A guide to Group 4 Internal Assessments

I have worked with many students over my years of teaching Physics, Chemistry, and Maths with EIB Tutors, and one of the things that I often support is the students’ internal assessments. The focus for this article is advice for writing the Group 4 internal assessment. While this may have a good amount of similarity with the Maths exploration, there are some significant differences, so I will leave the Maths exploration as a separate topic, perhaps for another day!

I first include in this article a recommended structure for your IA. This structure is a standard format for scientific writing – most reports, from Master’s theses to scientific journals, will follow this in some form. However, for many students, this may be the first time they are required to produce a detailed report, and I hope that the structure will be of some use. If you don’t have time to go through the whole article, read the overview below, so you can get straight to writing! I understand – IA season is a hectic time. Additionally, I have examined in detail the Assessment Criteria for the IA, which provides a rubric for teachers and examiners to grade the internal assessment. This is freely available in the syllabus of the subject, but I have found that quite often students are not directed to this. I have also included notes on how to meet the criteria, and which sections of the structure serve to do this. Checking your IA against the criteria in the rubric as you write will ensure that you will receive a good result for the internal assessments.

 

Overview 

  1. Follow this general structure when producing your IA. This format will serve you well for any scientific writing, even all the way to writing scientific journals.
  2. At each time you have made a decision or choice regarding the design or process of the experiment, state it to show personal engagement.
  3. Any time you have an intermediate or final result from your raw or processed data, consider its implications to show critical thinking and evaluation.
  4. Label and caption your figures, tables, graphs, and graph axes!

IA Structure 

Introduction

  • Motivation
  • Research question
  • Background theory
  • Hypothesis

Materials and Methods

  • Variables: Controlled/Independent/Dependent
  • Apparatus/Materials
  • Diagram of apparatus
  • Methodology
  • Risk assessment:
    • Safety hazards
    • Environmental hazards
    • Ethical concerns

Data Collection and Analysis

  • Raw data:
    • Quantitative (tables) 
    • Qualitative (notes/observations)
  • Example calculation for analysis
  • Example error calculation
  • Processed data:
    • Tables
    • Graphs and other charts
    • Discussion of results with each chart

Conclusion and evaluation

  • Conclusion:
    • Review of results and comparison to hypothesis
    • Answer the research question as much as possible
    • State if results support the hypothesis. If not, why?
  • Evaluation:
    • Limitations of experiment
    • Sources of error
    • Possible improvements
    • Future investigations/extensions

Bibliography

Appendix

Grading Criteria 

The grading criteria are available in the subject syllabus, under “Internal Assessment”. The grades for each of these are assigned separately, from zero to their maximum, based on the descriptions. Note that maximum points does not imply perfect performance, just that they have satisfied the criteria in the rubric to achieve those required points. Unfortunately, many teachers are afraid to give maximum points due to this misconception. Put another way, the grading criteria are the equivalent of the markscheme for the internal assessment – if you fill the criteria, the marker should award you the points. However, there are a few criteria which are differentiated only by the adjectives they use, such as “some consideration” vs “full and appropriate consideration”. However, the majority of them are objective and concrete, so make sure you get as many of them as possible!

 

Personal Engagement (out of 2 points)

This is the most misunderstood criteria by students that I have worked with. The definition of personal engagement in the syllabus is “the extent to which the student engages with the exploration and makes it their own.” This is evidenced in two ways, both of which must be fulfilled at each level:

  1. How the research subject is of interest to them, or is directly related to their everyday life.

  2. What decisions they have made in terms of the design, implementation, or presentation of the investigation.

The mistake many people make with this is to think that it is only the first point, and dedicate paragraphs to stories describing why the research question is important to them, but forget to address the second. This allows them to fulfil one of the two criteria, i.e., even if you address point 1 very strongly, but neglect point 2, you will still receive 0 points for personal engagement.

In order to ensure that you address point 2, you must constantly show evidence of your personal decision-making. Examples of such places in the investigation would be:

  • Design: why you have chosen to investigate one case over another, e.g. investigation of one material over another.

  • Understanding/interest: what approximations you have made in the theory or calculations

  • Implementation: Why you chose the range of dependent variables

  • Implementation: Why you have chosen the number of data points taken, the time between readings, etc.

  • Presentation: How you present the data; why you have chosen to plot variables against each other

Many of these decisions may be made by factors out of your control, e.g., there are only certain materials available in the lab, the thermometer/temperature gauge only takes readings of this range, or the maths becomes incredibly complex without an approximation. However, you have still made a decision based on these restrictions, and so demonstrated how you have designed and carried the experiment within your limitations.

 

Exploration (out of 6 points)

For the exploration criteria, you are expected to demonstrate that you understand the theory and concepts behind the experiment, its context and risks, and that the science is of an appropriate level. The evidence for these criteria are generally presented in their relevant sections:

“The topic of the investigation is identified and a relevant and fully focused research question is clearly described.”

This should be described and clearly stated in your motivation and your research question. As this is a concrete criteria, dedicate a section to explicitly state your research question.

“The background information provided for the investigation is entirely appropriate and relevant and enhances the understanding of the context of the investigation.”

Demonstrate your understanding of the topic by writing a complete but concise background theory section. Usually this will be about 2 – 3 pages. When considering whether to write about a topic, remember that the report should be accessible to another IB student without the relevant background. Another way of putting this is:

  • if it is in the prior learning (if you’d have learnt it before the IBDP) you should not include it but only state the results;

  • if it is in the course, write about it briefly;

  • If it is beyond the course, write about it in detail.

Much of the background theory can be adapted from reference texts and other sources; be sure to cite these sources when you use them and add them to your bibliography. Also, cite as you write! Add to your bibliography as you write, especially for online references: it can be difficult to locate the site you got the reference from again afterwards.

“The methodology of the investigation is highly appropriate to address the research question because it takes into consideration all, or nearly all, of the significant factors that may influence the relevance, reliability and sufficiency of the collected data.”

The bulk of this will be demonstrated in the materials and methods section. The design and purpose of steps in the experiment should be made clear (another chance to show personal engagement). Remember to include steps on the number of repeats, and account for errors. A separate section explicitly stating all the controlled, independent, and dependent variables, and the measures to control them, will also go a long way to satisfying this criteria.

“The report shows evidence of full awareness of the significant safety, ethical or environmental issues that are relevant to the methodology of the investigation.”

 This criteria can be easily fulfilled with a risk assessment section in the materials and methods section. Consider reasonable hazards such as:

  • toxic chemicals used

  • broken glassware

  • heavy weights

  • objects under strain or pressure

  • strong magnetic fields

Include measures to mitigate or minimise these hazards, such as working in fume hoods, wearing lab coats/eye protection, and warning people with pacemakers to stay away from the magnetic fields. Also describe processes to take should there be spills and how to minimise environmental impact, such as proper disposal of reagents. Ethical issues, if any, should also be addressed in this risk assessment.

If the experiment does not pose any significant risks in these areas, make this statement so show that this aspect has been considered. Do not include negligible hazards just so that you have something to talk about – e.g. if you are working on a simulation or theoretical IA, carpal tunnel syndrome and ergonomics is not significant hazard: carrying out the IA does not cause you to have a higher risk than routine computer use. This particular criteria is noted to only be applicable should the risks be “relevant to the methodology of the investigation”.

 

Analysis (out of 6 points)

The analysis criteria exams how well you have collected, analysed, interpreted, and drawn conclusions from the experiments you have carried out. This will mainly be addressed in the data collection/analysis and conclusion sections.

“The report includes sufficient relevant quantitative and qualitative raw data that could support a detailed and valid conclusion to the research question.”

 This should be demonstrated with the tables of raw data that you have collected in the data collection section, which should be accompanied by any qualitative observations you have. For example, you may put down a table of temperature, pH, and absorbance of an aqueous compound as your quantitative data. This would be accompanied by a short paragraph about any observations you made which is not represented in the table, such as “fizzing was observed when the solid was added to the solution”, and “the colour change was only observed after a few seconds.” Ensure that the design of the experiment generates enough data, usually by repeating measurements three or five cycles, depending on time available.

If you have a large amount of raw data, you may choose to put this in the appendix so that they don’t take up many pages of the report. If you do so, make reference to the appendix in the data collection section, and make sure to put in a table of summary data, such as averages, in the main text.

 

“Appropriate and sufficient data processing is carried out with the accuracy required to enable a conclusion to the research question to be drawn that is fully consistent with the experimental data.”

Demonstrate the data processing in the data analysis section with an example calculation, paired with the example calculation. The full set of processed data can be presented in table form, followed by graphing if necessary. Use graphs to confirm relationships between dependent and independent variables – try to manipulate equations to give you linear plots, in order to calculate maximum and minimum gradients for your errors. This will allow you to make a conclusion, based on if the data is in line with your hypothesis. It’s fine if the experimental data does not agree with your hypothesis, as long as the conclusion (results agree/don’t agree) is consistent with the data. You will have the opportunity to comment on why there is a disagreement in the evaluation section.

 

“The report shows evidence of full and appropriate consideration of the impact of measurement uncertainty on the analysis.”

The analysis section should include an example error calculation, and each datum should have associated errors, whether it is raw or calculated. Include error bars in your graphs, unless they are too small to be seen, in which case make a note that the errors are too small to be visible on the graph. This will be used when comparing the results to your hypothesis or to literature values. For example, if your experimental value for g is 9.79 ± 0.05, and the literature value is 9.81, then you will be able to make a statement along the lines of: “the experimental value is in agreement with the literature value within the error of the experiment” since 9.81 falls within the range of the error. If you don’t have the error values, you will not be able to make this comparison. The size of the percentage error also allows you to evaluate the precision of your experiment, to be discussed in light of the evaluation criteria.

 

“The processed data is correctly interpreted so that a completely valid and detailed conclusion to the research question can be deduced.”

In the data analysis section, once you have the processed data, take some time to describe what the data is showing you- this can be as simple as, “the graph shows a straight line relation between x and y”, or “we can see from the data that y decreases as x increases”. This will allow the reader to follow the logic of the readings to the conclusion. Once again, the conclusion drawn has to be consistent with the data, address the research question, and will state if the results agree or disagree with the hypothesis.

 

Evaluation (out of 6 points)

Evaluation examines your ability to critically examine your procedure, data, and findings, and see if there are limitations. They are looking for evidence that you can identify shortcomings to the experiment, caused by design, equipment, or approximations. It also looks for evidence of you understanding how your results fit in with the current scientific context.

“A detailed conclusion is described and justified which is entirely relevant to the research question and fully supported by the data presented.”

This joins on from the analysis section – your conclusion should reference the research question, and answer it using all the results you have found from your experiments. “Fully supported” means that any conclusions you make must have the results as evidence. If you find that you are not able to answer all parts of the research question, explain this in terms of why this is so; it may be perhaps due to limitations in the availability of equipment, or complexity of the problem. This gives you things to talk about in terms of improvements to the experiment, or future investigations.

 

“A conclusion is correctly described and justified through relevant comparison to the accepted scientific context.”

The conclusion also needs to be compared with what is the accepted science – this is done either through comparing the values you have obtained with literature values (also sometimes called theoretical values), or discussing if the trends you have observed in your data are in line with scientific concepts. This will be a good time to refer back to the background theory section to explain your results in line with the relevant theory.

 

“Strengths and weaknesses of the investigation, such as limitations of the data and sources of error, are discussed and provide evidence of a clear understanding of the methodological issues involved in establishing the conclusion.”

The evaluation section will be where most of this is discussed, although it may be worth mentioning this at a points throughout the report, such as in the methodology for design limitations, or in the results in qualitative observations (e.g., “reaction Z did not produce any observable results due to the chemical being contaminated, so the results have been discarded”). Sources of error, both random and systematic, and to what degree your experiment has compensated for them. Discuss if these have a material effect errors on your calculations, and their precision and accuracy. They do not need to have all been corrected for: these errors may have only become evident as you were performing the experiment. Discussions and recognition of these show the critical thinking required.

 

“The student has discussed realistic and relevant suggestions for the improvement and extension of the investigation.”

This criteria is another reason it is good to bring up errors and limitations in experimental design: the “possible improvements” and “further experiments” section are ideal for fulfilling this. In the possible improvements section, from your list of limitations, suggest changes you can make to the experiment to try and correct the limitations. These suggestions can now include processes which were not possible due to equipment or time availability. For example, an experiment for enthalpy of combustion using a spirit burner and heating a beaker could be improved by instead using a bomb calorimeter, which may not have been available to you. Remember that the suggestions have to be realistic – equipment that would be available perhaps in a different lab would be fine, but suggesting conducting an experiment in space and zero-g might be pushing it a bit!

“Further experiments” serves a slightly different purpose from “possible improvements” – generally these are experiments where the scope of the research question has been broadened. For example, you may have investigated the period of a torsional pendulum based on the length of the pendulum weight. Examples of further experiments, or extensions, may be investigating different shapes of pendulum, different materials, and so forth.

 

Communication (out of 4 points)

This assessment criteria is on how you present your report, and how clear the focus, process, and outcomes are. It is important for notation to be consistent, and for the reasoning to follow a natural flow.

“The presentation of the investigation is clear. Any errors do not hamper understanding of the focus, process and outcomes.”

“The report is well structured and clear: the necessary information on focus, process and outcomes is present and presented in a coherent way.”

The reader should be able to have a good idea of what you are trying to do, how you are doing it, how you have reached your conclusions. This is the main purpose for setting out the IA in the recommended format: use the structure to state the required points for focus, work through the process, and present the outcome at the end. Note that it says “any errors do not hamper” – the IA doesn’t have to be perfect for this criteria, just that it has to be clear. Ensure coherence by being clear and concise in your writing, and avoiding unnecessary jargon.

 

“The report is relevant and concise thereby facilitating a ready understanding of the focus, process and outcomes of the investigation.”

Write about everything that is relevant to your investigation, and nothing else. Whether something is relevant or extraneous can be somewhat subjective, but keep asking yourself the question as you write, “is this relevant”? The structure can keep you on track.

 

“The use of subject specific terminology and conventions is appropriate and correct. Any errors do not hamper understanding.”

This criteria requires you to use appropriate and consistent notation and terminology. For example, if you define a value as k in one equation, do not use the term l for the same constant in another. This can easily happen if you are using equations from different texts, especially in fields such as Physics, Chemistry, and Engineering, where different notations and definitions are often used for the same concepts. Make sure you understand the equations you are referencing, and instead of copy/pasting them into your IA, rewrite the equations using an equation editor in your word processor so that you ensure consistent notation. Once again, note the “any errors do not hamper…”: it need not be absolutely perfect, but good enough that the reader does not get confused.