Chem IA

Introduction to Assignment Submission and Feedback Process

  • Assignment Submission Policy:

    • Students must turn in their assignments to avoid losing points ("you don't get punched").

  • Using AI for Feedback:

    • Instead of traditional peer reviews, students are encouraged to upload drafts to an AI tool for constructive feedback.

    • Perceived issues with peer reviews include bias and inadequate feedback.

    • The AI tool is specifically named "School AI."

  • Assignment Timeline:

    • Monday: Students are required to upload their drafts to the AI tool and submit the revised drafts based on feedback.

    • One-on-One Feedback: After receiving feedback from the AI and updating the document, students will meet for personalized feedback.

    • A final version of the document will then be submitted.

    • A celebratory party is planned due to the lengthy effort (five months) students have devoted to this assignment.

Data Presentation in Scientific Assignments

  • Importance of Data Tables:

    • Data tables should be well-structured to provide a clear view of the research findings.

    • Essential components of data tables include:

    • Titles

    • Headings

    • Units of measurement

    • Clear representation of data to avoid ambiguity, e.g., distinguishing between mass and volume.

  • Presentation Style:

    • Raw data collected during experiments may be messy ("chicken scratch"), whereas final submissions must be polished.

    • Example of a well-presented data table includes:

    • Trial numbers, concentrations, initial volumes, measurements, and uncertainties.

    • All measurements should maintain consistent precision as determined by the measuring device used.

  • Role of Data Tables and Graphs:

    • Data tables and graphs act as visual aids that complement the research paper, allowing others to understand and potentially replicate the experiment.

    • Important to make sure all entries are clear to enhance readability and comprehension.

    • A data table should reflect raw data and processed results with averages clarifying analysis results.

    • Inclusion of uncertainty in measurements enhances credibility and precision.

Elements of Graph Creation

  • Graph Requirements:

    • A graph should depict five data points after processing data averages.

    • Important components include:

    • An informative title.

    • Labelled axes with units of measurement.

    • Inclusion of a line of best fit, which must be determined based on the nature of the data (e.g., linear, quadratic).

  • Labeling Considerations:

    • Avoid using abbreviations or jargon like "carb"; instead, use clear terms that any scientific reader can understand.

    • Ensure that all components of the graph are clearly labeled to minimize ambiguity.

Writing the Conclusion

  • Conclusion Writing Guidelines:

    • Conclusions should start with observations derived from the graph, explaining relationships found (e.g., whether a positive or negative linear relationship exists).

    • Each conclusion needs to:

    • Include an explanation of the graph's trend (e.g., affecting variables).

    • Report the statistical significance or relevance of findings, such as the r squared value.

    • Interrelate findings with established scientific knowledge, supported by references.

    • Address any anomalies in the data that contradict established science, if applicable.

Structuring the Assignment and Citations

  • Document Structure:

    • Suggested structure includes:

    • Introduction and background: approximately one page.

    • Conclusion: approximately one page.

    • Evaluation: also about a page.

    • Total word count should not exceed 3,000 words, excluding figures and tables, and must use standard formatting (12 font, double-spaced).

  • Citation Requirement:

    • At least three citations should be included in the conclusion to provide context for the data presented.

Evaluation Criteria for Assignments

  • Grading Breakdown:

    • Research design: 6 points.

    • Data analysis: 6 points.

    • Conclusion: 6 points.

    • Evaluation of methodology/errors: 6 points.

    • Spelling or grammatical errors should be avoided to enhance clarity and professionalism.

  • Common Errors to Address:

    • Students are cautioned against neglecting the connections between data and potential errors or limitations in semantic clarity.

    • Practical aspects such as the choice of measuring instruments and their impact on results must be discussed.

Final Advice and Tips

  • Effective Communication Skills:

    • Emphasize the importance of strong writing skills in scientific research.

    • Strategies for creating effective narratives include using argumentative essay techniques to structure discussions and findings.

    • Students must ensure objectivity and clarity in their writing to support their investigations effectively.

  • Use of Statistical Tools:

    • Recommend using five trials for data to facilitate robust conclusions.

    • Simplify evaluations by deriving Pearson’s r for datasets to understand relationships and reliability in the data presented.