IB Internal Assessment (Lab) Scoring

DCP- Aspect 1 Recording raw data Complete/2 Records appropriate quantitative and associated qualitative raw data, including units and uncertainties where relevant. Partial/1 Records appropriate quantitative and associated qualitative raw data, but with some mistakes or omissions. Not at all/0 Does not record any appropriate quantitative raw data OR raw data is incomprehensible.

DCP- Aspect 1: Recording raw data What is “raw” data? Quantitative & Qualitative data that you directly collect during the lab (BEFORE any math is done) – Mass – Volume – Temperature – Observations (qualitative)

What needs to be included in DCP- Aspect 1: Recording raw data? Title of Data Table Columns & rows completely labeled Observations Level of equipment uncertainty Level of precision in recorded data remains constant (same number of decimal places)

Title of Data Table; must be… Numbered – Table 1: Descriptive: includes both DV & IDV as well as detail – Table 1: Initial & Final Mass of a Dialysis Tube Containing Five Different Concentrations of Sucrose Solution When Immersed for 20 Minutes

Columns & rows completely labeled; must have… Complete label for column (or row) – Correct  Concentration of Sucrose Solution – Incorrect  Concentration – Incorrect  Concentration of Solution – Incorrect  Solution Concentration Units!! – (M) for Molarity – Always use metric system (no “pounds” or “inches”) If Data table goes onto a 2 nd page, you must include complete column headings again

Concentration of Sucrose Solution (M) Initial Mass (g)Final mass (g) NOTICE!! The units are ONLY at the top next to the label. Units do NOT go next to the data (#) being recorded.

Concentration of Sucrose Solution (M) Initial Mass (g)Final mass (g) Concentration of Sucrose Solution (M) Initial Mass (g)Final mass (g) Page 1 Page 2

Observations; must have… Detail – If recording data over time (ex: each day for a week), then you will have specific observations every day Description – Be specific as to what you see but do not draw conclusions here

Concentration of Sucrose Solution (M) Initial Mass (g) Final mass (g) Observations 0Not sticky; bag has resistance; water dripping from string 0.2Etc. 0.4Etc. 0.6Etc. 0.8Etc. 1.0Very sticky; bag looks more wrinkly DateNumbers of Days Passed Height of Plant (cm) Observations 9/6/ leaves (all green); stem straight 9/7/ leaves (2 all green & 1 has a small brown spot); stem straight 9/8/ leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around 9/9/ A 4 th leaf has sprouted; gnats not visible today 9/10/ th leaf green and the 1 brown spot is bigger today (2 mm diameter) 9/13/ nd stem beginning to branch out; leaves are the same Example 1 Example 2

Equipment uncertainity IB Bio is different for error than IB Chemistry (yea!) IB Bio only requires that you look at the equipment you are using when collecting data; list the uncertainty for that equipment only (degree of precision is ± the smallest division on the instrument) – Ex for a scale: if the scale measures to the hundredths place, the equip. uncertainty is +/- 0.01g (can be found on bottom of scale)  0.05g error for scale g error when massing an object =.1g – Ex for ruler: If measuring in centimeters  +/- 0.1cm – Do NOT list for anything the teacher provides (example- if I make a solution for you, do not include uncertainty of graduated cylinder) List that information in 1 place near your raw data table

56 mL +/- 0.5mL +/- 0.1g  0.05g error for scale g error when massing an object =.1g +/- 0.01g 5.1 cm +/- 0.05cm (you are estimating at the mm level)

DateNumbers of Days Passed Height of Plant (+/- 0.1cm) Observations 9/6/ leaves (all green); stem straight 9/7/ leaves (2 all green & 1 has a small brown spot); stem straight 9/8/ leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around 9/9/ A 4 th leaf has sprouted; gnats not visible today 9/10/ th leaf green and the 1 brown spot is bigger today (2 mm diameter) 9/13/ nd stem beginning to branch out; leaves are the same Table 1: Height of Wisconsin Fast Plant When Exposed to Blue Wavelengths of Light over 7 Days Uncertainty of ruler measurement: +/- 0.1cm #’d and descriptive Title Observations- detailed Uncertainty (here or in column heading) Complete column label with units

Height of Plant (+/- 0.1cm) Date Numbers of Days Passed Blue Light Green Light Red Light Yellow Light White Light Observations 9/6/ #.# B: 3 leaves (all green); stem straight G: R: Y:----- W: /7/ #.# B: 3 leaves (2 all green & 1 has a small brown spot); stem straight G: R: Y:----- W: /8/ #.# B: 3 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around G: R: Y:----- W: /9/ #.# B: A 4 th leaf has sprouted; gnats not visible today G: Etc… 9/10/ #.# B: 4 th leaf green and the 1 brown spot is bigger today (2 mm diameter) G: Etc… 9/13/ #.# B: 2 nd stem beginning to branch out; leaves are the same G: Etc… Table 1: Trial #1- Height of Wisconsin Fast Plants When Exposed to Five Different Light Wavelengths over 7 Days NOTE: how to label data when have 2 titles for a column (height & color) NOTE: data is all showing same # of decimal places (“1.0” not “1”) NOTE: observations for all colors each day

Practice scoring this table: Mini-checklist: Title of Data Table Columns & rows completely labeled Observations Level of equipment uncertainty Level of precision Complete/2 Records appropriate quantitative and associated qualitative raw data, including units and uncertainties where relevant. Partial/1 Records appropriate quantitative and associated qualitative raw data, but with some mistakes or omissions. Not at all/0 Does not record any appropriate quantitative raw data OR raw data is incomprehensible.

DESIGN- Aspect 2 Processing raw data Complete/2 Processes the quantitative data correctly. Partial/1 Processes quantitative data, but with some mistakes and/or omissions. Not at all/0 No processing of quantitative raw data is carried out OR major mistakes are made in processing.

DESIGN- Aspect 2 Processing raw data What is “processed” data? This is the final data that you will use in order to answer your original research question. If your question is looking to compare a rate, such as a growth rate: – Raw data: height (cm) for each unit of time(day) – Processed data  amount of growth in cm per day (cm/day) You will use math (or a computer will use math) in order to convert your raw data into processed data. – An average is NOT considered enough to be counted as data processing (even though you will need to average trials before continuing into “processing”)

In order to process your data: You need to consider what data you have & what you want the data to look like in order to answer your question. If you are doing the math, you must show 1 example of each type of calculation. – Should come between raw data and your presentation of your processed data (table showing what you calculated) Also, in order to earn a “complete” for Aspect 2 in DCP, you must use all of your data points while processing.

Which processing is the weakest? Background  Raw data includes height of plant every school day totally 10 data points over 12 days (plant still grows over the weekend) trying to calculate rate of growth (cm/day) 1.(final height – initial height) /12 days 2.Graph raw data & take slope of the line 3.Calculate rate of growth between each recorded data point & then calculate the average Time (days) Height of Fast plant (cm) Score for Aspect 2 would be a “1”

Examples of scoring: Background  Raw data includes height of plant every school day totally 10 data points over 12 days (plant still grows over the weekend) Didn’t take weekends into account; Slope (growth rate)= 0.21cm/day SCORE: 0  major mistake! Took weekends into account; Slope (growth rate)= 0.16cm/day

DESIGN- Aspect 3 Presenting processed data Complete/2 Presents processed data appropriately and, where relevant, includes error bars and uncertainties. Partial/1 Presents processed data appropriately, but with some mistakes and/or omissions. Not at all/0 Presents processed data inappropriately OR incomprehensibly.

Presentation= Table & Graph When presenting your processed data in a table, it can be a new table or an extra column in an existing table. Just like all tables, it needs to have a complete title, column headings, degree of precision, etc. Also need to take into account  SIG FIGS – Don’t show your processed data to be more precise than the equipment you used to collect the data FYI: If your lab dealt with counting living organisms, then IB expects you to round to the nearest whole organism at the end in processing & then present that number. Show BOTH unrounded & rounded! – Sometimes the rounding of living organisms makes it look like there wasn’t a difference (ex: 1.4 #/day vs. 0.6 #/day  both round to 1 #/day)

Graphs are also numbered & have the same title as your table Be sure you have the right type of graph When labeling bar graphs (Excel calls them “column” graphs), take note of how to label the x-axis: Complete label & unit below; ONLY numbers on x-axis line

ºC should not be part of axis; it should only be underneath next to “temperature” FYI: This person also included equipment uncertainty here, but it isn’t necessary.

Asp 1- Scoring Practice: Why is this a “1/ partial?” Where are the observations?? Temp listed under “maggot #” Should have table #!

Asp 2- Scoring Practice: Why is this a “1?” Missing the example/sample calculation! What if this student had only calculated an average?  An average is NOT sufficient math to be considered processing! Therefore, there isn’t any processing.

Asp 3- Scoring Practice: Why is this a “1/ partial?” Processed data should ALSO be in a table! Units do not go on x-axis! They go with the label Units go at the top of the column only!

DCP- Aspect 1 Concluding Complete/2 States a conclusion, with justification, based on a reasonable interpretation of the data. Partial/1 States a conclusion based on a reasonable interpretation of the data. Not at all/0 States no conclusion OR the conclusion is based on an unreasonable interpretation of the data.

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DESIGN- Aspect 2 Evaluating Procedure(s) Complete/2 Evaluates weaknesses and limitations. Partial/1 Identifies some weaknesses and limitations, but the evaluation is weak or missing. Not at all/0 Identifies irrelevant weaknesses and limitations.

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DESIGN- Aspect 3 Improving the Investigation Complete/2 Suggests realistic improvements in respect of identified weaknesses and limitations. Partial/1 Suggests only superficial improvements. Not at all/0 Suggests unrealistic improvements.

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