Technology Integration in STEM Education Training Course

Participants can design aligned STEM learning sequences with purposeful digital activities and evidence.

• Backward design using desired results, evidence and learning experiences
• NGSS-style science and engineering practices translated into lesson objectives
• Mathematical modelling tasks aligned with digital representations and evidence
• Learning progression maps linking prerequisite knowledge to technology tasks
• Success criteria and worked examples for complex STEM performance
• Alignment checks between objectives, digital activities and assessment evidence

Participants can create accessible, cognitively manageable digital resources for STEM instruction.

• SAMR used to distinguish substitution from task redesign
• Multimedia learning principles: signalling, segmenting, coherence and modality
• Cognitive load management through chunking, worked examples and faded guidance
• Universal Design for Learning checkpoints applied to STEM resources
• Accessible documents, captions, colour contrast and keyboard-operable materials
• Lesson flow combining direct instruction, inquiry and digital production

Participants can structure technology-supported investigations that develop scientific and computational reasoning.

• 5E instructional model structured around digital investigations
• Phenomenon-based prompts generating testable questions and disciplinary discussion
• Virtual laboratories selected for fidelity, variables and conceptual purpose
• Sensor-based data collection using probes, spreadsheets and visualisations
• Computational thinking routines: decomposition, patterns, abstraction and algorithms
• Claim-Evidence-Reasoning protocols supported by shared digital workspaces

Participants can match common STEM technologies to specific conceptual and procedural learning purposes.

• Spreadsheets used for data cleaning, formulas, charts and interpretation
• Dynamic geometry software for conjecture, construction and proof
• Block-based and text-based coding matched to learning progression stages
• Simulations compared with physical apparatus for learning purpose and constraints
• Data visualisation choices matched to variable types and audience
• AI-assisted feedback evaluated for accuracy, bias, privacy and learner agency

Participants can build assessment systems that capture learning from digital STEM tasks.

• Formative assessment cycles using retrieval, hinge questions and feedback
• Digital exit tickets designed around misconceptions and next-step instruction
• Analytic rubrics for models, code, investigations and engineering designs
• Student portfolios curated with annotations, versions and evidence tags
• Learning analytics interpreted cautiously alongside teacher observation and student voice
• Academic integrity expectations for collaboration, coding and generative AI use

Participants can manage technology-supported STEM classrooms with productive routines and collaboration structures.

• ISTE Standards translated into observable student learning behaviours
• Station rotation models planned around devices, staffing and lesson purpose
• Project-based learning milestones managed through digital planning boards
• Collaborative protocols assigning roles, dependencies and shared artefacts
• Teacher facilitation moves for productive struggle and technical interruptions
• Classroom routines for logins, file naming, transitions and device care

Participants can assess technology use for student safety, data protection and responsible participation.

• Student data inventory covering accounts, submissions, recordings and analytics
• Privacy impact questions applied before adopting educational technology platforms
• Age-appropriate consent, safeguarding and acceptable-use procedures for student accounts
• Copyright, Creative Commons licences and attribution in student media
• Cybersecurity routines covering passwords, phishing, access control and updates
• Vendor evaluation against accessibility, data retention and procurement requirements

Participants can organise a feasible technology integration rollout with clear ownership and support.

• Readiness assessment covering infrastructure, skills, leadership and instructional priorities
• RACI matrix assigns ownership for rollout, support and escalation
• Professional learning cycles combining modelling, coaching, observation and reflection
• Technology integration budget separating acquisition, renewal, training and maintenance
• Pilot success measures defined through adoption, learning and equity indicators
• Communication plan addresses staff concerns, family expectations and governance

Participants can evaluate technology integration results and prepare an evidence-based improvement plan.

• Logic model linking technology inputs, activities, outputs and outcomes
• Evaluation questions distinguish implementation fidelity from learning impact
• Mixed-method evidence plan combining attainment, observation, survey and usage data
• Disaggregated results reviewed for access gaps and unintended consequences
• Improvement cycles use Plan-Do-Study-Act findings to refine implementation
• 90-day action plan specifies priorities, owners, milestones and evidence