Unit rationale, description and aim
With the advancement of technology and computers capable of processing large sets of data, modelling and advanced analytics generate new evidence to inform a variety of sectors globally, including science-related disciplines. Given that science is a data-driven subject and explores real-world problems within a system, model-based reasoning and data-handling practices have become increasingly important in STEM education. Such practices require the capacity to critically evaluate the modelled evidence in order to form judgements and make decisions.
This unit will cover educational modelling tools and tasks, retrieving, selecting and using large data sets, instructional practices for teachers in STEM classrooms, Students will gain an understanding of meta-modelling skills, and critically analyse digital data to enhance school-wide evidentiary practices for data literate 21st century citizens. Students will communicate their knowledge to innovate professional practice.
The aim of this unit is to build students’ expertise in data modelling and innovate in-school practice by leading and communicating to colleagues.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Develop advanced skills in the use of data modelli...
Learning Outcome 01
Critically discuss data modelling practices regard...
Learning Outcome 02
Communicate knowledge, skills and ideas to special...
Learning Outcome 03
Synthesise complex information on learning and tea...
Learning Outcome 04
Content
This unit will cover educational modelling tools and tasks for both primary and secondary educational levels. It will cover simple data practices such as retrieving, selecting and using large data sets, that are freely available. Both, data modelling and data practices are supported by the introduction to contemporary, evidence-based instructional practices relevant to STEM classrooms, with appropriate assessment instruments to measure learning progression
Topics will include:
- Module 1: Acquiring modelling skills
- Gaining an understanding of agent-based and block-based modelling environments (e.g. SageModeler, NetLogo, StarLogo Nova, ViMAP), used at primary and secondary educational levels
- Identifying variables, relationships, systems behaviour in model building
- Understanding metacognitive modelling skills
- Module 2: Modelling with Big Data
- Exploring databases with credible, free data for STEM analysis
- Selecting data, identifying anomalous data and preparing data for analysis
- Developing awareness for the critical evaluation of evidence (evidentiary practices)
- Module 3: Instructional practices
- Discussing research on building student competencies in learning progressions for data and modelling practices
- Using a bifocal/multifocal modelling framework
- Developing synergistic learning, including systems thinking and computational thinking skills
- Module 4: Task design
- Using various types of scaffolding, the provision of practice examples, and class dialogue for task design
- Developing assessment instruments for modelling and data handling skills
- Investigating modelling processes and products deriving from socio-scientific and authentic learning units
Assessment strategy and rationale
The assessment tasks are designed for students to demonstrate achievement of each of the learning outcomes by progressing through the individual modules. They represent an opportunity for students to work in context of their professional setting. Assessment Task 1 on modelling and data practices will allow students to build on their prior subject knowledge, as they are choosing a socio-scientific topic for modelling and data practices. Assessment Task 2, designing a professional development opportunity for colleagues, is placed within the students’ professional context, either hypothetically or actually.
The assessment tasks are designed to provide students with the opportunity to meet the unit learning outcomes and develop graduate attributes and professional standards and criteria consistent with University assessment requirements (http://www.acu.edu.au/policy/student_policies/assessment_policy_and_assessment_procedures).
A variety of assessment procedures will be used to ascertain the extent to which graduates achieve stated outcomes. In order to pass this unit, students are required to submit or participate in all assessment tasks, and gain 50% or more for each task.
Overview of assessments
Assessment Task 1: Computational Model Building&...
Assessment Task 1: Computational Model Building
Students design and build a computational model, using freely available data sets regarding a socio-scientific issue, e.g. climate change. Students critically evaluate the modelled data to draw scientific conclusions.
(Written report, with log files)
50%
Assessment Task 2: Professional Development Work...
Assessment Task 2: Professional Development Workshop
Students design a professional development workshop that guides colleagues to acquire basic data modelling skills and to implement innovative and engaging learning and teaching programs in STEM. Students analyse and evaluate instructional practices relevant to the implementation of tasks with assessment, using research and with reference to the specific learning needs of students across the full range of abilities.
(Oral presentations, with resources)
50%
Learning and teaching strategy and rationale
The learning materials for this unit will cover key concepts in data practices and modelling, and contemporary, evidence-based research on relevant instructional practices. Learning will be active, collaborative and experiential, using practical activities to develop students' skills and content knowledge. This is achieved through a range of learning activities such as readings, reflection, practical tasks such as computational modelling, discussion, and engagement with webinars, podcasts and video sources. These will be presented in lectures, tutorials, or workshops.
Engagement for learning is the key driver in the delivery of this curriculum, therefore an active learning approach is utilised to support graduates in their exploration and demonstration of achievement of the unit’s identified learning outcomes.