The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

Predicting students’ academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like “Programming” and “Data Structures” through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students’ data gathered from various educational settings to predict students’ academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students’ academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students’ academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, random forest, gradient boosting, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students’ performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and K-nearest neighbor.

/pdf/prediction-of-students-academic-performance-based-on-courses-f8g7aeg02i.pdf

Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks

Successful prediction of student performance has significant impact to many stakeholders, including students, teachers and educational institutes. In this domain, it is equally important to have accurate and explainable predictions, where accuracy refers to the correctness of the predicted value, and explainability refers to the understandability of the prediction made. In this systematic review, we investigate explainable models of student performance prediction from 2015 to 2020. We analyze and synthesize primary studies, and group them based on nine dimensions. Our analysis revealed the need for more studies on explainable student performance prediction models, where both accuracy and explainability are properly quantified and evaluated.

/pdf/explainable-student-performance-prediction-models-a-imsfct50jx.pdf

Explainable Student Performance Prediction Models: A Systematic Review

Objectives: To make a systematic review of literature on the prediction of university student dropout through data mining techniques. Methods/Analysis: The study was developed as a systematic review of the literature of empirical research results regarding the prediction of university dropout. In this phase, the review protocol, the selection requirements for potential studies and the method for analyzing the content of the selected studies were provided. The classification presented in section 3 allowed answering the main research question. What are the aspects considered in the prediction of university student desertion through data mining? Findings: University dropout is a problem which affects universities around the world, with consequences such as reduced enrolment, reduced revenue for the university, and financial losses for the State which funds the studies, and also constitutes a social problem for students, their families, and society in general. Hence the importance of predicting university dropout, that is to say identify dropout students in advance, in order to design strategies to tackle this problem. Novelty /Improvement: This is the first work to perform an integral systematic literature review about university dropout prediction through data mining, with studies from 2006–2018.
Keywords: Data Mining, Dropout Factors, Dropout Prediction, Machine Learning, University Student Dropout

/pdf/predicting-university-dropout-through-data-mining-a-4roq2b6nu8.pdf

Predicting University Dropout through Data Mining: A Systematic Literature

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.,Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naive Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.,The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.,The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.,The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

Using educational data mining techniques to increase the prediction accuracy of student academic performance

Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department☆

Wireless Acoustic Sensor Networks (WASN) have drawn tremendous attention due to their promising potential audio-rich applications such as battlefield surveillance, environment monitoring, and ambient intelligence. In this context, designing an approach for target recognition using sensed audio data represents a very attractive solution that offers a wide range of deployment opportunities. However, this approach faces the limited resource’s availability in the wireless sensor. The power consumption is considered to be the major concern for large data transmission and extensive processing. Thus, the design of successful audio based solution for target recognition should consider a trade-off between application efficiency and sensor capabilities. The main contribution of this paper is to design a low-power scheme for target detection and recognition based on acoustic signal. This scheme, using features extraction, is intended to locally detect a specific target and to notify a remote server with low energy consumption. This paper details the specification of the proposed scheme and explores its performances for low-power target recognition. The results showed the hypothesis' validity, and demonstrate that the proposed approach can produce classifications as accurate as 96.88% at a very low computational cost.

Energy-aware Scheme for Animal Recognition in Wireless Acoustic Sensor Networks

The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data tran...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719891406

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks:

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Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department☆

Energy-aware Scheme for Animal Recognition in Wireless Acoustic Sensor Networks

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks: