EDITABLE - 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
Demonstrate Data Awareness and Identify Data Types and Formats
IEEE ICICLE Learning Engineering Framework v1.0
Organize and manage data effectively to support learning processes, ensuring data is structured appropriately and maintained accurately.
Collaborate effectively with data scientists by discussing mathematical and statistical approaches in learning analytics projects.
Interpret data visualizations in learning contexts
Apply data-driven decision-making in learning contexts.
Identify the question/challenge for the data to inform
Identify the types of data to capture the learning challenge or question
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 and articulate the specific data needs to address learning challenges or questions, aligning data collection with learning objectives.
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.
Maintain data quality standards throughout the collection process, ensuring the reliability and validity of data for learning analytics.
Identify and interpret key insights from learning data, reports, and findings that are relevant to learning objectives and support learning engineering projects.
Transform data into actionable information (wisdom)
Use the data information and results to inform decision making process and apply the data
Construct outline and story that visually highlights key learnings, opportunities, and insights from data as it relates to the challenge or question investigated
Communicate the data results, information, decisions and application to various stakeholder groups
Evaluate the potential benefits and limitations of AI applications in various learning contexts.
Identify and mitigate ethical implications of using AI in educational settings, including issues of privacy, fairness, and transparency.
Use AI-powered tools to enhance learning design processes, content creation, and data analysis.
Clearly explain AI concepts, applications, and implications to various stakeholders in the educational ecosystem.
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. )
Understand fundamental data practices for governance such as stewardship, storage, ownership, access, security, transpaentcy with the measurement, collection, analysis and use of learning data
Foster an inclusive environment that builds a safe, positive learning climate of openness, mutual respect, support, and inquiry and facilitates diversity
Community building in iterative design – Collaboration and stakeholder involvement
Identify tools and techniques for learning engineering
Knows and can explain the core concepts and theories in concise, actionable and accurate ways.
Can integrate insights across sub-disciplines. Can interpret findings through knowledge of various research traditions, debates, and limitations of various approaches.
Consistently incorporates up-to-date findings in explanations.
Apply theoretical and empirical findings in accurate and relevant ways
Identifies and utilizes emperically-derived boundary conditions.
Makes theoretically-informed adaptations to the context at hand that are likely to result in improved learning outcomes.
Synthesizes complex learning science concepts and research findings, and communicates them appropriately.
Synthesize and Communicate knowledge area for different audiences/purposes
Clearly links theoretical to practical. Makes implications clear and compelling.
Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instruments
Can select approproate assessment instruments for a particular context
Developing constructive and cooperative working relationships with others, and maintain them over time.
Build and maintain positive interpersonal relationships within the team
Work effectively with diverse teams to achieve common goals
Address and resolve conflicts in a constructive manner
Engage with project stakeholders, manage their expectations, and address concerns.
Clear and effective communication to diverse audiences. Adapts communication to the goals, needs, urgency and sensitivity of the interaction. Conveys information purposefully
Effective communication technical skills
Manages information sharing and documentation
Develop, contribute, manage a project plan (scope, objectives,activities, timeline, deliverables) to design and implement learning solution
Coordinate team efforts to ensure project timelines and deliverables are met.
Frame research questions; Formulate hypotheses
Articulate various research frameworks (quantitative, qualitative, mixed, data-centric), their unique attributes, and the situations under which each is most appropriate.
IDs the appropriate methodology based on the research goals, context, and timescale
Can design informative studies using qualitative methodologies alone or as part of mixed methods research
Can design informative studies using quantitative methodologies alone or as part of mixed methods research
Can design reliable and valid assessment instruments that accurately capture the construct of interest (i.e. formal assessment)
Can design an appropriate and valid data collection strategy that accurately capture the construct of interest (i.e. Learning analytics)
Can successfully engage in all needed preparation and project management activities to ensure successful study execution
Can successfully engage in all needed "day of" activities to conduct successful study execution
Can complete all IRB requirements to allow for study execution as intended, and on time.
Conducts all necessary follow-up activities (e.g. participant payments, thank yous)
Accurately reporting the results of the data analysis (The mean and sd are X and Y)
Accurately interpreting the results of the data analysis, and contextualizing it within the study's research question. (And this supports our hypothesis)
Accurately drawing inferences from the study results and sythensziing within the wider literature/context (And this has implication for our understanding of SEL...."
Can make accurate claims based on study data
Can accurately interpret research studies / literature
Adhere to ethical standards and principles in conducting research.
Adhere to ethical standards and principles in reporting research.
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
Something in general about data governance policies
EDITABLE - ICICLE Learning Engineering Competency Framework Proficiency Rubric
Holistic proficiency rubric for evaluating competencies in EDITABLE - ICICLE Learning Engineering Competency Framework
Proficiency evaluation criterion for EDITABLE - ICICLE Learning Engineering Competency Framework
Competent application in varied contexts