[论文解读] Machine Learning Approach for Predicting Students Academic Performance and Study Strategies based on their Motivation

Fidelia A. Orji, Julita Vassileva|arXiv (Cornell University)|Oct 15, 2022

Online Learning and Analytics被引用 8

一句话总结

本研究开发了机器学习模型，利用内在动机、外在动机、自主性、关联性、胜任感和自尊心等关键预测变量，预测大学生的学业表现和学习策略。基于智利924名牙科专业学生的数据，基于树的模型——尤其是随机森林模型——达到了94.9%的准确率，表明动机特征可有效用于高等教育中的个性化学习干预。

ABSTRACT

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.

研究动机与目标

• 开发可推广的机器学习模型，用于预测高等教育课程中的学业表现和学习策略。
• 探究关键心理学习属性（如自主性、胜任感和自尊心）对学生表现和学习行为的影响。
• 比较不同机器学习算法在基于动机因素建模学生结果时的预测性能。
• 通过识别关键动机预测变量，实现在线教育系统中的数据驱动个性化干预。

提出的方法

• 从智利924名大学牙科专业学生收集定量数据，以评估其动机属性和学业结果。
• 采用五种监督学习模型：决策树、K-近邻、随机森林、线性/逻辑回归和支持向量机。
• 同时执行回归和分类任务，以预测学业表现和学习策略。
• 使用Python中的Scikit-learn进行模型训练、评估和测试，并采用标准性能指标。
• 对各模型进行对比分析，以识别最准确且鲁棒的预测变量。
• 将多种动机属性（内在动机、外在动机、自主性、关联性、胜任感、自尊心）作为模型训练的输入特征。

实验结果

研究问题

• RQ1 基于学生动机属性，哪些机器学习模型最能预测学业表现和学习策略？
• RQ2 内在动机与外在动机、自主性、关联性、胜任感和自尊心如何共同影响学业结果？
• RQ3 动机属性能否被有效用于个性化在线教育系统中的学习体验？
• RQ4 在此背景下，基于树的模型与线性模型及核方法模型的相对预测准确率如何？

主要发现

• 随机森林模型在分类学生学习策略和学业表现方面达到了最高的预测准确率94.9%。
• 基于树的模型（包括决策树和随机森林）优于线性模型、K-近邻和支撑向量机。
• 整合多种动机属性（如自主性、胜任感、自尊心）显著提升了模型的预测能力。
• 研究结果支持利用基于动机的特征实现高等教育中学习干预个性化的可行性。
• 本研究证明，可将心理学习属性联合建模，以提升教育系统中的预测准确性和个性化水平。