Final answer:
The CRISP-DM framework offers a systematic approach to addressing the high failure rate in freshman math and accounting courses by thoroughly defining the issue, understanding and preparing data e.g., attendance records and student feedback, modeling to find patterns, and deploying solutions aligned with the college's objectives.
Step-by-step explanation:
The CRISP-DM framework, which stands for Cross-Industry Standard Process for Data Mining, is a systematic approach to tackle the problem of student failures in freshman math and accounting courses at a college. To apply this framework to the college's concern, we'd begin with the following six steps:
- Business Understanding: This phase involves defining the problem precisely. For the college, the primary issue is the high failure rate in freshman math and accounting courses leading to elevated attrition.
- Data Understanding: This involves collecting and understanding the data related to the problem, such as student attendance records, grades, and feedback on course redundancy and online classes.
- Data Preparation: Here, the collected data is cleaned and formatted to facilitate analysis. Factors such as attendance, mental health issues, and staff-to-student ratios could be considered.
- Modeling: In this phase, we apply various statistical or machine learning models to identify patterns and correlations, like how student absenteeism correlates with dropout rates.
- Evaluation: The models are evaluated against the business objectives. Does addressing absenteeism improve retention rates? Are course redundancies causing disengagement?
- Deployment: Implement the solutions, such as curriculum adjustments or more support services, and monitor their impact on dropout rates.
For instance, data might reveal that dditional math support services are required. By aligning this framework with the college's goals, a proactive strategy can be developed to reduce failures and dropout rates.