Commonly, students will do this y modifying the general aim provided and indicating the variable(s) chosen for investigation. The teacher may suggest the general research question only. Asking students to investigate some property of a plant’s cells, where no variables are given, would be an acceptable teacher prompt. This could be focused by the student as follows: “Does the cycloids of chloroplasts in Elodea leaf cells vary with Light intensity? ” Alternatively, the teacher may suggest the general research question and specify the dependent variable.
An example of such a teacher prompt would be to ask the student to investigate the effect of a actor that influences enzyme activity. This could then be focused by the student as follows: “Does ethanol concentration affect the activity of bovine catalane? ” It is not sufficient for the student merely to restate the research question provided by the teacher. Variables are factors that can be measured and/or controlled. Independent variables are those that are manipulated, and the result of this manipulation leads to the measurement of the dependent variable.
A controlled variable is one that should be held constant so as not to obscure the effects of the independent variable on the dependent variable. The variables deed to be explicitly identified by the student as the dependent (measured), independent (manipulated) and controlled variables (constants). Relevant variables are those that can reasonably be expected to affect the outcome. For example, in the investigation “How does the speed of movement of chloroplasts in Elodea cells vary with light intensity? “, the student must state clearly that the independent variable is the light intensity and the dependent variable is the speed of movement.
Relevant controlled variables would include temperature, preparation of Elodea cells, sample size and light quality (wavelength). Students should not be: given a focused research question told the outcome of the investigation told which independent variable to select told which variables to hold constant. Aspect 2: controlling variables “Control of variables” refers to the manipulation of the independent variable and the attempt to maintain the controlled variables at a constant value. The method should include explicit reference to how the control of variables is achieved.
If the control of variables is not practically possible, some effort should be made to monitor the variable(s). A standard measurement technique may e used as part of a wider investigation but it should not be the focus of that investigation. Students should be assessed on their individual design of the wider investigation. If a standard measurement technique is used, it should be referenced. For example, while planning an investigation to study the effect of light wavelength on the rate of photosynthesis in Cabman, the student may have adapted a method to measure the rate of photosynthesis taken from a textbook.
A Standard reference would then be expected as a footnote, for example, “Freehand, PI (1985) Problems in Practical Advanced Level Biology, Hooded and Sought. ” Or the student may adapt a general protocol provided by a teacher in a previous investigation. The reference may appear as: Michigan, J (2007) “Studying the rate photosynthesis” worksheet. Students should not be told: which apparatus to select the experimental method. Aspect 3: developing a method for collection of data The definition of “sufficient relevant data” depends on the context.
The planned investigation should anticipate the collection of sufficient data so that the aim or research question can be suitably addressed and an evaluation of the reliability f the data can be made. If error analysis involving the calculation of standard deviation is to be carried out, then a sample size of at least five is needed. The data range and amount of data in that range are also important. For example, when trying to determine the optimum pH of an enzyme, using a range of pH values between 6 and 8 would be insufficient.
Using a range of values between 3 and 10 would be better, but would also be insufficient if only three different pH values were tested in that range. Students should not be told: how to collect the data how ml_ACH data to collect. Data collection and processing Ideally, students should work on their own when collecting data. When data collection is carried out in groups, the actual recording and processing of data should be independently undertaken if this criterion is to be assessed.
Recording class or group data is only appropriate if the data-sharing method does not suggest a presentation format for the students. Pooling data from a class is permitted where the students have independently organized and presented their data. For example, they may have placed it on a real or virtual bulletin board. For assessment of aspect 1, students must clearly indicate which data is their own. Aspect 1: recording raw data Raw data is the actual data measured. This may include associated qualitative data.
It is permissible to convert handwritten raw data into word-processed form. The term “quantitative data” refers to numerical measurements of the variables associated with the investigation. Associated qualitative data are considered to be those observations that would enhance the interpretation of results. Uncertainties are associated with all raw data and an attempt should always be made to quantify uncertainties. For example, when students say there is an uncertainty in a stopwatch measurement because of reaction time, they must estimate the magnitude of the uncertainty.
Within tables of quantitative data, columns should be clearly annotated with a heading, units and an indication of the uncertainty of measurement. The uncertainty need not be the same as the manufacturer’s stated precision of the measuring device used. Significant digits in the data and the uncertainty in the data must be consistent. This applies to all measuring devices, for example, digital meters, stopwatches, and so on. The umber of significant digits should reflect the precision of the measurement. There should be no variation in the precision of raw data.
For example, the same number of decimal places should be used. For data derived from processing raw data (for example, means), the level of precision should be consistent with that of the raw data. The recording of the level of precision would be expected from the point where the student takes over the manipulation. For example, students would not be expected to state the level of precision in a saloonkeepers for them. Students should not be told how to record the raw data. For example, they should not be given a formatted table with columns, headings, units or uncertainties.
Aspect 2: processing raw data Data processing involves, for example, combining and manipulating raw data to determine the value off physical quantity (such as adding, subtracting, squaring, dividing), and taking the average of several measurements and transforming data into a form suitable for graphical representation. It might be that the data is already in a form suitable for graphical presentation, for example, distance traveled by woodlice against temperature. If the raw data is represented in this way and a best-fit line graph is drawn, the raw data has been processed.
Plotting raw data (without a graph line) does not constitute processing data. The recording and processing of data may be shown in one table provided they are clearly distinguishable. Students should not be told: how to process the data what quantities to graph/plot. Aspect 3: presenting processed data Students are expected to decide upon a suitable presentation format themselves (for example, spreadsheet, table, graph, chart, flow diagram, and so on). There would be clear, unambiguous headings for calculations, tables or graphs.
Graphs need to have appropriate scales, labeled axes with units, and accurately plotted data points with a suitable best-fit line or curve (not a scatterbrain with data-point to data-point connecting lines). Students should present the data so that all the stages to the final result can be followed. Inclusion of metric/SSL units is expected for final derived quantities, which should be expressed to the correct number of significant figures. The uncertainties associated with the raw data must be taken into account. The treatment of uncertainties in graphical analysis requires the construction of appropriate best-fit lines.
The complete fulfillment of aspect 3 does not require students to draw lines of minimum and maximum fit to the data points, to include error bars or to combine errors through root mean squared calculations. Although error bars on data points (for example, standard error) are not expected, they are a perfectly acceptable way of expressing the degree of uncertainty in the data. In order to fulfill aspect 3 completely, students should include a treatment of uncertainties and errors withered processed ATA, where relevant. The treatment of uncertainties should be in accordance with assessment statements 1. . 2, 1. 1. 3 and 1. 1. 4 of this guide. Conclusion and evaluation Aspect 1: concluding Analysis may include comparisons of different graphs or descriptions of trends shown in graphs. The explanation should contain observations, trends or patterns revealed by the data. When measuring an already known and accepted value of a physical quantity, students should draw a conclusion as to their confidence in their result by comparing the experimental value with the textbook or literature value. The literature consulted should be fully referenced.
Aspect 2: evaluating procedure(s) The design and method of the investigation must be commented upon as well as the quality of the data. The student must not only list the weaknesses but must also appreciate how significant the weaknesses are. Comments about the precision and accuracy of the measurements are relevant here. When evaluating the procedure used, the student should specifically look at the processes, use of equipment and management of time. Aspect 3: improving the investigation Suggestions for improvements should be based on the weaknesses and imitations identified in aspect 2.
Modifications to the experimental techniques and the data range can be addressed here. The modifications proposed should be realistic and clearly specified. It is not sufficient to state generally that more precise equipment should be used. Manipulative skills (This criterion must be assessed cumulatively. ) Aspect 1: following instructions Indications of manipulative ability are the amount of assistance required in assembling equipment, the orderliness of carrying out the procedure(s) and the ability to follow the instructions accurately.