Studentss Success Prediction Based on Bayes Algorithms

Hamoud, Alaa Khalaf; Humadi, Aqeel Majeed; Awadh, Wid Akeel; Hashim, Ali Salah

doi:10.2139/ssrn.3080633

Cited by 12 publications

(19 citation statements)

References 5 publications

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“…Our fuzzy evaluation model included 3 factors {SCP, AEP, SAR} and 3 criteria levels {v1, v2, v3} for each factor. Based on Equations (6) - (8), membership functions of factor SCP, AEP, and SAR at 3 criteria levels were established, with factor SCP at level 1 as an example shown in Equation (13). Then, by substituting the student's factor data into the membership functions of each factor at each level, the fuzzy matrix D was obtained for this student, which is shown in Equation (14).…”

Section: Resultsmentioning

confidence: 99%

“…Kris et al [7] used the naive bayes algorithm to predict academic performance based on intelligence, motivation and study habits factors. Madhavi et al [26], Mollica et al [21], and Khalaf et al [13] used the bayes classification to determine student study performance. Martin et al [18] and Kevin et al [14] used a classification system to identify poor performers during current course studying.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Mi¹

2018

IJPE

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As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies is a comprehensive result of various factors, which is characterized by uncertainty. To deal with this issue, fuzzy sets theory and fuzzy logic are advantageous compared with traditional methods. In this study, based on dynamic student study process data, a fuzzy synthetic evaluation method for dynamic early warning student study failure risk is provided. For each student, three specific early warning factors: 1) student course participation, 2) assignment earned points, and 3) student attendance record, are selected as risk indicators, and the overall risk level is determined by a fuzzy synthetic evaluation approach, which can dynamically give the situation of risk as the evaluation time point changes. Finally, our obtained results show that the employed method is good for identifying at-risk students and exploring the risk reasons by showing the degrees of each early warning factors to the overall risk level. It is of significance for educators to timely apply corresponding strategic pedagogical interventions to help at-risk students avoid academic failure.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Mi¹

2018

IJPE

View full text Add to dashboard Cite

show abstract

“…The following criteria were used to evaluate DT performance and determine if the model is suitable for prediction [61,65,68,78]. The true positive (TP) instances represent the correctly predicted cases.…”

Section: Model Evaluationmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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“…The other category is machine learning approaches. For example, decision tree and association rules [13], support vector machine [14], Bayesian algorithm [15], BP artificial neural network [16], genetic algorithm [17], and fuzzy comprehensive evaluation [18] were all applied in the prediction of students' learning performance and identification of at-risk students with learning difficulties. From these, it can be noted that research on learning performance prediction methods has shown a trend of algorithmization and automation.…”

Section: The Research On Prediction Algorithmsmentioning

confidence: 99%

Data-Driven Student Learning Performance Prediction based on RBF Neural Network

Mi¹

2019

IJPE

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With the expansion of college enrollment in recent years, the quality of students' learning is beginning to decline. At present, education quality governance has become the internal demand of the reform and development of higher education. Learning performance prediction is an important means to effectively resolve the academic crisis and improve the overall education quality. In this study, firstly, the current status and problems about learning performance prediction were analyzed from the perspective of basic data, evaluation indicators, and prediction methods. Secondly, driven by ten items of basic learning situation data, a learning performance prediction model based on the RBF neural network was established, which included three layers in network topology: the input layer, hidden layer, and output layer. The activation functions of the hidden layer and output layer were a Gauss radial basis function and linear function, respectively. The modeling process included three steps: forward propagation computing prediction loss, error backward propagation adjusting network parameters, and network optimization determining model hyperparameters. The obtained results showed that the trained model had small relative root mean square error values for both the training data and testing data. When comparing the original observation values and model predicted values, it was observed that most of the sample points were evenly distributed on both sides of the diagonal line of the contrast graph, which indicates that the RBF neural network model employed in this study is promising in learning performance prediction. It is of good reference significance for promoting more accurate and efficient learning performance prediction and improving the efficiency and effectiveness of education quality governance.

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Studentss Success Prediction Based on Bayes Algorithms

Cited by 12 publications

References 5 publications

A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Data-Driven Student Learning Performance Prediction based on RBF Neural Network

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