This research aims to develop machine learning models for students academic
performance and study strategies prediction which could be generalized to all
courses in higher education. Key learning attributes (intrinsic, extrinsic,
autonomy, relatedness, competence, and self-esteem) essential for students
learning process were used in building the models. Determining the broad effect
of these attributes on students' academic performance and study strategy is the
center of our interest. To investigate this, we used Scikit-learn in python to
build five machine learning models (Decision Tree, K-Nearest Neighbour, Random
Forest, Linear/Logistic Regression, and Support Vector Machine) for both
regression and classification tasks to perform our analysis. The models were
trained, evaluated, and tested for accuracy using 924 university dentistry
students' data collected by Chilean authors through quantitative research
design. A comparative analysis of the models revealed that the tree-based
models such as the random forest (with prediction accuracy of 94.9%) and
decision tree show the best results compared to the linear, support vector, and
k-nearest neighbours. The models built in this research can be used in
predicting student performance and study strategy so that appropriate
interventions could be implemented to improve student learning progress. Thus,
incorporating strategies that could improve diverse student learning attributes
in the design of online educational systems may increase the likelihood of
students continuing with their learning tasks as required. Moreover, the
results show that the attributes could be modelled together and used to
adapt/personalize the learning process.

本研究使用 Scikit-learn 中五种机器学习模型，在 924 名牙科学生的数据集上训练并比较，结果显示随机森林模型的准确率为 94.9％，效果最佳。研究建立的模型可以用于预测学生的表现和学习策略，并实施恰当的干预措施来提高学生的学习进展。