The objective of the activity is to make students aware of the thinking process involved in doing exploratory data analysis in addition to the knowledge of how to perform the analysis.
Type of Material:
Assignment
Recommended Uses:
This assignment would be useful for individual and group student practice. For anyone who is doing research in EDA.
Technical Requirements:
Any browser.
Document Reader (e.g. Word)
Identify Major Learning Goals:
The objective of the activity is to make students aware of the thinking process involved in doing exploratory data analysis in addition to the knowledge of how to perform the analysis.
Target Student Population:
Graduate School, College General Ed, College Lower Division. Any academic looking for materials and contents in EDA. Professionals and anyone doing some research about. Incoming graduate students in MS AI, MSISE and MSEM program.
Prerequisite Knowledge or Skills:
Elementary and descriptive statistics
Content Quality
Rating:
Strengths:
This activity introduces the students to the end-to-end data analysis process through hands-on analysis using R statistical programming language. This assignment promotes the statistical thinking involved in the gathering of data that uses the different statistical methods and procedures to do the analysis of the data. It includes a link to the data set that will provide students with a real-life statistics activity. It also includes a link to an explanation of common plotting pitfalls.
Concerns:
Having students gather data would be helpful as the first step to the EDA. Through the gathering of data, the students could see where the value of the data comes from, the perception of the variability of the values, the minimum and maximum values that make them recognize the pattern and tendency of the data and confirm that pattern or tendency in the exploratory analysis. It would also be helpful to specifically list the charts and statistics that might be relevant for this assignment.
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
The students are asked to analyze a dataset of their choice from the inbuilt datasets in R as homework, which could promote the effectiveness of the assignment. Also, the assignment promotes statistical literacy and demonstrates the necessity of data. The three questions from the metacognitive conversation will make the students aware what they need to have in order to build an understanding of the data before performing and sharing data analysis. The text material included is very helpful because it highlights the importance to be aware of some common plotting problems. Having students move to breakout sessions in groups to perform the data analysis is an effective way to ensure that the learn how to communicate their statistical ideas. Also going back and forth between the full class being together and working in small groups is pedagogically appropriate.
Concerns:
It should include bibliography references about EDA to do further reviews. The site lists three questions about the decision to be made based on the data. It would be helpful to also ask if the data set is sufficient to confidently make that decision.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
The document is concise and clearly written. The two hyperlinks include to the material help to address the contents. The step-by-step instructions are easy to understand and follow.
Concerns:
It would be helpful if the assignment highlighted the importance of EDA to make decisions based on the data. Stating “the task takes place in the second lecture” is not enough information for instructors who teach at different colleges.
Creative Commons:
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