measures. Various models have been explored, tried and tested with varying results; some purely mathematical and statistical, others quasi-mathematical cum software model-driven.MethodsOne of the tools that has been explored is Artificial Intelligence (AI). AI is a technique that enables computers to intelligently perform and mimic actions and tasks usually reserved for human experts. AI presents a great opportunity for redefining how the data is more meaningfully processed and packaged. This research explores AI’s Machine Learning (ML) theory as a differentiator in the crunching of notifiable disease data and adding perspective. An algorithm has been designed to test different notifiable disease outbreak data cases, a shift to managing disease outbreaks via the symptoms they generally manifest. Each notifiable disease is broken down into a set of symptoms, dubbed symptom burden variables, and consequently categorized into eight clusters: Bodily, Gastro-Intestinal, Muscular, Nasal, Pain, Respiratory, Skin, and finally, Other Symptom Clusters. ML’s decision tree theory has been utilized in the determination of the entropies and information gains of each symptom cluster based on select test data sets.ResultsOnce the entropies and information gains have been determined, the information gain variables are then ranked in descending order; from the variables with the highest information gains to those with the lowest, thereby giving a clear-cut criteria of how the variables are ordered. The ranked variables are then utilized in the construction of a binary decision tree, which graphically and structurally represents the variables. Should any variables have a tie in the information gain rankings, such are given equal importance in the construction of the binary decision-tree. From the presented data, the computed information gains are ordered as; Gastro-Intestinal, Bodily, Pain, Skin, Respiratory, Others. Muscular, and finally Nasal Symptoms respectively. The corresponding binary decision tree is then constructed.ConclusionsThe algorithm successfully singles out the disease burden variable(s) that are most critical as the point of diagnostic focus to enable the relevant authorities take the necessary, informed interventions. This algorithm provides a good basis for a country’s localized diagnostic activities driven by data from the reported notifiable disease cases. The algorithm presents a dynamic mechanism that can be used to analyze and aggregate any notifiable disease data set, meaning that the algorithm is not fixated or locked on any particular data set.

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