Abstract is confusing or fails to offer important details about the issue, variables, context, or methods of the project.
Abstract lacks relevance or fails to offer pertinent details about the issue, variables, context, or methods of the project.
Abstract is relevant, offering details about the research project.
Abstract is informative, succinct, and clear. It offers specific details about the educational issue, variables, context, and proposed methods of the study.
Problem Statement
Research issue remains unclear. The research purpose, questions, hypotheses, definitions or 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 whether the research problem is quantifiable, measurable, and replicable.
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. The limited description of whether the research problem is quantifiable, measurable, and replicable.
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. Clear reasoning and description on that the research problem is quantifiable, measurable, and replicable
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. Clear and significant reasoning on the quantifiability, measurability, and replicability of the research problem. 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.
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.
Proposed Solution
Lacks most details; vague or interpretable in different ways. Or seems completely unrealistic and inapplicable to the project domain.
Limited descriptions of the rationales and theories behind the solution provided and on how the solution will be tested. Limited relevance to the input dataset and problem to be solved.
Sufficient details on algorithmic description or theoretical properties; clear definition of how the solution will be tested, reproduced, and on the benchmark used.
Highly clear and succinct description of the rationales and theories behind the solution; thorough and comprehensive consideration of how the solution will be applied and tested; valid approach on how to reproduce the solution and effective benchmark to test the solution; a strong connection to the problem proposed.
Data
Did not have references to relevant data sources for this problem. Did not describe the data obtained at those sources
A key data source was not referenced or described in a satisfactory level of detail
All relevant data sources were referenced and described in terms of their key variables and size
Multiple data sources for each aspect of the project, All data sources are fully described and referenced. The details of the descriptions also make it clear how they support the needs of the project.
Evaluation Metrics
Did not propose any metric for evaluating the model or very little effort in this section.
Evaluation metrics proposed with limited relevance or inappropriate metrics; ambiguous description of the metrics to be used.
Thoughtful and meaningful evaluation metrics with sufficient considerations and descriptions of the model to be evaluated.
Effective and comprehensive evaluation metrics with thorough and detailed descriptions.
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.
Ethical concerns described are appropriate and described sufficiently
Ethical concerns are described clearly and succinctly. This was clearly a thoughtful 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 the 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 completable in the allotted time.
Scoring: Out of 9 points
Each Developing => -0.75 pts
Each Unsatisfactory/Missing => -1.5 pts
until the score is 0
If students address the detailed feedback in a future checkpoint, they will earn these points back.
Comments
My main concern is how you are going to use some variables in the dataset. I feel artist_name and track_name are actually more directly related to the genre (i.e., as a human being you can predict the genre of a song based on the artist and the name of the song even before listening to it). But how you are going to utilize these features? Since to interpret these features you may need external dataset or do some NLP. (Well maybe you consider treating artist name as categorical value and using one-hot encoding but that will result in huge number of features since that are thousands different artists, so anyway it's impossible to utilize these features directly.) If you decide not to use these features (which is understandable since that might be too much for a course mini project) you need to mention it in the proposal and the final write-up. But if you feel some basic semantic analysis (e.g. sentiment analysis) of track_name is doable and relevant you may try (just a suggestion not requirement).
eleeeysh
commented
1 year ago
Project Proposal Feedback
Score (out of 9)
Score = 9
Feedback:
Rubric
Scoring: Out of 9 points
If students address the detailed feedback in a future checkpoint, they will earn these points back.
Comments
My main concern is how you are going to use some variables in the dataset. I feel
artist_nameandtrack_nameare actually more directly related to the genre (i.e., as a human being you can predict the genre of a song based on the artist and the name of the song even before listening to it). But how you are going to utilize these features? Since to interpret these features you may need external dataset or do some NLP. (Well maybe you consider treating artist name as categorical value and using one-hot encoding but that will result in huge number of features since that are thousands different artists, so anyway it's impossible to utilize these features directly.) If you decide not to use these features (which is understandable since that might be too much for a course mini project) you need to mention it in the proposal and the final write-up. But if you feel some basic semantic analysis (e.g. sentiment analysis) of track_name is doable and relevant you may try (just a suggestion not requirement).