IB Internal Assessment (Lab) Scoring

IB Internal Assessment (Lab) Scoring

DATA COLLECTION & PROCESSING (DCP)

DCP- Aspect 1Recording raw dataComplete/2Records appropriate quantitative and associated qualitative raw data, including units and uncertainties where relevant.Partial/1Records appropriate quantitative and associated qualitative raw data, but with some mistakes or omissions.Not at all/0Does not record any appropriate quantitative raw data OR raw data is incomprehensible.DCP- Aspect 1: Recording raw dataWhat is raw data?Quantitative & Qualitative data that you directly collect during the lab (BEFORE any math is done)MassVolumeTemperatureObservations (qualitative)

What needs to be included in DCP- Aspect 1: Recording raw data?Title of Data TableColumns & rows completely labeledObservationsLevel of equipment uncertaintyLevel of precision in recorded data remains constant (same number of decimal places)

Title of Data Table; must beNumbered Table 1: -------Descriptive: includes both DV & IDV as well as detailTable 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 haveComplete label for column (or row)Correct Concentration of Sucrose SolutionIncorrect ConcentrationIncorrect Concentration of SolutionIncorrect Solution Concentration

Units!!(M) for MolarityAlways use metric system (no pounds or inches)

If Data table goes onto a 2nd page, you must include complete column headings again

Concentration of Sucrose Solution (M)Initial Mass (g)Final mass (g)00.20.40.60.81.0NOTICE!! 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)00.2Concentration of Sucrose Solution (M)Initial Mass (g)Final mass (g)0.40.60.81.0Page 1Page 2Observations; must haveDetail 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)Observations0Not sticky; bag has resistance; water dripping from string0.2Etc.0.4Etc.0.6Etc.0.8Etc.1.0Very sticky; bag looks more wrinklyDateNumbers of Days PassedHeight of Plant (cm)Observations9/6/1201.33 leaves (all green); stem straight9/7/1211.43 leaves (2 all green & 1 has a small brown spot); stem straight9/8/1221.43 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around9/9/1231.6A 4th leaf has sprouted; gnats not visible today9/10/1241.74th leaf green and the 1 brown spot is bigger today (2 mm diameter)9/13/1272.12nd stem beginning to branch out; leaves are the sameExample 1Example 2Equipment uncertainityIB 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 + 0.05g 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

12

56 mL +/- 0.5mL+/- 0.1g0.05g error for scale + 0.05g error when massing an object = .1g+/- 0.01g

5.1 cm +/- 0.05cm(you are estimating at the mm level)DateNumbers of Days PassedHeight of Plant (+/- 0.1cm)Observations9/6/1201.33 leaves (all green); stem straight9/7/1211.43 leaves (2 all green & 1 has a small brown spot); stem straight9/8/1221.43 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around9/9/1231.6A 4th leaf has sprouted; gnats not visible today9/10/1241.74th leaf green and the 1 brown spot is bigger today (2 mm diameter)9/13/1272.12nd stem beginning to branch out; leaves are the sameTable 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 TitleObservations- detailedUncertainty(here or in column heading)Complete column label with unitsHeight of Plant (+/- 0.1cm)DateNumbers of Days PassedBlue LightGreen LightRed LightYellow LightWhite LightObservations9/6/1201.31.0#.##.##.#B: 3 leaves (all green); stem straightG: ------R: ------Y:-----W:-----9/7/1211.4#.#

#.##.##.#B: 3 leaves (2 all green & 1 has a small brown spot); stem straightG: ------R: ------Y:-----W:-----9/8/1221.4#.#

#.##.##.#B: 3 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying aroundG: ------R: ------Y:-----W:-----9/9/1231.6#.#

#.##.##.#B: A 4th leaf has sprouted; gnats not visible todayG: Etc9/10/1241.7#.#

#.##.##.#B: 4th leaf green and the 1 brown spot is bigger today (2 mm diameter)G: Etc9/13/1272.1#.#

#.##.##.#B: 2nd stem beginning to branch out; leaves are the sameG: EtcTable 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 dayPractice scoring this table:

Mini-checklist:Title of Data TableColumns & rows completely labeledObservationsLevel of equipment uncertaintyLevel of precisionComplete/2Records appropriate quantitative and associated qualitative raw data, including units and uncertainties where relevant.Partial/1Records appropriate quantitative and associated qualitative raw data, but with some mistakes or omissions.Not at all/0Does not record any appropriate quantitative raw data OR raw data is incomprehensible.DESIGN- Aspect 2Processing raw dataComplete/2Processes the quantitative data correctly.Partial/1Processes quantitative data, but with some mistakes and/or omissions.Not at all/0No processing of quantitative raw data is carried out OR major mistakes are made in processing.DESIGN- Aspect 2 Processing raw dataWhat 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)(final height initial height) /12 days

Graph raw data & take slope of the line

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 1Examples 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)

Didnt take weekends into account;Slope (growth rate)= 0.21cm/daySCORE: 0 major mistake!Took weekends into account;Slope (growth rate)= 0.16cm/dayDESIGN- Aspect 3Presenting processed data

Complete/2Presents processed data appropriately and, where relevant, includes error bars and uncertainties.Partial/1Presents processed data appropriately, but with some mistakes and/or omissions. Not at all/0Presents processed data inappropriately OR incomprehensibly.Presentation= Table & GraphWhen 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 Dont 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 wasnt 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 tableBe sure you have the right type of graphWhen 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

C should not be part of axis; it should only be underneath next to temperatureFYI: This person also included equipment uncertainty here, but it isnt necessary.Asp 1- Scoring Practice: Why is this a 1/ partial?

Where are the observations??Temp listed under maggot #Should have table #!26

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 isnt 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 labelUnits go at the top of the column only!CONCLUSION & EVALUATION (CE)

DCP- Aspect 1ConcludingComplete/2States a conclusion, with justification, based on a reasonable interpretation of the data. Partial/1States a conclusion based on a reasonable interpretation of the data.Not at all/0States no conclusion OR the conclusion is based on an unreasonable interpretation of the data.Examples of scoring:DESIGN- Aspect 2Evaluating Procedure(s)Complete/2Evaluates weaknesses and limitations.Partial/1Identifies some weaknesses and limitations, but the evaluation is weak or missing.Not at all/0Identifies irrelevant weaknesses and limitations.Examples of scoring:DESIGN- Aspect 3Improving the InvestigationComplete/2Suggests realistic improvements in respect of identified weaknesses and limitations.Partial/1Suggests only superficial improvements.Not at all/0Suggests unrealistic improvements.Examples of scoring: