Comprehensive competency framework for Learning Engineering professionals, defining professional competence to participate on LE teams and contribute to LE initiatives
Competencies (74)
Demonstrate Data Awareness and Identify Data Types and FormatsData Literacy
Organize and manage data effectively to support learning processes, ensuring data is structured appropriately and maintained accurately.Data Literacy
Collaborate effectively with data scientists by discussing mathematical and statistical approaches in learning analytics projects.Data Literacy
Interpret data visualizations in learning contextsData Literacy
Apply data-driven decision-making in learning contexts.Data Literacy
Identify the question/challenge for the data to informData Literacy
Identify the types of data to capture the learning challenge or questionData Literacy
Identify what data exists, what is built into systems and tools that can be leveraged, and what has to be uniquely developed and require instrumentationData Literacy
Identify and articulate the specific data needs to address learning challenges or questions, aligning data collection with learning objectives.Data Literacy
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.Data Literacy
Maintain data quality standards throughout the collection process, ensuring the reliability and validity of data for learning analytics.Data Literacy
Identify and interpret key insights from learning data, reports, and findings that are relevant to learning objectives and support learning engineering projects.Data Literacy
Transform data into actionable information (wisdom)Data Literacy
Use the data information and results to inform decision making process and apply the dataData Literacy
Construct outline and story that visually highlights key learnings, opportunities, and insights from data as it relates to the challenge or question investigatedData Literacy
Communicate the data results, information, decisions and application to various stakeholder groupsData Literacy
Evaluate the potential benefits and limitations of AI applications in various learning contexts.Data Literacy
Identify and mitigate ethical implications of using AI in educational settings, including issues of privacy, fairness, and transparency.Data Literacy
Use AI-powered tools to enhance learning design processes, content creation, and data analysis.Data Literacy
Clearly explain AI concepts, applications, and implications to various stakeholders in the educational ecosystem.Data Literacy
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. )Ethical Practice
Understand fundamental data practices for governance such as stewardship, storage, ownership, access, security, transpaentcy with the measurement, collection, analysis and use of learning dataEthical Practice
Foster an inclusive environment that builds a safe, positive learning climate of openness, mutual respect, support, and inquiry and facilitates diversityEthical Practice
Community building in iterative design – Collaboration and stakeholder involvementEthical Practice
Describe Learning EngineeringLearning Engineering Essentials
Identify tools and techniques for learning engineeringLearning Engineering Essentials
Apply learning engineering processLearning Engineering Essentials
Knows and can explain the core concepts and theories in concise, actionable and accurate ways.Learning Sciences
Can integrate insights across sub-disciplines. Can interpret findings through knowledge of various research traditions, debates, and limitations of various approaches.Learning Sciences
Consistently incorporates up-to-date findings in explanations.Learning Sciences
Apply theoretical and empirical findings in accurate and relevant waysLearning Sciences
Identifies and utilizes emperically-derived boundary conditions.Learning Sciences
Makes theoretically-informed adaptations to the context at hand that are likely to result in improved learning outcomes.Learning Sciences
Synthesizes complex learning science concepts and research findings, and communicates them appropriately.Learning Sciences
Synthesize and Communicate knowledge area for different audiences/purposesLearning Sciences
Clearly links theoretical to practical. Makes implications clear and compelling.Learning Sciences
Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instrumentsLearning Sciences
Can select approproate assessment instruments for a particular contextLearning Sciences
Can explain suitability and limitationsLearning Sciences
Developing constructive and cooperative working relationships with others, and maintain them over time.Professional Skills
Build and maintain positive interpersonal relationships within the teamProfessional Skills
Work effectively with diverse teams to achieve common goalsProfessional Skills
Address and resolve conflicts in a constructive mannerProfessional Skills
Engage with project stakeholders, manage their expectations, and address concerns.Professional Skills
Clear and effective communication to diverse audiences. Adapts communication to the goals, needs, urgency and sensitivity of the interaction. Conveys information purposefullyProfessional Skills
Effective communication technical skillsProfessional Skills
Manages information sharing and documentationProfessional Skills
Develop, contribute, manage a project plan (scope, objectives,activities, timeline, deliverables) to design and implement learning solutionProfessional Skills
Coordinate team efforts to ensure project timelines and deliverables are met.Professional Skills
Frame research questions; Formulate hypothesesResearch
Articulate various research frameworks (quantitative, qualitative, mixed, data-centric), their unique attributes, and the situations under which each is most appropriate.Research
IDs the appropriate methodology based on the research goals, context, and timescaleResearch
Can ID gaps in the literatureResearch
Can design informative studies using qualitative methodologies alone or as part of mixed methods researchResearch
Can design informative studies using quantitative methodologies alone or as part of mixed methods researchResearch
Can design reliable and valid assessment instruments that accurately capture the construct of interest (i.e. formal assessment)Research
Can design an appropriate and valid data collection strategy that accurately capture the construct of interest (i.e. Learning analytics)Research
Can successfully engage in all needed preparation and project management activities to ensure successful study executionResearch
Can successfully engage in all needed "day of" activities to conduct successful study executionResearch
Can complete all IRB requirements to allow for study execution as intended, and on time.Research
Conducts all necessary follow-up activities (e.g. participant payments, thank yous)Research
Accurately reporting the results of the data analysis (The mean and sd are X and Y)Research
Accurately interpreting the results of the data analysis, and contextualizing it within the study's research question. (And this supports our hypothesis)Research
Accurately drawing inferences from the study results and sythensziing within the wider literature/context (And this has implication for our understanding of SEL...."Research
Can make accurate claims based on study dataResearch
Evaluate strength of methodologyResearch
Evaluate Strength of ClaimsResearch
Can accurately interpret research studies / literatureResearch
Adhere to ethical standards and principles in conducting research.Research
Adhere to ethical standards and principles in reporting research.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 applyEngineering
Something in general about data governance policiesEngineering
Apply engineering processEngineering
Apply systems thinkingEngineering