Year
2024Credit points
10Campus offering
Prerequisites
Nil
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.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Develop advanced skills in the use of data modelling tools and applications, and demonstrate an understanding of the critical selection and use of large data sets (APST HA 1.1, 1.2, 2.2, 2.5, 2.6, 4.5) | GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11 |
| LO2 | Critically discuss data modelling practices regarding conceptual understanding of socio-scientific issues, computational thinking, and systems thinking (APST HA 1.1, 1.2, 2.2, 2.5, 2.6, 4.5) | GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11, GC12 |
| LO3 | Communicate knowledge, skills and ideas to specialist and non-specialist audiences on data handling and computational modelling in STEM education (APST HA 2.1, 3.4, 4.1, 4.5, 6.2) | GC1, GC2, GC3, GC4, GC6, GC7, GC8, GC9, GC10, GC11, GC12 |
| LO4 | Synthesise complex information on learning and teaching strategies with computational models and Big Data in STEM to enhance and innovate professional practice (APST HA 1.2, 1.3, 1.5, 2.1, 2.2, 3.3, 5.1, 6.3) | GC1, GC2, GC3, GC4, GC7, GC8, GC9, GC10, GC11, GC12 |
AUSTRALIAN PROFESSIONAL STANDARDS FOR TEACHERS - HIGHLY ACCOMPLISHED
On successful completion of this unit, students should have gained evidence towards the following standards:
1.1 Physical, Social and intellectual development and characteristics of students Select from a flexible and effective repertoire of teaching strategies to suit the physical, social and intellectual development and characteristics of students. |
1.2 Understand how students learn Expand understanding of how students learn using research and workplace knowledge. |
1.5 Differentiate teaching to meet the specific learning needs of students across the full range of abilities Evaluate learning and teaching programs, using student assessment data, that are differentiated for the specific learning needs of students across the full range of abilities |
1.6 Strategies to support full participation of students with disability Work with colleagues to access specialist knowledge, and relevant policy and legislation, to develop teaching programs that support the participation and learning of students with disability. |
2.1 Content and teaching strategies of the teaching area Support colleagues using current and comprehensive knowledge of content and teaching strategies to develop and implement engaging learning and teaching programs. |
2.2 Content selection and organisation Exhibit innovative practice in the selection and organisation of content and delivery of learning and teaching programs. |
2.5 Literacy and numeracy strategies Support colleagues to implement effective teaching strategies to improve students’ literacy and numeracy achievement. |
2.6 Information and Communication Technology (ICT Model high-level teaching knowledge and skills and work with colleagues to use current ICT to improve their teaching practice and make content relevant and meaningful. |
3.3 Use teaching strategies Support colleagues to select and apply effective teaching strategies to develop knowledge, skills, problem solving and critical and creative thinking. |
3.4 Select and use resources Assist colleagues to create, select and use a wide range of resources, including ICT, to engage students in their learning. |
4.1 Support student participation Model effective practice and support colleagues to implement inclusive strategies that engage and support all students. |
4.5 Use ICT safely, responsibly and ethically Model, and support colleagues to develop, strategies to promote the safe, responsible and ethical use of ICT in learning and teaching. |
5.1 Assess student learning Develop and apply a comprehensive range of assessment strategies to diagnose learning needs, comply with curriculum requirements and support colleagues to evaluate the effectiveness of their approaches to assessment. |
6.2 Engage in professional learning and improve practice Plan for professional learning by accessing and critiquing relevant research, engage in high quality targeted opportunities to improve practice and offer quality placements for pre-service teachers where applicable |
6.3 Engage with colleagues and improve practice Initiate and engage in professional discussions with colleagues in a range of forums to evaluate practice directed at improving professional knowledge and practice, and the educational outcomes of students. |
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
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.
This unit is offered in multi-mode and will be supported by a Learning Management System (LMS) site. 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.
This is a 10 credit point unit and has been designed to ensure that the time needed to complete the required volume of learning to the requisite standard is approximately 150 hours in total across the semester.
Mode of delivery: This unit will be offered in one or more of modes of delivery described below, chosen with the aim of providing flexible delivery of academic content.
- On Campus: Most learning activities or classes are delivered at a scheduled time, on campus, to enable in-person interactions. Activities will appear in a student’s timetable.
- Intensive: In an intensive mode, students require face-to-face attendance on weekends or any block of time determined by the school. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you students to prepare and revise.
- Multi-mode: Learning activities are delivered through a planned mix of online and in-person classes, which may include full-day sessions and/or placements, to enable interaction. Activities that require attendance will appear in a student’s timetable.
- Online unscheduled: Learning activities are accessible anytime, anywhere. These units are normally delivered fully online and will not appear in a student’s timetable.
- Online scheduled: All learning activities are held online, at scheduled times, and will require some attendance to enable online interaction. Activities will appear in a student’s timetable.
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
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
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% | LO1, LO2, LO3 |
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% | LO1, LO2, LO3, LO4 |
Representative texts and references
Bielik, T., Fonio, E., Feinerman, O., Duncan, R. G., & Levy, S. T. (2021). Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena. Journal of Science Education and Technology, 30(1), 40–57. https://doi.org/10.1007/s10956-020-09869-x
Fuhrmann, T., Schneider, B., & Blikstein, P. (2018). Should students design or interact with models? Using the Bifocal Modelling Framework to investigate model construction in high school science. International Journal of Science Education, 40(8), 867–893. https://doi.org/10.1080/09500693.2018.1453175
Göhner, M. F., Bielik, T., & Krell, M. (2022). Investigating the dimensions of modeling competence among preservice science teachers: Meta‐modeling knowledge, modeling practice, and modelling product. Journal of Research in Science Teaching, 59(8), 1354–1387. https://doi.org/10.1002/tea.21759
Howley, P., Wang, K., & Bilgin, A. A. (2021). Big Data for Early Learners. In T. Prodromou (Ed.), Big Data in Education: Pedagogy and Research (pp. 41-64). Springer International Publishing. https://doi.org/10.1007/978-3-030-76841-6_2
MacDonald, A., Danaia, L., & Murphy, S. (2020). STEM Education Across the Learning Continuum Early Childhood to Senior Secondary . Springer Singapore. https://doi.org/10.1007/978-981-15-2821-7
Shin, N., Bowers, J., Roderick, S., McIntyre, C., Stephens, A. L., Eidin, E., Krajcik, J., & Damelin, D. (2022). A framework for supporting systems thinking and computational thinking through constructing models. Instructional Science, 50(6), 933–960. https://doi.org/10.1007/s11251-022-09590-9
Sengupta, P., Dickes, A., & Farris, A. (2018). Toward a Phenomenology of Computational Thinking in STEM Education. ArXiv:1801.09258 [Physics]. https://arxiv.org/abs/1801.09258
Upmeier zu Belzen, A., Krüger, D., & van Driel, J. (2019). Towards a competence-based view on models and modeling in science education. Springer International Publishing. https://doi.org/10.1007/978-3-030-30255-9
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. MIT Press.