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View Code? Open in Web Editor NEWCOGS108 Final Project -- Group043_WI24 Repository
COGS108 Final Project -- Group043_WI24 Repository
Score = 5
Quality | Reasons | |
---|---|---|
Data relevance | P | |
Data description | D | Since you don’t have many columns in your dataset, you could include a paragraph describing what each column variable means. Also, in your later submissions, you should include more comments in your code. |
Data wrangling | P | Overall you did a good job cleaning your data. |
|
Unsatisfactory | Developing | Proficient | Excellent | |
---|---|---|---|---|
Data relevance | Did not have data relevant to their question. Or the datasets don't work together because there is no way to line them up against each other. If there are multiple datasets, most of them have this trouble | Data was only tangentially relevant to the question or a bad proxy for the question. If there are multiple datasets, some of them may be irrelevant or can't be easily combined. | All data sources are relevant to the question. | Multiple data sources for each aspect of the project. It's clear how the data supports the needs of the project. |
Data description | Dataset or its cleaning procedures are not described. If there are multiple datasets, most have this trouble | Data was not fully described. If there are multiple datasets, some of them are not fully described | Data was fully described | The details of the data descriptions and perhaps some very basic EDA also make it clear how the data supports the needs of the project. |
Data wrangling | Did not obtain data. They did not clean/tidy the data they obtained. If there are multiple datasets, most have this trouble | Data was partially cleaned or tidied. Perhaps you struggled to verify that the data was clean because they did not present it well. If there are multiple datasets, some have this trouble | The data is cleaned and tidied. | The data is spotless and they used tools to visualize the data cleanliness and you were convinced at first glance |
Scoring: Out of 5 points
Each Developing => -1 pts
Each Unsatisfactory=> -2 pts
until the score is 0
If students address the detailed feedback in a future checkpoint they will earn these points back
DETAILED FEEDBACK should be left in the data section AND anywhere the student addressed proposal feedback but did not do it to your satisfaction
Score = 5
Quality | Reasons | |
---|---|---|
EDA Relevance | P | |
EDA Analysis and Description | P | |
EDA Figures | P |
Unsatisfactory | Developing | Proficient | Excellent | |
---|---|---|---|---|
EDA relevance | EDA is mostly neither relevant to the question nor helpful in figuring out how to address the question. Or the EDA does address the question, but many obviously relevant variables / analyses / figures were not included. EDA does not include explore distributions of single variables or relationships between variables or both | EDA is partly irrelevant/unhelpful. Or some obviously relevant variables / analyses / figures were not included. EDA does not include a few distributions of single variables or relationships between variables | EDA is almost all relevant / helpful in addressing the question. No obviously relevant variables / analyses / figures were not included. | Thorough EDA addressed all aspects that are relevant to the question |
EDA analysis and description | Many of the analyses are poor choices (e.g., using means instead of medians for obviously skewed data), or are poorly described in the text, or do not aid understanding the data | Some of the analyses are poor choices, or are poorly described in the text, or do not aid understanding the data | All analyses are correct choices. Only one or two have minor issues in the text descriptions supporting them. Mostly they fit well with other elements of the EDA and support understanding the data | All analyses are correct choices with clear text descriptions supporting them. The figures fit well with the other elements of the EDA, producing a clear understanding of the data. |
EDA figures | Many of the figures are poor plot choices (e.g., using a bar plot to represent a time series where it would be better to use a line plot) or have poor aesthetics (including colormap, data point shape/color, axis labels, titles, annotations, text legibility) or do not aid understanding the data | Some of the figures are poor plot choices or have poor aesthetics. Some figures do not aid understanding the data | All figures are correct plot choices. Only one or two have minor questionable aesthetic choices. The figures mostly fit well with the other elements of the EDA and support understanding the data | All figures are correct plot choices with beautiful aesthetics. The figures fit well with the other elements of the EDA, producing a clear understanding of the data. |
Scoring: Out of 5 points
Each Developing => -1 pts
Each Unsatisfactory=> -2 pts
until the score is 0
If students address the detailed feedback in a future checkpoint they will earn these points back
DETAILED FEEDBACK should be left in the data section AND anywhere the student addressed proposal feedback but did not do it to your satisfaction
Hi group 43, your assigned grader is Yueying Dong.
The person will be responsible for reading, grading, and providing feedback on your final project throughout the entire quarter. If you and your teammates have project-specific questions, it is strongly recommended that you reach out to the assigned grader and/or attend their office hours, as they will have the deepest understanding of your project.
Hello!
We have addressed the detailed feedback in checkpoint #2. With this within our work for checkpoint #1, we have created a more detailed summary of the code we created and why we chose to, so we would have more comments within our code. Additionally, we included a paragraph about the column variables we have.
Thank you for your time!
Group 43
Score = 9
Quality | Reasons | |
---|---|---|
Abstract | NA | |
Research question | P | |
Background | D | You did a great job discussing related work. But you also need to write what aspects the previous work lacks that your project aims to address. Maybe you can say something about how no one has yet looked at how the regional & seasonal factors in comparison affects the avocado prices, etc. |
Hypothesis | P | This is good. |
Data | D | You should describe your ideal dataset, and make comparisons between the ideal dataset & the current one you have. |
Ethics | P | |
Team expectations | E | |
Timeline | P |
Unsatisfactory | Developing | Proficient | Excellent | |
---|---|---|---|---|
Abstract | The abstract is confusing or fails to offer important details about the issue, variables, context, or methods of the project. | The abstract lacks relevance or fails to offer appropriate details about the issue, variables, context, or methods of the project. | The abstract is relevant, offering details about the research project. | The abstract is informative, succinct, and clear. It offers specific details about the educational issue, variables, context, and proposed methods of the study. |
Research question | The research issue remains unclear. The research purpose, questions, hypotheses, definitions variables, and controls are still largely undefined, or when they are poorly formed, ambiguous, or not logically connected to the description of the problem. Unclear connections to the literature. | The research issue is identified, but the statement is too broad or fails to establish the importance of the problem. The research purpose, questions, hypotheses, definitions or variables, and controls are poorly formed, ambiguous, or not logically connected to the description of the problem. Unclear connections to the literature. | Identifies a relevant research issue. Research questions are succinctly stated, connected to the research issue, and supported by the literature. Variables and controls have been identified and described. Connections are established with the literature. | Presents a significant research problem. Articulates clear, reasonable research questions given the purpose, design, and methods of the project. All variables and controls have been appropriately defined. Proposals are clearly supported by the research and theoretical literature. All elements are mutually supportive. |
Background | Did not have at least 2 reliable and relevant sources. Or relevant sources were not used in relevant ways | A key component was not connected to the research literature. Selected literature was from unreliable sources. Literary supports were vague or ambiguous. | Key research components were connected to relevant, reliable theoretical and research literature. | The narrative integrates critical and logical details from the peer-reviewed theoretical and research literature. Each key research component is grounded in the literature. Attention is given to different perspectives, threats to validity, and opinion vs. evidence. |
Hypothesis | Lacks most details; vague or interpretable in different ways. Or seems completely unrealistic. | A key detail to understand the hypothesis or the rationale behind it was not described well enough | The hypothesis is clear. All elements needed to understand the rationale were described in sufficient detail | The hypothesis and its rationale were described succinctly and with clarity about how they are connected to each other |
Data | Did not describe ideal dataset fully AND does not include at least one reference to an external source of data. | Either does not describe the ideal dataset fully AND does not include at least one reference to an external source of data that could be used to answer the proposed question. | Ideal dataset(s) well-described and includes everything needed for answering question(s) posed. Includes at least one reference to a source of data that would be needed to fully answer the question proposed. | Ideal dataset(s) well-described and includes everything needed for answering question(s) posed. Includes references to all sources of data that would be needed to fully answer the question proposed. The details of the descriptions also make it clear how they support the needs of the project and discuss the differences betweeen the ideal and real datasets. |
Ethics | No effort or just says we have no ethical concerns | Minimal ethical section; probably just talks about data privacy and no unintended consequences discussion. Ethical concerns raised seem irrelevant. | The ethical concerns described are appropriate and sufficient. | Ethical concerns are described clearly and succinctly. This was clearly a thorough and nuanced approach to the issues |
Team expectations | Lack of expectations | The list of expectations feels incomplete and perfunctory | It feels like the list of expectations is complete and seems appropriate | The list clearly was the subject of a thoughtful approach and already indicates a well-working team |
Timeline | Lack of timeline. Or timeline is completely unrealistic | The timeline feels incomplete and perfunctory. The timeline feels either too fast or too slow for the progress you expect a group can make | It feels like the timeline is complete and appropriate. it can likely be completed as is in the available amount of time | The timeline was clearly the subject of a thoughtful approach and indicates that the team has a detailed plan that seems appropriate and completeable in the allotted time. |
Scoring: Out of 9 points
If students address the detailed feedback in a future checkpoint, they will earn these points back.
We have added towards the end of our sections (background and data) the edits you have recommended us to implement for our project proposal inside of Checkpoint #1 for a regrade to obtain a better grade for the past submission that was due 2 weeks ago (I believe).
Thank you in advance for looking them over!
-Team 43
Score = 14.9
Notes: Checked checkbox means that you earned this rubric item. Uncheck box indicate you did not (fully) earned this rubric item. See the Notes for details.
Overview (0.5 pt):
Research Question (0.5 pt):
Background & Prior Work and Hypothesis (0.5 pts):
Data Description/ Datasets (0.75 pts):
Data Cleaning/Processing (0.75 pts):
Data Visualizations (3 pts): minimum of 3 viz required; divide points by N visualizations
Data Analysis and Results (4.5 pts) divide points by N analyses performed
Privacy/Ethics Considerations (1.5 pts):
Conclusion & Discussion (1.5 pts):
Documentation/ Written Communication (1.5 pts):
Hello!
Based on the suggestions you provided, we have created better comments throughout our code and have created better descriptions. We also added a paragraph to describe the variables within our dataset that we had.
When you have the chance, may you please look them over!
Thank you,
Group 43
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