An enhanced bayesian network model for prediction of students' academic performance in engineering programs

Sharabiani, Ashkan; Karim, Fazle; Sharabiani, Anooshiravan; Atanasov, Mariya; Darabi, Houshang

doi:10.1109/educon.2014.6826192

Cited by 43 publications

(17 citation statements)

References 21 publications

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“…Personal information parameters are used in [1], [3],[4], [7], [8], [9], [10], [11], [12], [16], [17], [20], [22], [23], [ 24], [31], [32], [33]. These include physical characteristics (such as gender, age, disability, race), extra-curricular activities, stress management, religion, etc.…”

Section: Personal Information Parametersmentioning

confidence: 99%

Academic Performance Predictors

Liu

2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops

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The ability to predict one's academic performance is a great asset for both the students and the institution administrators. For the students, they can adjust workload, career direction, etc. if they are aware of their capability. For the administrators and instructors, early warnings would facilitate intervention thus enabling a more successful academic environment. In addition, institutional resources can be utilized in an optimal way thus gaining operation efficiency. This work surveys existing literature in student academic performance prediction. Parameters used for predictions are examined. Useful predictors are identified.

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Section: Personal Information Parametersmentioning

confidence: 99%

Academic Performance Predictors

Liu

2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops

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“…The second phase using Bayesian estimation to combine a few direct of sample evidence, and the last phase is the posterior distribution to calculate the student final marks. From the above studies, it can be concluded that researchers has been using several different methods to predict best accuracy of student performance such as early prediction [25], [6], [34], final prediction [2], [20] and novel predictive method [33].…”

Section: Methodology On Student Academic Performancementioning

confidence: 99%

“…This could gives valuable information for educational organization to improve quality of services and make insightful direction on decision making [9]. For instructor, the information will offer huge opportunities to improve their quality of teaching [2]. Accurate prediction of student performance will be helpful in order to provide a guidance in learning process [10] and will benefit to student in taking positive steps and avoiding poor performance in their scores [8].…”

Section: Student Academic Performancementioning

confidence: 99%

“…The performance prediction model can be built by applying data mining technique to an available collected data such as student CGPA / GPA and other variables [22]. The previous work on student performance prediction used methods such as Bayesian Network [2], [10], [22]- [27], Decision Tree [6], [9], [22], [28], Support Vector Machine [1], [8], regression [29] [39] and others. Some of the models had categorized students into a few categories such as below satisfactory, satisfactory, and above satisfactory.…”

Section: Performance Predictionmentioning

confidence: 99%

“…The main purpose of this study is to review current research activities related to academic analytics focusing on predicting student academic performance. Student academic performance provides valuable information for educational authorities which offers diverse opportunities for decision making to and assisting the students to achieve a great performance in their studies [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

et al. 2019

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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.

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A data‐mining‐based approach to informed decision‐making in engineering education

Yahya

Osman

2019

Comp Applic In Engineering

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In outcome‐based academic programs, Program Educational Objectives (PEOs) and Student Outcomes (SOs) are two cores around which all programs’ components and processes revolve. Needless to say, the PEOs‐SOs mapping is very critical for program success and, therefore, a deep understanding of PEOs, SOs, and their intra/inter‐correlations is very important for effective program's decisions making. In this context, this paper proposes a data mining‐based approach to discover hidden knowledge of PEOs, SOs, and their mapping and correlations in engineering programs. More specifically, the proposed approach employs association rule mining techniques to generate association rules, among and between PEOs and SOs, from PEOs‐SOs mapping data of a set of engineering programs, which can be then filtered and manipulated to discover the desired knowledge. To this end, a set of 152 self‐study reports of engineering programs, accredited by American Board for Engineering and Technology‐Engineering Accreditation Commission (ABET‐EAC), are collected and the mapping data between their PEOs and ABET‐EAC SOs are extracted. The dataset is processed and transformed into a representation suitable for association rules mining. This involves developing a set of PEOs labels, annotating data with PEOs labels, and extracting three target datasets. Apriori algorithm is then applied to each dataset to generate three sets of association rules. The generated association rules are then filtered and manipulated to discover the knowledge of PEOs, ABET‐EAC SOs, and their mapping and correlations. Finally, a discussion on the informativeness of the discovered knowledge for the decisions making in engineering education is given.

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An enhanced bayesian network model for prediction of students' academic performance in engineering programs

Cited by 43 publications

References 21 publications

Academic Performance Predictors

Academic Performance Predictors

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

A data‐mining‐based approach to informed decision‐making in engineering education

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