One type of data mining that could be effectively used at CTIS (Computer Technology and Information Systems) to improve student performance is Predictive Analytics. Here's how it can be applied:

What is Predictive Analytics?

Predictive analytics uses historical and current data to make predictions about future outcomes. It employs techniques like machine learning, statistical algorithms, and data modeling to identify patterns and trends.

How It Can Improve Student Performance at CTIS

1. Identifying At-Risk Students

• How it works: Analyze historical data on grades, attendance, participation in class activities, and engagement in online learning platforms.
• Benefit: Predict which students are at risk of failing or underperforming early in the semester.
• Action: Intervene proactively with personalized support, such as tutoring, counseling, or revised learning strategies.

2. Personalized Learning Plans

• How it works: Use data from past performance to predict individual learning preferences and strengths.
• Benefit: Tailor coursework and assignments to match each student’s unique needs, such as offering more interactive content for visual learners.
• Action: Provide customized resources or recommendations to optimize learning outcomes.

3. Optimizing Course Content

• How it works: Analyze feedback, test results, and assignment scores across courses to identify topics where most students struggle.
• Benefit: Highlight areas where the curriculum needs improvement or additional focus.
• Action: Adjust course content or teaching methods to address these weak spots.

4. Enhancing Academic Advising

• How it works: Predict future academic success based on a student's choice of courses, grades in pre-requisite subjects, and workload balance.