Working Scientifically Resources, Part 3

Working Scientifically is the overarching concept in the new science syllabi produced for schools. (Although the K-6 syllabus needs to emphasise this more). If you look at the organisation of the 7-10 syllabus below you can see that Working Scientifically encompasses both the Focus Areas and Depth studies. This article is the third of four (one article each term) which I hope will help teachers that need to clarify their ideas so that the Working Scientifically skills can be integrated into science lessons. We tend to concentrate on the content material rather than the skills and I hope these articles will bring a better balance so that Working Scientifically skills are developed and taught continually in science classrooms just as much as the science content. NOTE: Just because a skill is not mentioned in a Focus Area does not mean that it can’t be developed during lessons in that Focus Area.

I am structuring the articles within the 4 issues of SEN as follows:

• Term 1 Observing, questioning, predicting (see SEN Vol 74 No. 1)
• Term 2 Planning investigations, conducting investigations. (See SEN Vol 74 No. 2)
• Term 3 Processing data and information, Analysing data and information.
• Term 4 Problem solving, communicating.

I would like to pose a question:

How do we learn and develop a skill?

If you are like me, I need to first learn the various aspects of the skill and practice each aspect several (or many!) times in order to master that particular skill so that I could perform the skill with competence. Can students learn and develop a skill with competence with just one try at a Working Scientifically skill? NO. We need to allow students to practice Working Scientifically skills over as many relevant lessons as possible so that they can become competent in that skill.

It is very important that we allow students time to develop their Working Scientifically skills as we work through the new syllabi – Primary, 7-10 and Stage 6.

In this section I refer to two resources that are available:

• NESA Teaching and Learning support Science 7-10 (2023) Working Scientifically Processes Guide (Look for Appendix 1 - starts p 39)
• NSW Government Education Public Schools ‘Guidelines for some working scientifically skills’ State of NSW, Department of Education, 2017 Learning and Teaching

Processing data and information

The acquisition, recording, organisation, retrieval, display, and dissemination of information and data

Data processing is the conversion of data, often in a raw form, into a usable, understandable, and more valuable format. It involves various steps, including collection, organization, storage, retrieval, and the use of data to achieve specific goals or generate insights. 

As you can see in the above definition (courtesy of AI), this is a very wide-ranging skill. I will try to break it down as much as possible. The importance of this skill is that it is used more and more in Stage 6 courses and not only science courses. The following resources, as noted above, can give some conventions for each of the following aspects of Working Scientifically skills.

• NSW Government Education Public Schools ‘Guidelines for some working scientifically skills’ State of NSW, Department of Education, 2017 Learning and Teaching
• NESA Teaching and Learning support Science 7-10 (2023) Working Scientifically Processes Guide

Stage 4 outcomes

• Extract information from texts, diagrams, flow charts, tables, databases, graphs and multimedia resources
• Use a range of representations to organise data, including graphs, keys, models, diagrams, tables and spreadsheets
• Include sources, titles, labels and scales when displaying data in a graph
• Select the type of graph best suited to represent various single datasets and justify this choice
• Calculate the mean and range of a dataset
• Convert between units of measurement

Extract information from texts, diagrams, flow charts, tables, databases, graphs and multimedia resources.

TEXTS: This skill of extracting information from texts is the basis of scientific literacy. Unfortunately, it is also the skill which we tend to not emphasise in the mix of skills. Reading for understanding is vitally necessary for all science students at all levels as it will overflow into their everyday lives in the future. It just so happens that I am reading the book ‘OCEAN Earth’s last Wilderness’ by David Attenborough and Colin Butfield at the moment. It requires an understanding of ecosystems, oceanography, climate change and species anatomy among other scientific concepts. Even if students don’t read books anymore!, they will need to understand articles about energy, climate, animals and plants in ecosystems, etc. in their everyday lives. One way to develop this skill is to have text in assessments that will evaluate the student’s ability to draw out knowledge and understanding so that it can be applied in another situation. Obviously, the reading material needs to be at the appropriate level for the Stage they are in.

DIAGRAMS: Diagrams are used in HSC assessments extensively. There are a variety of types of diagrams that can be used in assessments, therefore using a variety of diagrams for students to practise how to interpret and extract information is important. Being able to extract information and meaning from this type of stimulus material is essential for science students. View the NESA reference above (pages 60, 63-68) for different types of diagrams. The Department resource also has information online and Lewis diagrams.

FLOW CHARTS: Extracting information from a flow chart requires an understanding of how a flow chart is constructed. The Department resource (above) has a section on flow charts and the NESA resource has information on pages 61-62.

TABLES: Extracting information from tables requires that students understand the format of a table. A table should show the independent and dependent variables and the data gathered while conducting an investigation. The Department resources has a section on tables as does the NESA resource on page 40. The aspects of a table that I find are important for Stage 4 is that, as a general rule, the independent variable is located in the left hand column and units should be in parentheses in the heading row only, with numbers only in the columns.

DATABASES: Information in a database is something that students need to be able to understand as they research for Depth Studies in Stages 4-6. Probably the best way to help students understand the idea of databases is to refer to information gathered by your school on students. Different types of information is gathered about students including qualitative and quantitative data. Databases can be organised in many different ways. In Stage 4 it is important to start simply and gradually develop the skill to the end of Stage 5 i.e. I do (teacher), We do (teacher & student), You do (student).

GRAPHS: There are several types of graphs; line, column, bar, etc. The best way to look at these types is to use the NESA resource pages 41-52. The Department resource also has a section on graphs and charts. It is important that students understand how to read graphs in order to extract the information presented. An important aspect in reading graphs is to identify the type of data that has been collected i.e. quantitative, qualitative, continuous, discrete data.

MULTIMEDIA RESOURCES: The information in these resources is varied as it can be presented as video, audio, photographs, animations as well as a combination of these which also could include using text. These types of resources are used by students every day but they need to be taught how to extract relevant information from them. Note taking is a skill students need to develop so they can extract relevant information efficiently.

Use a range of representations to organise data, including graphs, keys, models, diagrams, tables and spreadsheets.

In order to organise data students need to collect data, therefore these skills should be developed during practical investigations.

GRAPHS: There are several different types of graphs so instead of going through each one I want to refer you to the Department and NESA references (above). They have conventions for each as well as what is included in a good graph for that type of graph. In the NESA resource there are also examples of graphing errors. In my school we generally look for the following in a line graph:

• use of pencil and eraser (to correct mistakes)
• a title indicating the relationship of Independent and dependent variables, even scales on axes
• label and units on each axis (unit abbreviation in parentheses i.e.(cm)
• independent variable on the x-axis and dependent variable on the y-axis
• data points correctly plotted (with data points that can be seen!)
• line or curve of best fit, if necessary

KEYS: The NESA resource (above) has examples of the use of keys in various situations. Note pages 42, 48-52, 59, 64, 67. The other aspect of the need for a key is in the pedigree diagram even though this is in Stage 5. The bottom line for keys is to use a key when different aspects in a diagram, graph or chart need to be identified. Keys are important for the interpretation of information in a resource. Dichotomous key example below:

MODELS: This dot point indicates that students need to organise data in order to develop a model from the data. For example, this can be physical modelling made from data about the solar system such as the size of planets or planet distance from the Sun, modelling a concept such as the Bohr atom model or a mathematical model like the stretch of a spring as different mass is added to the spring. Modelling can be done using a graph as well as a mathematical equation developed. (of course this depends on the mathematical skills of the students!)

DIAGRAMS: There are several types of diagrams in science. The NESA resource gives conventions for several types of diagrams on pages 56-60 and 63-68. Again the use of pencil and eraser is important if mistakes are made. Any diagram needs to communicate any information gathered, for example about plant and animal cells.

TABLES: This dot point develops the skill of collecting data and developing a table to present the data. In the NESA and Department resources above, the criteria and conventions for tables are given. Tables show the structure of the data and a graph is the picture of that data. One thing that I find is that students need to learn to place units in the table heading row only and not place units in the columns, only the numbers (see image above under tables).

SPREADSHEETS: This requires the use of spreadsheets like EXCEL. Depending on the expertise of the students, they will need to be able to enter and manipulate data using the spreadsheet. Remember that a database is not a spreadsheet. The spreadsheet enables data from the database to be manipulated in specific ways.

Include sources, titles, labels and scales when displaying data in graphs

This dot point indicates the aspects of developing a graph in Stage 4. As noted above the graph needs a title showing the independent and dependent variable relationship i.e. Total energy vs Height. Labels for the graph need to be on the correct axis ie x axis Independent, y axis dependent. The labelling needs to have units (can be abbreviation) in parentheses i.e. (cm). Scales need to be evenly distributed along the axes so that the graph takes at least 2/3 of the area of the graph. A graph grid should not have to be extended or the graph should not just take up a small corner of the grid! Students need to learn to look at the data to find the highest and lowest data values gathered and adjust scales to these numbers. NOTE: the scales on the 2 axes do not have to be the same ie x axis 10,20,30,40 - y axis 5,10,15,20. I have left sources to the end as this depends on the type of investigation that is done. If the data is gathered from secondary sources then it is important to acknowledge those sources when presenting the graph.

Select the type of graph best suited to represent various single datasets and justify this choice

Again the NESA and Department resources give the basic information for the use of a graph to represent a certain type of dataset. Note that the dot point includes the verb justify. This means that students need to give a good reason or reasons to support their choice of the type of graph used. Ask the simple question: Why did you choose that type of graph for this data? A good question for an assessment!

Calculate the mean and range of a dataset

These statistical calculations need to be checked with your Mathematics Department to see when they are presented to students. You may have to teach these if they have not been taught these in Mathematics yet. It will be a good revision if they have been taught mean and range. It is a good way to make students realise that Science uses Maths skills! Also see the Data Science 1 focus area outcomes.

MEAN, or the average, is the addition of values and then divided by the number of those values. Example: 2+5+3+2=12÷4=3 Include units which is generally necessary for science!

RANGE is the difference between the highest and lowest values in the dataset. Example: 5-2=3

Convert between units of measurement

This dot point requires students to convert scientific units of measurements. The chart above can help with the conversions. Notice the mnemonic about King Henry! for remembrance. Also check with Mathematics to see if students have been exposed to the conversion of units. There may also be some information in the Data Book resource for Science 7-10.

Stage 5 outcomes

• Select and use a range of representations to organise data and information, including graphs, keys, models, diagrams, tables and spreadsheets
• Select and extract information from texts, diagrams, flow charts, tables, databases, graphs and multimedia resources
• Calculate a range of descriptive statistics using SI units
• Identify data which supports or refutes questions, hypotheses and proposed solutions to problems
• Describe specific ways to improve the quality of data collected in an investigation

Select and use a range of representations to organise data and information including graphs, keys, models, diagrams, tables and spreadsheets

This dot point is similar to the Stage 4 2nd dot point except for the words Select and Information. Refer to the Stage 4 dot point above for specifics but I want to look at what is necessary to do for Stage 5. This dot point points to the development of a Depth Study where students select how they are going to organise the data they generate in an investigation as well as any research information that has been gathered for the Depth Study. The skills to organise data and information should have been developed in Stage 4 so that students can utilise those skills at an individual level. Remember that Depth Studies can be both practical investigations as well as secondary source investigations. They can be individual or group work. Note the syllabus guidelines for Depth Studies.

Select and extract information from texts, diagrams, flow charts, tables, databases, graphs and multimedia resources

This is again the same as the Stage 4 1st dot point with the addition of the word Select (see the specifics in the Stage 4 dot points). The information about select in the above dot point is similar except that this dot point looks at students extracting information for any investigation: first hand or second hand investigations, as well as individual or collaborative work. This dot point requires that students develop these skills in Stage 4 so that they can be utilised by students in Stage 5 thus transferring this skill into Stage 6 courses.

Calculate a range of descriptive statistics using SI units

This is where you need to talk to your Mathematics Department in order to see how much has been taught about statistics. Descriptive statistics summarise a data set. This could include mean, median, mode, range, standard deviation, variance, interquartile range, skewness and possible others. The Maths Department should be able to guide you into what and when Stage 5 students should know about these aspects of statistical analysis. Also see the Data Science 2 focus area outcomes. However, I haven’t seen any information about statistics in the Data Book resource as referenced in the above Stage 4 dot point on conversion of units.

Identify data which supports or refutes questions, hypotheses and proposed solutions to problems

This seems fairly straight forward but the ability to find data to support or refute questions, hypotheses and proposed solutions need to be practiced. This can be done by having students evaluate each practical investigation they complete to see if their data supports their hypothesis. If not, have students explain why the data refutes the hypothesis. Various databases i.e. climate change data or data from an ecosystem study such as the coral reef, can be used to help students practice identifying data which can be used to support or refute proposed solutions to a problem. This is why it is important to have students write up their investigations so that they can analyse their data in order to answer the questions posed in this dot point.

Describe specific ways to improve the quality of data collected in an investigation

Quality of data is characterised by the following three words: accuracy, reliability and validity. Accuracy has to do with the way that data is collected. Was the correct equipment used and was it able to measure as accurately as possible. Another aspect of accuracy is how the use of the equipment by the investigator allowed accurate data to be collected. Was the equipment used correctly? Reliability is the ability to collect data which is consistent over several trials. The more trials with consistent results the greater the reliability. Validity is the aspect of an investigation that shows the investigation has accomplished the purpose or aim of the investigation. This can be shown through the reliability and accuracy that was accomplished in the investigation. The design of the experiment and its execution is crucial for the validity of the investigation. The independent and dependent variables change but all other variables in the investigation need to be controlled. Another important aspect is that the method of the investigation should be able to be replicated by another investigator and achieve similar results. This would enhance the validity and reliability of the investigation. Having another student undertake the method of another person’s investigation can emphasise the importance having a repeatable method!

Analysing data and information

Analysing information involves breaking down data or ideas into their component parts to understand how they function and interact, and drawing conclusions based on that understanding. It's a process of careful examination and interpretation to gain insights and make informed decisions. The Working Scientifically skills development for analysing data and information in Stage 4 needs to be developed incrementally over year 7 & 8. The NESA definition of analyse is: Identify components and the relationship between them and Draw out and relate implications.

Stage 4 outcomes

• Assess the reliability of gathered data and information by comparing it to observations and information from other sources, including published scientific writing
• Identify patterns and relationships in graphs, keys, models, diagrams, tables and spreadsheets
• Identify data which supports or refutes a testable statement being investigated or a proposed solution to a problem
• Use scientific understanding to identify relationships and draw conclusions based on students’ data and secondary sources
• Propose inferences based on presented information and observations
• Evaluate the method used to investigate a question or solve a problem, including evaluating the quality of the data collected and identifying possible improvements to the investigation

Assess the reliability of gathered data and information by comparing it to observations and information from other sources, including published scientific writing

Assess means that students need to form an opinion about the data and information that has been gathered in an investigation or Depth Study. The reliability of that data indicates that students have observed and gathered other resource information about that investigation or Depth Study and found that the results are either similar or not similar. This will give students the ability to form the opinion. (Note: we assess students all the time!)

Identify patterns and relationships in graphs, keys, models, diagrams, tables and spreadsheets

GRAPHS: the pattern that graphs show is in the shape of the graph. The variables can be in a linear relationship i.e. as one variable increases the other variable increases OR as one variable increases the other variable decreases. Curved lines show changing relationships i.e. as one variable increases the other variable may increase to a point and then decrease. The graph is a picture of the data in a table.

KEYS: The patterns and relationships in keys can be seen in paired options such as in a dichotomous key. The relationships in these keys are based on the characteristics of the objects being studied. Keys should also be used in pedigree charts in order to clarify any traits that might be inherited. They can also be used in Punnett square problems to indicate various characteristics such as phenotypes or genotypes.

MODELS: There are several types of models that can be used in science. Physical models such as a model of the earth, solar system, body organs, etc. Mathematical models such as physics formulas or equations as well as chemical reaction formulas. Conceptual models such as a model of an atom or ecosystem. Computer models such as simulations of cells or chemical reactions. A scientific model helps students to understand concepts through visualisation that are either too big or too small for students to comprehend. One model that we have used at school is a model of the phases of the moon. This example uses a model to help students understand the pattern of movement by Earth and Moon to visualise the moon phases.

DIAGRAMS: We use diagrams quite a bit in science to get students to understand patterns and relationships. One diagram that I use is the structure of atoms when looking at the structure of the periodic table. Visualising the numbers of electrons in the valence shells help students start to understand concepts behind chemical reactions.

TABLES: Patterns and relationships in any table can start to identify what is happening in data that is collected. Many times students can identify a pattern in the collected data but a graph can then give a picture of that relationship.

SPREADSHEETS: Placing data in a spreadsheet allows that data to be manipulated so patterns and relationships can be identified depending on the function or functions used in the spreadsheet.

Identify data which supports or refutes a testable statement being investigated or a proposed solution to a problem

In science a testable statement is similar to a hypothesis. The statement can be tested using the scientific method. The statement needs to contain an independent variable and a dependent variable with any other variables controlled. The data collected can then be used to either support or refute the hypothesis (testable statement) or propose a solution to a problem. An example of a testable statement might be; ‘A plant grown in a dark environment will grow faster than a plant grown in an environment with light.’

Use scientific understanding to identify relationships and draw conclusions based on students’ data and secondary sources

This dot point indicates the need for Stage 4 students to understand not only the science content but also the scientific method. Both of these aspects of science help students identify relationships in collected data and sources identified during an investigation. This will then allow students to make conclusions based on their investigations. This is the reason for emphasising Working Scientifically skills presented in the syllabus.

Propose inferences based on presented information and observations

An inference is an opinion statement based on any data or observations gathered during an investigation. The NESA glossary states that an inference is ‘ a conclusion based on evidence and reasoning’. Students need to be able to develop statements, opinions and conclusions to their experiments or Depth Studies. An inference based on the testable statement two dot points above could be ‘The plant that grows in the dark environment will not grow better than the plant that grows in the light environment.’

Evaluate the method used to investigate a question or solve a problem, including evaluating the quality of the data collected and identifying possible improvements to the investigation

Evaluate means to make a judgement about or determine the value of a method and data collected in an investigation. Notice that the verb evaluate mentions method and quality of the data collected. As mentioned above these skills need to be developed incrementally over the two years of Stage 4. This means that in the beginning students should be guided and then gradually give students more ability to make decisions on the way investigations are conducted to provide relevant results (a staged development i.e. I do/teacher, We do/together, You do/student). Students also need to be able to suggest improvements to an investigation if it was to be conducted again.

Stage 5 outcomes

• Describe patterns and trends, including inconsistencies in data and information
• Describe relationships between variables
• Assess the validity and reliability of first-hand data
• Use graphed data from investigations to extrapolate or interpolate information to make predictions
• Use knowledge of scientific concepts to draw conclusions that are consistent with evidence
• Synthesise data and information to develop evidence-based arguments
• Evaluate conclusions and evidence, including identifying sources of uncertainty and possible alternative explanations
• Analyse the validity of information from secondary sources

Describe patterns and trends, including inconsistencies in data and information

Students need to be able to look for patterns in any data generated or information gathered. Any trends need to be explained through the patterns in the data or information. A trend is how the data or information changes. As students look at the patterns and trends in data or information they need to identify any inconsistent data or information that has been gathered. Inconsistent means doesn’t fit into the pattern or trend observed in the investigation. Any inconsistencies noted need to be described and possible reasons for that inconsistency given.

Describe relationships between variables

Variable relationships mean how one variable changes in relation to another variable. So if an independent variable goes up and the dependent variable also goes up this is called positive correlation. Negative correlation is when one variable increases the other variable goes down. There is zero correlation if variables don’t show a definite relationship. In the Science 7-10 syllabus glossary the term correlational relationship is defined as ‘a relationship that measures the strength of interdependence of 2 variables’. Thus, if points on a graph are in a straight line the relationship is strong but if data points are widely scattered then the relationship is weak depending on the scatter of the plotted data. Note: these relationships are seen better when graphed!

Assess the validity and reliability of first-hand data

Data is valid if it measures what it is intended to measure in the investigation/experiment. This means that all variables are controlled except the independent (IV) and dependent (DV) variables. Data is reliable when the experiment is repeated several times and the data is consistent with the original data. In order to assess these aspects of an investigation students must make a judgement about the first hand data i.e. are variables controlled, except the IV snd DV and is the data consistent after several repeated trials have been completed. At school we usually indicate the need to do at least 3 trials. Remember that first-hand data is gathered from ‘hands on’ experiments.

Use graphed data from investigations to extrapolate or interpolate information to make predictions

Extrapolate means to extend the line of a graph past the data points, either past the last data point or before the first data point. Because there are no data points in these areas of the graph students need make predictions of possible values in these areas. Interpolate means to be able to predict point values between the graphed data points. A ‘line of best fit or curve of best fit enables these predictions to be made with a degree of accuracy.

Use knowledge of scientific concepts to draw conclusions that are consistent with evidence

This dot point requires students to use what they have learned in science content and skills to enable them to form a conclusion based on the first-hand data or second-hand information gathered in an investigation. Students need to be able, in Stage 5, to identify how data gathered from sources in investigations is consistent with the science concepts that the investigation is based on.

Synthesise data and information to develop evidence-based arguments

Synthesis means to put together any first-hand data and reliable secondary source information to build up evidence to support a conclusion to an investigation. An evidence-based argument is a judgement that is based on facts, data or credible information. Opinion or belief does not lead to an evidence-based argument.

Evaluate conclusions and evidence, including identifying sources of uncertainty and possible alternative explanations

Evaluate means to make a judgment about any conclusions and the evidence used to make those conclusions. In the process of the making the judgment students need to think about any possible data or information that might cause any alternatives to the conclusion being made. If the sources don’t support evidence, then could there be any other explanation? Do those sources have a different interpretation based on relevant data? A common source of uncertainty is the lack of accuracy in gathering of data in an investigation. Also when using secondary sources students need to be able to identify any potential bias or lack of integrity in a source of information i.e. we don’t want students to use Wikipedia sources, they must use reliable and credible sources such as government institutions like CSIRO, ANSTO. In terms of AI we need to teach students how to check if reliable sources are behind that information i.e. check other reliable and credible sources!

Analyse the validity of information from secondary sources

To analyse validity of secondary sources students need to know how the information is gathered by the secondary sources, what scientific criteria was used to gather the information and whether the information gathered is relevant to the hypothesis of the investigation or problem. Giving students a list of credible organisations and websites at the beginning of any Depth Study or investigation will help students to understand that science needs to be based on credible information not on anything that might be found on a search engine!