Predictive Learning Analytics in Grade 7-12 Education
While Learning Analytics is more well-known and publicly accepted, we want to focus on a niche interest in Predictive Learning Analytics. Let's get started!
What is Learning Analytics?
This 2-minute video offers introductory knowledge about Learning Analytics.
Learning Analytics refers to the process of collecting, analyzing, and interpreting data generated through educational activities to gain insights and inform decision-making (Siemens & Long, 2011).
It involves the use of various data sources, such as student interactions with learning management systems, online platforms, and educational technologies.
What is Predictive Learning Analytics?
This 4-minute video explains what Predictive Analytics is, the types of models used, and what its goals are.
Predictive Learning Analytics builds on the foundation of learning analytics by using data-driven techniques to make predictions about student behavior, performance, and outcomes.
It goes beyond descriptive analytics which focuses on analyzing past and current data and aims to forecast future learning behavior and outcomes.
4 Types of Learning Analytics
There are 4 key types of Learning Analytics: Prescriptive, Predictive, Diagnostic, and Descriptive.
As noted by Cote (2021), Predictive Analytics is the use of data to predict future events and trends using large amounts of historical data. Predictive Analytics uses historical data to help drive strategic decision-making.
What does Predictive Learning Analytics Look Like in Grade 7-12 Education?
Predictive Learning Analytics involves the use of data, statistical algorithms, and machine learning techniques to identify patterns, predict future outcomes, and make data-driven decisions (OpenAI, 2021).
In the context of 7-12 education, Predictive Analytics can be a powerful tool to enhance teaching and learning by providing insights into student performance, identifying at-risk students, and personalizing instruction.
What are the Benefits of Using Learning Analytics in Grade 7-12 Education?
Predictive Learning Analytics can offer several benefits in the 7-12 education domain. By leveraging data from various sources such as student assessments, attendance records, and learning management systems, educators can gain valuable insights to:
Identify At-risk Students
Personalize Instruction
Optimize Resource Allocation
Improve Decision-making
What are the Key Concepts of Predictive Learning Analytics?
Data Mining
The exploration and analysis of large datasets to discover learning patterns and reveal the relationships in between.
Application: Data-mining results can be used for predictive modeling and decision-making.
Machine Learning
The use of algorithms that enable systems to automatically learn from data without being explicitly programmed.
Application: Machine learning techniques are commonly used in Predictive Analytics to develop models that can make accurate predictions and classifications.
Predictive Models
The mathematical algorithms that use historical data to make predictions about future outcomes.
Application: Predictive models are trained using machine learning techniques and can be applied to various educational scenarios, such as student performance prediction or identifying students at risk.
Decision Trees
Decision trees are popular machine learning techniques used in Predictive Analytics. They are graphical models that represent decisions and their possible consequences.
Application: Decision trees can be used to classify students into different groups based on specific criteria or predict outcomes based on a set of conditions.
Evaluation Metrics
Common metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC).
These metrics provide insights into the model's predictive accuracy and ability to correctly classify or predict outcomes.
Application: Evaluation metrics are used to assess the performance of predictive models.
Emsemble Learning
Ensemble learning combines multiple predictive models to improve prediction accuracy and robustness. It involves training multiple models and aggregating their predictions to make a final prediction.
Application: Ensemble learning can aid in identifying the most important features or variables that contribute to educational outcomes. By analyzing the patterns and relationships learned by different models, ensemble methods can help educators and researchers understand the key factors influencing student success.
Data Preprogressing
Data preprocessing involves cleaning, transforming, and preparing the dataset for analysis.
Application: This may include tasks such as handling missing values, outlier detection, feature scaling, and data normalization to ensure the data is suitable for predictive modeling.
Deployment
Deployment is the process of integrating the predictive model into the educational system or workflow, making it available for use in real-world scenarios.
Application: It involves taking the solution from the planning and development stage and putting it into practice in schools, classrooms, or educational systems.
Optimization
Optimization involves the implementation, monitoring, and maintenance of the predictive analytics solution to ensure its effectiveness and ongoing value.
Application: It involves analyzing data, gathering insights, and making adjustments to enhance outcomes and achieve desired objectives.