Convincing Features
Assignment Type
Subject
Uploaded by Malaysia Assignment Help
Date
Assessment 1: Data science product and report
Task type: Report
Method: Individual
Weighting: 70%
Due date: 23:59 MYT Tuesday, Week 7
Length: 10,000 to 30,000 words
Referencing style: Use APA 7th referencing style. No footnotes.
Present your tables and figures (with captions and references) in the report for easy reading, not in the appendices.
The table caption should be on top of the table. Figure caption should be at the bottom of the figure.
Please refer to Guidelines for Students: Using Generative AI Tools for Learning and Research by Academic Enhancement Division, Sunway University for guidelines on use of generative AI tools.
After submitting your assessment on XLearn, ensure that your Turnitin similarity index is no more than 15%.
A higher score will be flagged for plagiarism. If it is higher than the allowed threshold, you may delete your submission (before the due date) and resubmit another copy of your assessment.
| Criteria / Level A+ (80–100%) | A to A- (70–79%) | B+ (65–69%) | B (60–64%) | Fail (Below 60%) |
|||||
|---|---|---|---|---|---|
| Criteria | Outstanding | Excellent | Good | Satisfactory | Fail |
| Develop Abstract [10%] | Concise (under 200 words) and informative abstract. Clearly states research question, methodology, key findings, and project significance. Uses strong keywords and avoids unnecessary jargon. |
Informative but may lack clarity or conciseness. Some key elements may be partially missing or unclear. Keywords may be suboptimal. |
Present but brief or missing essential information. Limited keywords or excessive jargon affects clarity. |
Weak or poorly written. Purpose and significance unclear. Heavy use of jargon. |
Abstract is missing. |
| Justify Project Aims & Objectives [15%] | Clearly articulates the project aims and objectives with strong justification and clear linkage to the problem. |
Aims and objectives stated but may lack clarity or depth. | Aims and objectives stated but justification is weak or incomplete. | Aims and objectives are unclear or poorly justified. | Aims and objectives section is missing. |
Student’s report with a Turnitin similarity score of more than 15% will be investigated to ensure fairness and consistency in assessment.
If deemed plagiarism, it will be considered academic malpractice.
All alleged academic malpractice will be handled, investigated, and decided upon according to the Academic Malpractice Policy and Procedure.
You can refer to the Student Handbook and Sunway University’s Academic Malpractice Policy and Procedure from the iZone system.
Your assessment will be marked using the rubric below.
| Criteria / Level A+ (80–100%) | A to A- (70–79%) | B+ (65–69%) | B (60–64%) | Fail (Below 60%) |
|||||
|---|---|---|---|---|---|
| Criteria | Outstanding | Excellent | Good | Satisfactory | Fail |
| Critical Analysis of Literature [20%] | Thorough and critical analysis of relevant data science literature. Identifies key themes, trends, and research gaps. Evaluates strengths and weaknesses of studies. Uses correct citations with complete references. |
Covers relevant sources but lacks depth. Themes and gaps identified but not fully explored. Minor citation errors. |
Limited scope with weak critical analysis. Themes and gaps unclear. Citation formatting issues. |
Superficial review with little or no analysis. No identification of themes or gaps. |
Literature review missing. |
| Formulate Methodology [20%] | Clearly defined methodology aligned with objectives. Demonstrates strong understanding of data science techniques. Well-structured and adaptable approach. |
Methodology addresses objectives but lacks detail. Good understanding of techniques, minor weaknesses. |
Partial alignment with objectives. Adequate understanding but limited justification. |
Poor alignment or misunderstanding of techniques. Major structural flaws. |
Methodology absent or irrelevant. |
| Criteria / Level A+ (80–100%) | A to A- (70–79%) | B+ (65–69%) | B (60–64%) | Fail (Below 60%) |
|||||
|---|---|---|---|---|---|
| Criteria | Outstanding | Excellent | Good | Satisfactory | Fail |
| Interpret Results with Discussion [20%] | Objective interpretation of results considering biases. Clear implications linked to objectives. Insightful discussion of outcomes. |
Mostly objective interpretation. Some implications lack depth. |
Biased or unclear interpretation. Weak linkage to objectives. |
Misleading or flawed interpretation. No meaningful analysis. |
Results and discussion missing. |
| Criteria / Level A+ (80–100%) | A to A- (70–79%) | B+ (65–69%) | B (60–64%) | Fail (Below 60%) |
|||||
|---|---|---|---|---|---|
| Criteria | Outstanding | Excellent | Good | Satisfactory | Fail |
| Assess Findings from Data Science Perspective [10%] | Comprehensive assessment of findings. Evaluates methodology effectiveness. Identifies limitations and future improvements. Discusses broader implications. |
Adequate assessment with minor gaps. Some limitations discussed. |
Weak or superficial assessment. Limited evaluation of methodology. |
No meaningful assessment or evaluation. | Assessment section missing. |
| Citing References [5%] | Comprehensive, high-quality scholarly sources. Perfect citation formatting. |
Relevant and extensive references. Minor citation errors. |
Adequate references with formatting issues. | Limited or weak-quality sources. Noticeable citation errors. |
Insufficient or irrelevant references. Incorrect citation practices. |
For further support, please visit Important Contacts for a range of support services and resources to help you in your learning.
Assessment plagiarism, cheating, fabrication, falsification of data or any form of assessment dishonesty is a misconduct.
Refer to the Programme Handbook for more information regarding student responsibilities.
You can find information on how to request for an assessment extension in the Subject Outline.
You can find more important information regarding penalties for late assessment submissions, publication of results, return of marked assessments and re-sit in the Programme Handbook.
