Use the below guiding questions and resources to design and plan your assessment and learning resources/activities to support student development of data literacy capabilities.

  Student learning outcome examples

• Collate, manage and access digital data in spreadsheets and other media
• Understand ethical, legal and security responsibilities when accessing data
• Critically appraise published data in order to understand potential limitations or biases
• Interpret data in databases and spreadsheets by running queries, data analyses and reports
• Produce data visualisations and reports
• Follow ethical, legal and security guidelines when recording, accessing, managing and using data
• Make informed and ethical choices about the use of data in personal and professional life

Assessment design

Example assessment types may include:

• Statistical analysis of data
• Research proposal
• Research ethics application
• Data security plan
• Research project
• Graphs or Plots
• Table or spreadsheet design
• Dashboard
• Analysis report

Marking criteria examples for Data Literacy

• Data management
• Data inclusions
• Data representations/visualisation
• Data analysis
• Ethical data use

Criterion standards case example

  Capability development case examples

Data literacy quiz 

Ask the students to complete this The Data Literacy Project: Self Assessment Tool in class, share their results with the person next to them and report back on a few key takeaways depending on your focus for the tutorial.

Working with data module

• Ask the students to complete the Working with Data Canvas Commons Module (based on the UQ Online Module, 30 mins) before or in class. To add this module to your subject site, visit this page <under construction> to find out how.

An introduction to the language and context of scientific data analysis 

• Vision Learning hosts online modules aligned to The Process of Science by Anthony Carpi and Anne Egger. There are four modules on data analysis, uncertainty and error, statistics, and data visualisation, that could be assigned to students as an asynchronous pre-reading and preparatory activity to help establish a common vocabulary and understanding of key data concepts, prior to engaging more deeply in discipline specific data activities in class. Each module has a quiz that students can use to self-assess their understanding. Other modules provide an introduction to the process of science as a socio-cultural endeavour, that may be helpful to draw upon in situating data literacy in the broader context of “doing science”.

Evaluating dataset quality

• Critical evaluation skills are important at a time when information is highly producible and accessible. Similarly, it is important for students to evaluate the authority, coverage, purpose, accuracy, and relevance of data to inform their inclusion in assignments.
• Example in-class activity: Select a number of data sets/research articles relevant to in class topics. In groups (or as a class), ask students to choose one of the data sets and use the table below to evaluate the quality of the data:
  • Authority – who collected data, affiliation to organisation, credentials
  • Coverage – is sample size representative of group/population, time frame
  • Purpose – why was data collected, intended audience
  • Accuracy – collection methods, is it a complete dataset, anything missing
  • Relevance – is data meaningful for your work

Data storage 

• Class discussion about where and how data can be stored. Create a simple scenario about a research project, and ask students to form groups and create a data flow diagram that considers:
  • Security considerations
  • Legal considerations
  • Ethical considerations
  • File hierarchy and naming conventions
  • Recording, accessing, and managing data, version control

Visualising data

• Provide a simple data set to students and in small groups, ask them to visualise the data, considering:
  • chart type based on data
  • inclusions and exclusions
  • choices they have made with intended audience in mind
• Groups present their graphs to intended audience (e.g. research conference, sales meeting, consumers). At the completion of each presentation, encourage students to ask questions and provide feedback based on the criteria.
• Alternative presentation: groups complete their graphs and swap with another group, without explanation about the graph. New groups allotted 2 mins to analyse the graph and present to class. Following presentation, graph creators correct any discrepancies with graph interpretation, followed by class discussion about how misinterpretations could have been avoided. Continue for all groups.

Analysing data 

• The National Science Teaching Association hosts a repository of case studies developed by the National Centre for Case Study Teaching in Science that can be filtered by data or statistics to help students develop data interpretation and analysis skills in problem-based contexts covering a variety of scientific and health topics. These resources are particularly aimed at an early undergraduate level and may provide a useful stepping stone for data analysis before students engage with academic publications in their discipline area. The case studies are made available for students and instructors in educational settings under the expectation of “fair use”.

Simulated experimental design and data analysis   

• The Islands is a synthetic dataset containing ethnographic and health data of simulated inhabitants across a series of Islands, developed by The University of Queensland (Bulmer and Haladyn, 2011). This resource can help provide a training ground for students to develop skills in designing experiments, designing research questions and methodologies, and analysing the resulting data before working with real datasets, particularly datasets involving sensitive human data. After completing their investigation, students could write a reflection on what worked and didn’t work, how they would amend their research plan and approaches to analysis if they were to complete a similar investigation in the future, and discuss any ethical or data stewardship considerations that might arise if they were to conduct the investigation on a real human population. This could be completed as a one-off exercise or form the basis of a portfolio of multiple investigations/reflections.

Interpreting published data:   

• Collect discipline relevant examples where data is used to establish outcome(s) and ask students to critique their relevance/validity. For example, students could consider:
  • Is the data appropriately presented?
  • Is there any possibility that the data could mislead?
  • Can the data be reasonably interpreted from just the visual representations, where relevant?
  • Does the context give meaning to the data?
  • Is the chosen analysis appropriate (e.g. has inference been carefully handled and statistical methods appropriately matched to the data)?
  • Are the results reproducible?
  • Are the conclusions derived from the data appropriate?

Source materials could include reports, articles, advertising materials, press releases etc. How do the answers to the questions above inform how students interpret the persuasiveness and trustworthiness of the outcomes being presented and how does that inform their own practice?

Planning for data collection and dissemination

• Scenario linked to topic provided to students. Students are to develop a research question related to unit content (or perhaps this is provided) and a methodology for data collection. They should think through the entire data lifecycle (e.g. will collected data be shared or stored in a repository, in alignment with FAIR principles; or will it be disposed of in accordance with appropriate data retention requirements?). Students can consider how planning ahead for data dissemination might influence how they strategise the data collection process. The QUT AIRS module ‘Managing Data’ provides some guidelines that students can use to benchmark their planning, to ensure that their intended data collection, management and processing protocols align with good practice.

  Resources

• Document your research data (UQ, online module)
• 35 incredible dataviz tools
• Bulmer, M., & Haladyn, J. K. (2011). Life on an Island: A simulated population to support student projects in statistics. Technology Innovations in Statistics Education, 5(1). https://escholarship.org/uc/item/2q0740hv

Further reading…