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Unit outline_

EDPZ6012: Data Literacy for School Teachers

Semester 1, 2020 [Online] - Camperdown/Darlington, Sydney

The pressures and incentives that are driving the need for data literacy for school teachers come from many directions: (a) the move to a standards referenced system, (b) the integration of international, national and statewide high stakes testing programs, and (c) the requirement in the Australian Professional Standards for Teachers that directly address data literacy. In this unit data literacy is broadly defined as the ability to understand and use data effectively to inform teaching and learning decisions.

Unit details and rules

Academic unit Education
Credit points 6
Prerequisites
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None
Corequisites
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None
Prohibitions
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None
Assumed knowledge
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None

Available to study abroad and exchange students

No

Teaching staff

Coordinator James Tognolini, jim.tognolini@sydney.edu.au
Type Description Weight Due Length
Creative assessment / demonstration Formative Assessment and Engagement 1
short answer, multiple choice and true false formative assessment
5% Week 04
Due date: 20 Mar 2020 at 23:00

Closing date: 27 Mar 2020
250 words
Outcomes assessed: LO1 LO4 LO3 LO2
Skills-based evaluation Major Assessment (2) Stage 1
Develop task to use for data literacy analysis using real-life context
25% Week 05
Due date: 27 Mar 2020 at 23:00

Closing date: 03 Apr 2020
1,500 words
Outcomes assessed: LO2 LO3 LO4
Creative assessment / demonstration Formative Assessment and Engagement 2
short answer, multiple choice and true false formative assessment
5% Week 07
Due date: 09 Apr 2020 at 23:00

Closing date: 16 Apr 2020
250 words
Outcomes assessed: LO1 LO4 LO3
Creative assessment / demonstration Formative Assessment and Engagement 3
short answer, multiple choice and true false formative assessment
5% Week 09
Due date: 01 May 2020 at 23:00

Closing date: 08 May 2020
250 words
Outcomes assessed: LO3 LO5 LO4
Skills-based evaluation Major Assessment (2) Stage 2
Collection of student data based on task they designed in Stage 1
25% Week 10
Due date: 08 May 2020 at 23:00

Closing date: 15 May 2020
1,500 words
Outcomes assessed: LO3 LO4 LO5
Creative assessment / demonstration Formative Assessment and Engagement 4
short answer, multiple choice and true false formative assessment
5% Week 12
Due date: 22 May 2020 at 23:00

Closing date: 29 May 2020
250 words
Outcomes assessed: LO3 LO6 LO5 LO4
Skills-based evaluation Major Assessment (2) Stage 3
Consider an intervention program to enhance student learning improvement.
30% Week 14 (STUVAC)
Due date: 05 Jun 2020 at 23:00

Closing date: 12 Jun 2020
2,000 words
Outcomes assessed: LO4 LO5

Assessment summary

  • Formative Assessment and Engagement 1 – 250 words – 5%
  • Formative Assessment and Engagement 2 – 250 words – 5%
  • Formative Assessment and Engagement 3 – 250 words – 5%
  • Formative Assessment and Engagement 4 – 250 words – 5%
  • Major Assessment Stage 1 – develop task to use for data literacy analysis using real-life school teaching context – 1500 words – 25%
  • Major Assessment Stage 2 – collect student data based on task designed in Stage 1 – 1500 words – 25%
  • Major Assessment Stage 3 – consider intervention program to enhance student learning – 2000 words – 30%

Assessment criteria

  • Formative Assessment and Engagement 1 – 5%
  • Formative Assessment and Engagement 2 – 5%
  • Formative Assessment and Engagement 3 – 5%
  • Formative Assessment and Engagement 4 – 5%
  • Major Assessment Stage 1 – develop task to use for data literacy analysis using real-life school teaching context – 25%
  • Major Assessment Stage 2 – collect student data based on task designed in Stage 1 – 25%
  • Major Assessment Stage 3 – consider intervention program to enhance student learning – 30%

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

One week extension granted after negotiation with Supervisor.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introducing a conceptual framework for data literacy. Lecture (2 hr) LO1
Week 02 Data its definition, properties, types and classifications. Lecture (2 hr) LO1 LO3
Week 03 Data-driven decision making (DDDM) Lecture (2 hr) LO2 LO4
Week 04 Introducing Modern Assessment Theory Lecture (2 hr) LO1
Week 05 Using data: Generating data through formative assessments. Lecture (2 hr) LO1 LO4
Week 06 Using data:The role that formative assessment might play in providing feedback to improve teaching and learning. Lecture (2 hr) LO3
Week 07 Using data: Generating data through summative assessments. Lecture (2 hr) LO3 LO4
Week 08 Transforming data into information: Interpreting assessment results in a meaningful way. Lecture (2 hr) LO4 LO5
Week 09 Transforming data into information: Using NAPLAN and HSC data obtained from analysis packages to interpret results in a way that informs teaching and learning. Lecture (2 hr) LO4
Week 10 Transforming data into decisions: Identifying problems, framing questions and constructing hypotheses. Lecture (2 hr) LO4 LO5
Week 11 Using data: Some practical strategies for using data for improved learning. Lecture (2 hr) LO3 LO4
Week 12 Using data: Building collaborative communities of practice. Lecture (2 hr) LO4 LO5
Week 13 A new approach to writing data reports. Lecture (2 hr) LO3

Attendance and class requirements

This Unit of Study is delivered Online through Canvas requiring 1x2 hour lecture per week for 13 weeks to be completed at a time to suit the student.

A 2 hour Zoom is available once per week for students to attend if they wish to discuss the week’s lecture or clarify any queries.Attendance at this is not compulsory and lecturers will be available by email or face to face meetings at other times if requested.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Short/Indicative Reading List

Mandinach, E., & Gummer, E. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42, 30–37. http://journals.sagepub.com.ezproxy1.library.usyd.edu.au/doi/pdf/10.3102/0013189X12459803

Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S. and Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge Synthesis Report. Report produced by Dalhousie University. http://dalspace.library.dal.ca/bitstream/handle/10222/64578/Strategies%20and%20Best%20Practices%20for%20Data%20Literacy%20Education.pdf?sequence=1&isAllowed=y

Mandinach, E., and Jackson, S. (2012). What Research Tells Us About Data-Driven Decision Making. In Mandinach, E., and Jackson, S. (Eds.). Transforming teaching and learning through data-driven decision making. Thousand Oaks, CA: Sage Publications, 23 - 58. http://sk.sagepub.com.ezproxy1.library.usyd.edu.au/books/transforming-teaching-and-learning-through-data-driven-decision-making/n3.xml

Hattie, J. (2012). Visible learning for teachers: Maximising impact on learning. London; New York: Routledge. http://ebookcentral.proquest.com.ezproxy1.library.usyd.edu.au/lib/usyd/detail.action?docID=958163

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Define data literacy in relation to assessment literacy.
  • LO2. Define data-driven decision and consider some of the more common data-based decision making models.
  • LO3. Collect, organise, analyse, interpret, draw conclusions from and apply assessment results to modify teaching practice.
  • LO4. Interpret key statistics that are used to evaluate the outcomes from analyses of student assessment data, e.g.effect size; trend graph; standard error of measurement; confidence-intervals; and value-added.
  • LO5. Explain and give examples of how assessment results can be misinterpreted and lead to unintended consequences.
  • LO6. Explain the role of moderation in making assessments like the HSC and NAPLAN comparable.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

We welcome feedback on this Unit of Study. Please take the time to offer constructive written feedback at the end of the semester. The teaching team is committed to the participation of learners in the process of planning and evaluation of courses. The last unit of study evaluation was reviewed and no changes have occurred.

The unit meets the following Focus Areas of the Australian Professional Standards for Teachers: 5.1 Assess student learning 5.2 Provide feedback to students on their learning 5.3 Make consistent and comparable judgements 5.4 Interpret student data 5.5 Report on student achievement.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.