ICICLE Learning Engineering Competency Framework
Comprehensive competency framework for Learning Engineering professionals, defining professional competence to participate on LE teams and contribute to LE initiatives
Something in general about data governance policies
Articulate what is a data standard, why they are needed and list data standards Toolkit: Data standards make it easier to create, share and integrate data Performance criteria Able to articulate difference between learning data standards such as SCORM, xAPI, cMI5 High level understanding and awareness of interoporability and the role standards, APIs have in transmitting data across systems GDPR, ISO standards awareness and how AI does or does not apply
Adhere to ethical standards and principles in reporting research.
Adhere to ethical standards and principles in conducting research.
Can accurately interpret research studies / literature
Can make accurate claims based on study data
Accurately drawing inferences from the study results and sythensziing within the wider literature/context (And this has implication for our understanding of SEL...."
Accurately interpreting the results of the data analysis, and contextualizing it within the study's research question. (And this supports our hypothesis)
Accurately reporting the results of the data analysis (The mean and sd are X and Y)
Conducts all necessary follow-up activities (e.g. participant payments, thank yous)
Can complete all IRB requirements to allow for study execution as intended, and on time.
Can successfully engage in all needed "day of" activities to conduct successful study execution
Can successfully engage in all needed preparation and project management activities to ensure successful study execution
Can design an appropriate and valid data collection strategy that accurately capture the construct of interest (i.e. Learning analytics)
Can design reliable and valid assessment instruments that accurately capture the construct of interest (i.e. formal assessment)
Can design informative studies using quantitative methodologies alone or as part of mixed methods research
Can design informative studies using qualitative methodologies alone or as part of mixed methods research
IDs the appropriate methodology based on the research goals, context, and timescale
Articulate various research frameworks (quantitative, qualitative, mixed, data-centric), their unique attributes, and the situations under which each is most appropriate.
Frame research questions; Formulate hypotheses
Coordinate team efforts to ensure project timelines and deliverables are met.
Develop, contribute, manage a project plan (scope, objectives,activities, timeline, deliverables) to design and implement learning solution
Manages information sharing and documentation
Effective communication technical skills
Clear and effective communication to diverse audiences. Adapts communication to the goals, needs, urgency and sensitivity of the interaction. Conveys information purposefully
Engage with project stakeholders, manage their expectations, and address concerns.
Address and resolve conflicts in a constructive manner
Work effectively with diverse teams to achieve common goals
Build and maintain positive interpersonal relationships within the team
Developing constructive and cooperative working relationships with others, and maintain them over time.
Can select approproate assessment instruments for a particular context
Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instruments
Clearly links theoretical to practical. Makes implications clear and compelling.
Synthesize and Communicate knowledge area for different audiences/purposes
Synthesizes complex learning science concepts and research findings, and communicates them appropriately.
Makes theoretically-informed adaptations to the context at hand that are likely to result in improved learning outcomes.
Identifies and utilizes emperically-derived boundary conditions.
Apply theoretical and empirical findings in accurate and relevant ways
Consistently incorporates up-to-date findings in explanations.
Can integrate insights across sub-disciplines. Can interpret findings through knowledge of various research traditions, debates, and limitations of various approaches.
Knows and can explain the core concepts and theories in concise, actionable and accurate ways.
Identify tools and techniques for learning engineering
Community building in iterative design – Collaboration and stakeholder involvement
Foster an inclusive environment that builds a safe, positive learning climate of openness, mutual respect, support, and inquiry and facilitates diversity
Understand fundamental data practices for governance such as stewardship, storage, ownership, access, security, transpaentcy with the measurement, collection, analysis and use of learning data
Understand ethic principles and standards such as privacy, confidentiality, intergity with the measurement, collection, analysis and use of learning data at all levels (classroom, research, etc. )
Clearly explain AI concepts, applications, and implications to various stakeholders in the educational ecosystem.
Use AI-powered tools to enhance learning design processes, content creation, and data analysis.
Identify and mitigate ethical implications of using AI in educational settings, including issues of privacy, fairness, and transparency.
Evaluate the potential benefits and limitations of AI applications in various learning contexts.
Communicate the data results, information, decisions and application to various stakeholder groups
Construct outline and story that visually highlights key learnings, opportunities, and insights from data as it relates to the challenge or question investigated
Use the data information and results to inform decision making process and apply the data
Transform data into actionable information (wisdom)
Identify and interpret key insights from learning data, reports, and findings that are relevant to learning objectives and support learning engineering projects.
Maintain data quality standards throughout the collection process, ensuring the reliability and validity of data for learning analytics.
Evaluate existing data sources, systems, and tools to determine what data can be leveraged for learning analytics and identify gaps requiring new data collection methods.
Identify and articulate the specific data needs to address learning challenges or questions, aligning data collection with learning objectives.
Identify what data exists, what is built into systems and tools that can be leveraged, and what has to be uniquely developed and require instrumentation
Identify the types of data to capture the learning challenge or question
Identify the question/challenge for the data to inform
Apply data-driven decision-making in learning contexts.
Interpret data visualizations in learning contexts
Collaborate effectively with data scientists by discussing mathematical and statistical approaches in learning analytics projects.
Organize and manage data effectively to support learning processes, ensuring data is structured appropriately and maintained accurately.
Demonstrate Data Awareness and Identify Data Types and Formats
ICICLE Learning Engineering Competency Framework Proficiency Rubric
Holistic proficiency rubric for evaluating competencies in ICICLE Learning Engineering Competency Framework
Proficiency evaluation criterion for ICICLE Learning Engineering Competency Framework
Competent application in varied contexts