Higher Education Student Academic Performance Analysis and Prediction Using Machine Learning and Data Mining Techniques

Higher education institutions face the challenge of enhancing student academic performance to ensure the quality of graduates and promote student success. Machine learning (ML) and data mining (DM) techniques offer powerful tools for analyzing large educational datasets, identifying patterns, and making predictions, enabling educators to develop targeted interventions and personalized learning experiences.

HIGHER EDUCATION STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

by Vivian Siahaan

4.7 out of 5

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Methodologies and Algorithms

Supervised Learning

Linear Regression: A simple yet effective algorithm that establishes a linear relationship between input features (e.g., student demographics, course attendance) and an output variable (e.g., GPA).

Decision Trees: Rule-based algorithms that recursively split data into subsets based on specific criteria, creating a hierarchical structure that represents the decision-making process.

Support Vector Machines: Complex algorithms that separate data into distinct classes using hyperplanes, maximizing the margin between classes to improve prediction accuracy.

Unsupervised Learning

Clustering: Techniques such as k-means and hierarchical clustering group similar students into clusters based on their academic performance, demographics, or other characteristics.

Dimensionality Reduction: Algorithms such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) reduce the dimensionality of large datasets, making them easier to visualize and analyze.

Applications and Case Studies

Predicting Student Performance

ML models can predict student GPA, course grades, or graduation probability based on historical data. This information can help identify students at risk of academic failure and provide early intervention support.

Example: A study by Almaiah et al. (2021) used a Random Forest algorithm to predict student performance in a programming course, achieving an accuracy of 85.7%.

Identifying Factors Influencing Performance

DM techniques can uncover hidden patterns and relationships within educational data. This knowledge can guide educators in developing interventions to address specific factors that impact student success.

Example: A study by Zafra et al. (2019) used cluster analysis to identify student profiles based on their academic performance, social participation, and learning strategies, providing insights into the factors influencing their academic success.

Personalized Learning

ML and DM can help create personalized learning experiences by recommending tailored content, adjusting instructional strategies, and providing feedback based on students' individual learning needs and preferences.

Example: A study by Liu et al. (2020) used a Deep Learning model to recommend personalized learning paths for students in an online course, resulting in improved student engagement and learning outcomes.

Challenges and Limitations

Data Quality and Availability

The quality and availability of educational data can impact the accuracy and effectiveness of ML and DM models. Data collection methods and data cleaning processes need to be carefully designed and implemented.

Model Interpretability

Some ML algorithms, such as Deep Learning models, can be complex and difficult to interpret. Understanding how these models make predictions is crucial for ensuring fairness and avoiding biased outcomes.

Ethical Considerations

The use of ML and DM in education raises ethical concerns related to data privacy, bias mitigation, and the potential for algorithmic discrimination. Ethical guidelines and best practices must be established to ensure responsible use of these technologies.

Machine learning and data mining techniques provide powerful tools for analyzing and predicting higher education student academic performance. By leveraging these methodologies, institutions can gain valuable insights into student learning, identify factors influencing success, and develop personalized interventions. However, challenges related to data quality, model interpretability, and ethical considerations need to be addressed to ensure effective and responsible implementation of these technologies in educational settings.

References

• Almaiah, M. A., Aljeraiwi, A. A., & Mohammed, F. A. (2021). Predicting Students' Performance in Programming Course Using Machine Learning.
• Liu, J., Lee, C., & Algesheimer, R. (2020). Personalized Learning Recommendation Based on Deep Learning.
• Zafra, A., Romero, C., & Ventura, S. (2019). Clustering to Identify Student Profiles Based on Course Performance, Social Participation, and Learning.

HIGHER EDUCATION STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

by Vivian Siahaan

4.7 out of 5

Language	:	English
File size	:	5371 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	294 pages
Lending	:	Enabled
Paperback	:	206 pages
Item Weight	:	10.9 ounces
Dimensions	:	6 x 0.47 x 9 inches

FREE