The Internal Assessment (IA) - Practical Skills & Guidance

Today we're discussing the Internal Assessment, or IA. It's a significant part of your IB Chemistry grade, contributing 20%. Can anyone tell me what that means for your work?

It means we need to take it seriously since it affects our final grade a lot.

Exactly! Now, there are five criteria we will use to assess your IA. These include Personal Engagement, Exploration, Analysis, Evaluation, and Communication. Let's break these down. Personal Engagement is about how much initiative you show in your work. What's an example of showing personal engagement?

Maybe picking a topic I’m really interested in?

Great point! The more invested you are in your topic, the better your IA will reflect your passion. Remember, it’s about showing ownership of your project.

What about Exploration and Analysis? Can we have examples of those too?

Absolutely! Exploration involves defining a clear research question and methodology, while Analysis is about interpreting the data you've collected. Think of it like telling a story with your results!

So, how should we go about choosing our research question?

We’ll cover that in the next session! To summarize, the IA is your chance to undertake a scientific investigation that counts for a significant part of your grade. Understanding the assessment criteria is the first step to success.

Let's delve into how to formulate a research question. A good question is focused, specific, and investigable. Can someone give an example of a broad question?

How does temperature affect reaction rates?

That’s a good start but too broad! How can we make it more focused?

We could specify the reaction or the substances involved?

Exactly! For instance, 'How does the temperature affect the rate of the reaction between sodium thiosulfate and hydrochloric acid?' This is specific and clear.

What about controlled variables? How do they come into play?

Great question! Controlled variables ensure that your experiment is fair. You must keep other factors constant while changing your independent variable. This helps isolate the effects you wish to study.

Can we brainstorm some variables as a group?

Of course! Let’s write some potential variables on the board and start forming more specific research questions together. By refining your question and thinking of variables, you'll set a strong foundation for your IA!

Now that we have our research questions established, let’s discuss designing your methodology for your IA. What does a good methodology include?

It should explain how we’re going to carry out the experiment step-by-step.

Exactly! You also need to include background information to contextualize your research, define your variables, and list the materials and equipment you'll need. Can anyone give me an example of a controlled variable we might encounter?

Maybe the concentration of reactants?

Correct! Keeping the concentration constant helps ensure that any changes in the reaction rate are solely due to the variable you’re adjusting. This leads us to safety considerations. Why is that important?

We need to ensure we are safe while conducting our experiments!

Right! Always perform a risk assessment, noting potential hazards and outlining precautions to minimize risks. Always remember: safety first!

What about data collection?

Data collection is crucial for your analysis later. You should plan out how you will record your observations and results consistently. Let’s review what we’ve learned and solidify your understanding of creating a robust methodology.

Now that we've collected and recorded our data, let’s talk about how to analyze and present it effectively. What’s the first step in data processing?

We should organize our raw data into clear tables.

Exactly! Clear tables should have appropriate headings, including units of measurement. Let’s remember to include uncertainties too. Why is that important?

It helps in understanding the precision of our measurements.

Correct! Once you have your raw data organized, you will need to process it. Show your calculations clearly and ensure your results align with significant figures. Can anyone explain why this is important?

Because it gives our results credibility and shows attention to detail!

Well stated! Presenting your data graphically is also critical. What type of graph should we typically use for continuous data?

A scatter plot!

Great! Ensure that your graphs are well-labeled and include error bars to indicate uncertainties. Let's take examples from your last experiments and work on creating effective data presentations.