Using Artificial Intelligence to Predict Academic Performance

Research on the university intelligent learning analysis system based on AI

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189820 ◽

2021 ◽

pp. 1-10

Author(s):

Meng Huang ◽

Shuai Liu ◽

Yahao Zhang ◽

Kewei Cui ◽

Yana Wen

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Academic Performance ◽

Clustering Algorithm ◽

Back Propagation ◽

Three Dimensional ◽

Training Model ◽

Future Trend ◽

Artificial Intelligence Technology ◽

Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

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Predicting academic performance with Artificial Intelligence (AI), a new tool for teachers and students

2020 IEEE Global Engineering Education Conference (EDUCON) ◽

10.1109/educon45650.2020.9125141 ◽

2020 ◽

Author(s):

Elvira G. Rincon-Flores ◽

Eunice Lopez-Camacho ◽

Juanjo Mena ◽

Omar Olmos Lopez

Keyword(s):

Artificial Intelligence ◽

Academic Performance ◽

Teachers And Students

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Academic Performance Prediction Method of Online Education using Random Forest Algorithm and Artificial Intelligence Methods

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v16i05.20297 ◽

2021 ◽

Vol 16 (05) ◽

pp. 45

Author(s):

Jing Yu

Keyword(s):

Artificial Intelligence ◽

Academic Performance ◽

Random Forest ◽

Online Education ◽

Decision Tree ◽

Empirical Analysis ◽

Performance Prediction ◽

Prediction Method ◽

Learning Activities ◽

Learning Analysis

In order to improve the teaching quality of online education, the prediction method of students' online academic performance has been studied. First, the learning analysis, artificial intelligence (AI) and other related theoretical concepts are analyzed and introduced. Then, the decision tree of single classification algorithm and the random forest (RF) of ensemble learning algorithm are analyzed, and the academic performance prediction model of online education is constructed by RF algorithm. Finally, the data of education platform is used for empirical analysis to verify the reliability and practicability of the academic performance prediction algorithm of online education. The connotation of learning analysis, the role and elements of learning analysis in the learning process are introduced. The algorithm principle of RF and decision tree is analyzed. By using the idea of information entropy and discretization, the continuous variables are processed to improve the fitting degree of the algorithm. The model is evaluated by empirical analysis, and the test accuracy of several different algorithms is compared. It is found that the prediction accuracy of the RF algorithm is more than 90%, which shows that the prediction method can help teachers and students to carry out better teaching and learning activities, so as to better improve students' ability to master knowledge. It is hoped that the result can provide some reference for the management of students' learning behavior and the optimization of teachers' teaching strategies in online learning activities

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Applications of Artificial Intelligence in Assessment for Learning in Schools

Handbook of Research on Digital Content, Mobile Learning, and Technology Integration Models in Teacher Education - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-2953-8.ch010 ◽

2018 ◽

pp. 185-206 ◽

Cited By ~ 1

Author(s):

Subhagata Chattopadhyay ◽

Savitha Shankar ◽

Ramya B. Gangadhar ◽

Karthik Kasinathan

Keyword(s):

Artificial Intelligence ◽

Academic Performance ◽

Control System ◽

Learning Outcome ◽

Assessment For Learning ◽

Assessment Process ◽

Assessment Practices ◽

Tutoring Systems ◽

Agent Based ◽

Qualitative And Quantitative

Assessment for Learning (AfL) is a process in measuring the learning outcome in students. Current practices in assessing the academic performance of students in most of the countries are still manual. It is based on the qualitative and quantitative feedbacks, obtained by expressed statement and marks, respectively. The issues associated with such assessment-practices are that it (a) lacks autonomy in students and the teachers to assess themselves for (1) better learning (ABeL) and (2) to learning (AtoL) with greater accuracy; (b) Self, peer and parents' involvements in the assessment process has often been underestimated, and (c) involved human bias while giving the qualitative and quantitative feedbacks. Given the background, this chapter attempts to showcase how various Artificial Intelligence (AI)-based solutions, such as Expert Control System (ECS)-based tutoring platform and Agent-based tutoring systems (AbS) can be used for the AfL, which in turn, improve ABeL and AtoL in students.

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