# 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 ## Framework Details | Property | Value | | --- | --- | | **Status** | draft | | **Version** | 1.0 | | **Competencies** | 74 | ## Competencies | Statement | Domain | Bloom Level | Context | | --- | --- | --- |--- | | Demonstrate Data Awareness and Identify Data Types and Formats | Data Literacy | - | all contexts | | Organize and manage data effectively to support learning processes, ensuring data is structured appropriately and maintained accurately. | Data Literacy | - | all contexts | | Collaborate effectively with data scientists by discussing mathematical and statistical approaches in learning analytics projects. | Data Literacy | - | all contexts | | Interpret data visualizations in learning contexts | Data Literacy | - | all contexts | | Apply data-driven decision-making in learning contexts. | Data Literacy | - | all contexts | | Identify the question/challenge for the data to inform | Data Literacy | - | all contexts | | Identify the types of data to capture the learning challenge or question | Data Literacy | - | all contexts | | 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 | Data Literacy | - | all contexts | | Identify and articulate the specific data needs to address learning challenges or questions, aligning data collection with learning objectives. | Data Literacy | - | all contexts | | 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 | - | all contexts | | Maintain data quality standards throughout the collection process, ensuring the reliability and validity of data for learning analytics. | Data Literacy | - | all contexts | | Identify and interpret key insights from learning data, reports, and findings that are relevant to learning objectives and support learning engineering projects. | Data Literacy | - | all contexts | | Transform data into actionable information (wisdom) | Data Literacy | - | all contexts | | Use the data information and results to inform decision making process and apply the data | Data Literacy | - | all contexts | | Construct outline and story that visually highlights key learnings, opportunities, and insights from data as it relates to the challenge or question investigated | Data Literacy | - | all contexts | | Communicate the data results, information, decisions and application to various stakeholder groups | Data Literacy | - | all contexts | | Evaluate the potential benefits and limitations of AI applications in various learning contexts. | Data Literacy | - | all contexts | | Identify and mitigate ethical implications of using AI in educational settings, including issues of privacy, fairness, and transparency. | Data Literacy | - | all contexts | | Use AI-powered tools to enhance learning design processes, content creation, and data analysis. | Data Literacy | - | all contexts | | Clearly explain AI concepts, applications, and implications to various stakeholders in the educational ecosystem. | Data Literacy | - | all contexts | | 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 | - | all contexts | | Understand fundamental data practices for governance such as stewardship, storage, ownership, access, security, transpaentcy with the measurement, collection, analysis and use of learning data | Ethical Practice | - | all contexts | | Foster an inclusive environment that builds a safe, positive learning climate of openness, mutual respect, support, and inquiry and facilitates diversity | Ethical Practice | - | all contexts | | Community building in iterative design – Collaboration and stakeholder involvement | Ethical Practice | - | all contexts | | Describe Learning Engineering | Learning Engineering Essentials | - | all contexts | | Identify tools and techniques for learning engineering | Learning Engineering Essentials | - | all contexts | | Apply learning engineering process | Learning Engineering Essentials | - | all contexts | | Knows and can explain the core concepts and theories in concise, actionable and accurate ways. | Learning Sciences | - | all contexts | | Can integrate insights across sub-disciplines. Can interpret findings through knowledge of various research traditions, debates, and limitations of various approaches. | Learning Sciences | - | all contexts | | Consistently incorporates up-to-date findings in explanations. | Learning Sciences | - | all contexts | | Apply theoretical and empirical findings in accurate and relevant ways | Learning Sciences | - | all contexts | | Identifies and utilizes emperically-derived boundary conditions. | Learning Sciences | - | all contexts | | Makes theoretically-informed adaptations to the context at hand that are likely to result in improved learning outcomes. | Learning Sciences | - | all contexts | | Synthesizes complex learning science concepts and research findings, and communicates them appropriately. | Learning Sciences | - | all contexts | | Synthesize and Communicate knowledge area for different audiences/purposes | Learning Sciences | - | all contexts | | Clearly links theoretical to practical. Makes implications clear and compelling. | Learning Sciences | - | all contexts | | Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instruments | Learning Sciences | - | all contexts | | Can select approproate assessment instruments for a particular context | Learning Sciences | - | all contexts | | Can explain suitability and limitations | Learning Sciences | - | all contexts | | Developing constructive and cooperative working relationships with others, and maintain them over time. | Professional Skills | - | all contexts | | Build and maintain positive interpersonal relationships within the team | Professional Skills | - | all contexts | | Work effectively with diverse teams to achieve common goals | Professional Skills | - | all contexts | | Address and resolve conflicts in a constructive manner | Professional Skills | - | all contexts | | Engage with project stakeholders, manage their expectations, and address concerns. | Professional Skills | - | all contexts | | Clear and effective communication to diverse audiences. Adapts communication to the goals, needs, urgency and sensitivity of the interaction. Conveys information purposefully | Professional Skills | - | all contexts | | Effective communication technical skills | Professional Skills | - | all contexts | | Manages information sharing and documentation | Professional Skills | - | all contexts | | Develop, contribute, manage a project plan (scope, objectives,activities, timeline, deliverables) to design and implement learning solution | Professional Skills | - | all contexts | | Coordinate team efforts to ensure project timelines and deliverables are met. | Professional Skills | - | all contexts | | Frame research questions; Formulate hypotheses | Research | - | all contexts | | Articulate various research frameworks (quantitative, qualitative, mixed, data-centric), their unique attributes, and the situations under which each is most appropriate. | Research | - | all contexts | | IDs the appropriate methodology based on the research goals, context, and timescale | Research | - | all contexts | | Can ID gaps in the literature | Research | - | all contexts | | Can design informative studies using qualitative methodologies alone or as part of mixed methods research | Research | - | all contexts | | Can design informative studies using quantitative methodologies alone or as part of mixed methods research | Research | - | all contexts | | Can design reliable and valid assessment instruments that accurately capture the construct of interest (i.e. formal assessment) | Research | - | all contexts | | Can design an appropriate and valid data collection strategy that accurately capture the construct of interest (i.e. Learning analytics) | Research | - | all contexts | | Can successfully engage in all needed preparation and project management activities to ensure successful study execution | Research | - | all contexts | | Can successfully engage in all needed "day of" activities to conduct successful study execution | Research | - | all contexts | | Can complete all IRB requirements to allow for study execution as intended, and on time. | Research | - | all contexts | | Conducts all necessary follow-up activities (e.g. participant payments, thank yous) | Research | - | all contexts | | Accurately reporting the results of the data analysis (The mean and sd are X and Y) | Research | - | all contexts | | Accurately interpreting the results of the data analysis, and contextualizing it within the study's research question. (And this supports our hypothesis) | Research | - | all contexts | | Accurately drawing inferences from the study results and sythensziing within the wider literature/context (And this has implication for our understanding of SEL...." | Research | - | all contexts | | Can make accurate claims based on study data | Research | - | all contexts | | Evaluate strength of methodology | Research | - | all contexts | | Evaluate Strength of Claims | Research | - | all contexts | | Can accurately interpret research studies / literature | Research | - | all contexts | | Adhere to ethical standards and principles in conducting research. | Research | - | all contexts | | Adhere to ethical standards and principles in reporting research. | Research | - | all contexts | | 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 | Engineering | - | all contexts | | Something in general about data governance policies | Engineering | - | all contexts | | Apply engineering process | Engineering | - | all contexts | | Apply systems thinking | Engineering | - | all contexts | ## Competency Details ### Demonstrate Data Awareness and Identify Data Types and Formats Demonstrate Data Awareness and Identify Data Types and Formats ### Organize and manage data effectively to support learning processes, ensuring data is structured appropriately and maintained accurately. 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. Collaborate effectively with data scientists by discussing mathematical and statistical approaches in learning analytics projects. ### Interpret data visualizations in learning contexts Interpret data visualizations in learning contexts ### Apply data-driven decision-making in learning contexts. Apply data-driven decision-making in learning contexts. ### Identify the question/challenge for the data to inform Identify the question/challenge for the data to inform ### Identify the types of data to capture the learning challenge or question 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 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. 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. 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. 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. 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) Transform data into actionable information (wisdom) ### Use the data information and results to inform decision making process and apply the data 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 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 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. 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. 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. 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. 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 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 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 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 Community building in iterative design – Collaboration and stakeholder involvement ### Describe Learning Engineering Describe Learning Engineering ### Identify tools and techniques for learning engineering Identify tools and techniques for learning engineering ### Apply learning engineering process Apply learning engineering process ### Knows and can explain the core concepts and theories in concise, actionable and accurate ways. 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. 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. Consistently incorporates up-to-date findings in explanations. ### Apply theoretical and empirical findings in accurate and relevant ways Apply theoretical and empirical findings in accurate and relevant ways ### Identifies and utilizes emperically-derived boundary conditions. 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. 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. Synthesizes complex learning science concepts and research findings, and communicates them appropriately. ### Synthesize and Communicate knowledge area for different audiences/purposes Synthesize and Communicate knowledge area for different audiences/purposes ### Clearly links theoretical to practical. Makes implications clear and compelling. Clearly links theoretical to practical. Makes implications clear and compelling. ### Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instruments Is aware of the breadth of relevant constructs, measures, frameworks, and assessment instruments ### Can select approproate assessment instruments for a particular context Can select approproate assessment instruments for a particular context ### Can explain suitability and limitations Can explain suitability and limitations ### Developing constructive and cooperative working relationships with others, and maintain them over time. Developing constructive and cooperative working relationships with others, and maintain them over time. ### Build and maintain positive interpersonal relationships within the team Build and maintain positive interpersonal relationships within the team ### Work effectively with diverse teams to achieve common goals Work effectively with diverse teams to achieve common goals ### Address and resolve conflicts in a constructive manner Address and resolve conflicts in a constructive manner ### Engage with project stakeholders, manage their expectations, and address concerns. 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 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 Effective communication technical skills ### Manages information sharing and documentation Manages information sharing and documentation ### Develop, contribute, manage a project plan (scope, objectives,activities, timeline, deliverables) to design and implement learning solution 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. Coordinate team efforts to ensure project timelines and deliverables are met. ### Frame research questions; Formulate hypotheses 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. 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 IDs the appropriate methodology based on the research goals, context, and timescale ### Can ID gaps in the literature Can ID gaps in the literature ### Can design informative studies using qualitative methodologies alone or as part of mixed methods research 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 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 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 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 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 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. 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) 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 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 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...." 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 make accurate claims based on study data ### Evaluate strength of methodology Evaluate strength of methodology ### Evaluate Strength of Claims Evaluate Strength of Claims ### Can accurately interpret research studies / literature Can accurately interpret research studies / literature ### Adhere to ethical standards and principles in conducting research. Adhere to ethical standards and principles in conducting research. ### Adhere to ethical standards and principles in reporting 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 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 Something in general about data governance policies ### Apply engineering process Apply engineering process ### Apply systems thinking Apply systems thinking --- *Generated from TLA Toolbox on 4/5/2026*