Decision trees are revolutionizing educational methodologies by offering clear, transparent decision-making tools for educators and students alike. These tree-like models help in visualizing various outcomes to educational decisions, simplifying complex problems by breaking them down into more manageable parts. They provide a straightforward graphical representation of options, consequences, and end results. Decision trees support critical thinking and foster self-guided learning by allowing students to map out cause-and-effect relationships. In the classroom, they can be used for curriculum planning, classroom management, and assessing student needs, ensuring decisions are made with clarity and purpose.
Decision trees provide the interpretability and transparency that education requires.
The ongoing challenge in education is how to explain to stakeholders (teachers, administrators, parents) the effectiveness of any education process and the contributing factors to a student’s success. This challenge is even greater when technology is introduced and concepts like blended learning are introduced.
So how can a blended learning provider use decision trees to help sell a new system to an education provider? First, we need to show that using decision trees has several advantages:
- Understandable and explainable: Decision trees are easy to understand and interpret, making them a suitable choice for the education industry where stakeholders need to comprehend the decision-making process.
- Handling mixed data: Decision trees can handle both categorical and continuous data, which is beneficial in the context of education.
- Identifying influential factors: Decision trees can help identify the most important factors that contribute to successful blended learning.
We can start by formulating a clear objective or target variable and allowing the stakeholders to be part of this process. For instance, the objective could be to maximize student engagement or improve learning outcomes. However, the key is from the beginning to focus on making the process understandable and aligned with the current pedagogy of the institution.
By incorporating node questions into our decision tree, we can compare the effectiveness of blended learning and traditional classroom approaches based on various factors.
A decision tree that demonstrates the effectiveness of blended learning over traditional classroom approaches would need to gather data on various factors related to student performance and learning experiences in both settings. Potential node questions (splitting criteria) could include:
- Student engagement: Are students more engaged in blended learning compared to traditional classroom settings?
- Learning outcomes: Do students in blended learning environments perform better on assessments?
- Personalization and differentiation: Are students receiving more personalized learning experiences in blended learning environments?
- Access to learning resources: Do students in blended learning environments have more access to diverse learning resources?
- Time spent on learning activities: Is the time spent on learning activities more effective in blended learning environments?
- Assessment completion: Has the student completed all assessments related to the learning materials?
- Use of supplemental resources: Do students who use supplemental resources achieve higher grades?
The resulting tree structure will help stakeholders understand which aspects of blended learning contribute to improved learning experiences and outcomes. However, neither a Random Forest nor a boosting approach would be ideal for the described problem of creating a blended learning model in the education industry, where interpretability and explainability are of high importance.
As a machine learning practitioner working in education, it is crucial to recognize the responsibility to ensure that the data used and models built fairly represent the lived realities of students.
Interpretability, fairness, and non-discrimination are key in educational machine learning models to ensure that the model's predictions and outcomes do not unfairly favor or disadvantage any particular group of students.
To mitigate discrimination in data or models in education, actions such as balancing the dataset, evaluating features, using fairness-aware algorithms, and regular evaluation of the model's performance are essential.
By understanding the societal context in which the data is collected and the model is deployed, addressing biases in data, and continuously evaluating the fairness of the models, we can contribute to building a more equitable educational system.