Artifact: Problems in Instructional Design – Articulate Rise Module (EDF 6284)
Role: Instructional Designer and Content Author (Individual Contribution within Group Project)
Type of Project: Design-based inquiry through instructional module development

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates the application of research-informed instructional design and formal inquiry through the creation of a self-contained instructional module developed as part of a larger group project. The module represents the author’s individual contribution to a multi-section instructional unit and reflects deliberate design decisions grounded in instructional design theory, multimedia learning principles, and learner analysis. The artifact provides evidence of design-based inquiry by using instructional development as a method for examining how learners engage with and understand core concepts in instructional design.

AECT Standard 5.2 – Research Methods

Candidates demonstrate the ability to apply research methodologies to examine issues related to learning and instructional technology.

This artifact demonstrates AECT Standard 5.2 through the application of design-based research principles within an instructional development context. The module was designed using instructional design frameworks and multimedia learning theory to examine how structured sequencing, worked examples, and scenario-based explanations support learner comprehension of instructional design concepts. Design decisions related to content chunking, interaction pacing, and visual layout reflect methodological application of research-supported instructional strategies.

Although the artifact does not employ experimental or statistical research methods, it reflects an applied research methodology in which instructional design itself serves as the investigative tool. The module operationalizes theoretical constructs—such as alignment, instructional problem analysis, and design decision-making—into a concrete instructional intervention. This approach aligns with accepted forms of applied inquiry and design-based research within instructional technology.

AECT Standard 5.3 – Inquiry

Candidates demonstrate the ability to use formal inquiry to assess instructional processes and outcomes.

This artifact demonstrates AECT Standard 5.3 through systematic inquiry into instructional processes embedded within the module’s design. The instructional sequence was intentionally constructed to explore how learners interpret instructional design problems when presented with structured explanations, guided reflection prompts, and real-world examples. The module implicitly examines instructional effectiveness by testing assumptions about learner understanding, cognitive load, and instructional clarity.

Inquiry is further demonstrated through reflective consideration of instructional coherence across the broader unit. The module was designed to integrate with preceding and subsequent sections (Introduction, Sections 1.2 and 1.3, and Conclusion), requiring attention to continuity, conceptual scaffolding, and cumulative learning outcomes. This reflects formal inquiry into how instructional components function together within a larger learning experience.

Competencies Demonstrated

• Competency: Apply instructional design theory

This artifact demonstrates Apply instructional design theory through intentional use of established instructional design principles to structure content, sequence instruction, and support learner understanding. Design decisions reflect awareness of alignment among objectives, content, and instructional activities.

• Competency: Conduct design-based inquiry

This artifact demonstrates Conduct design-based inquiry by using instructional development as a method for examining learning processes. The module functions as a practical investigation into how learners engage with instructional design concepts when theory is translated into interactive instruction.

• Competency: Design and develop instructional materials

This artifact demonstrates Design and develop instructional materials through the creation of a polished Articulate Rise module that integrates text, visuals, interactions, and formative checks for understanding. The module represents a complete instructional product rather than a conceptual outline.

• Competency: Analyze instructional effectiveness (Applied)

This artifact demonstrates Analyze instructional effectiveness by embedding reflective prompts and instructional sequencing intended to reveal learner comprehension and misconceptions. While formal data collection was not conducted, the design reflects evaluative thinking about how instructional choices affect learning.

Artifact: Foundations of Educational Research – Final Assessment (EDF 6481)
Role: Sole Author
Type of Project: Research synthesis and methodological analysis of empirical education studies

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates foundational knowledge of educational research theory and the ability to critically analyze empirical studies. The final assessment required synthesizing peer-reviewed research, interpreting statistical findings, and evaluating methodological decisions related to sampling, variables, and analytical techniques. The artifact provides direct evidence of research literacy and inquiry skills essential to instructional design and educational technology practice.

AECT Standard 5.1 – Theoretical Foundations (Primary Alignment)

Candidates demonstrate foundational knowledge of research theories and principles relevant to instructional design and educational technology.

This artifact demonstrates AECT Standard 5.1 through detailed analysis of research questions, hypotheses, variables, sampling approaches, and analytical frameworks used in large-scale education finance studies. The assessment shows understanding of core research concepts such as null hypotheses, dependent variables, purposive sampling, correlation analysis, and interpretation of quantitative findings within broader policy contexts.

The artifact further demonstrates theoretical understanding by situating empirical findings within established literature on equity, resource allocation, and educational outcomes. By engaging with concepts such as inter-district equity, funding progressiveness, and structural disparities, the assessment reflects knowledge of how research theory informs interpretation of data and policy implications in education.

AECT Standard 5.2 – Research Methods (Secondary Alignment)

Candidates demonstrate the ability to apply research methodologies to examine issues related to learning and instructional technology.

This artifact provides secondary support for AECT Standard 5.2 through applied analysis of research methodologies used in the examined studies. The assessment evaluates methodological choices including sampling criteria, variable operationalization, and correlational analysis, explaining how these decisions affect validity and interpretation of findings. Rather than merely summarizing results, the artifact critiques how methodological constraints shape conclusions about equity and resource distribution.

The analysis demonstrates the ability to engage with research methods as tools for examining complex educational issues. By interpreting figures, identifying limitations, and connecting findings to policy implications, the artifact reflects methodological reasoning appropriate to applied research contexts in education.

Competencies Demonstrated

• Competency: Demonstrate research literacy

This artifact demonstrates Demonstrate research literacy through accurate interpretation of scholarly research, including hypotheses, variables, sampling strategies, and statistical relationships. The analysis shows the ability to read and interpret empirical studies critically rather than descriptively.

• Competency: Evaluate research quality and limitations

This artifact demonstrates Evaluate research quality and limitations by examining how methodological choices influence findings and by acknowledging constraints related to sampling, policy context, and data interpretation. This evaluative stance reflects professional judgment in assessing the strength of research evidence.

• Competency: Synthesize research findings

This artifact demonstrates Synthesize research findings through integration of multiple figures, results, and scholarly perspectives into coherent conclusions about educational equity and funding policy. The synthesis moves beyond isolated observations to identify broader patterns and implications.

• Competency: Apply research findings to educational contexts

This artifact demonstrates Apply research findings to educational contexts by connecting empirical results to real-world implications for state policy, district practice, and instructional conditions. The analysis illustrates how research informs decision-making in educational systems.

Artifact: AI-Enabled Early Warning System – Final Project (EME 6936, AI in Education)
Role: Sole Author and Research Designer
Type of Project: Research-informed system design proposal with methodological framework and ethical governance plan

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates advanced research synthesis, methodological reasoning, and ethical inquiry applied to a complex educational problem. The project required reviewing and integrating empirical literature on dropout prevention, learning analytics, and artificial intelligence, then applying those findings to design a theoretically grounded early warning architecture. The artifact provides strong evidence of the ability to move from theory and research to a structured, research-informed solution while explicitly addressing ethical considerations in educational data use.

AECT Standard 5.1 – Theoretical Foundations (Primary Alignment)

Candidates demonstrate foundational knowledge of research theories and principles relevant to instructional design and educational technology.

This artifact demonstrates AECT Standard 5.1 through extensive synthesis of theoretical and empirical research related to student attrition, early warning indicators, and predictive analytics. The literature review integrates foundational theories of academic disengagement, school climate, and socio-ecological risk factors with contemporary research on early warning systems and machine-learning approaches in education. These theoretical perspectives inform the proposed system architecture, ensuring that design decisions are grounded in established research rather than speculative technological capability.

The project also demonstrates theoretical understanding by connecting learning analytics and AI research to broader policy and equity frameworks, including ESSA mandates and graduation rate accountability. This reflects an understanding of how theoretical foundations guide both instructional design and systemic educational interventions.

AECT Standard 5.2 – Research Methods (Primary Alignment)

Candidates demonstrate the ability to apply research methodologies to examine issues related to learning and instructional technology.

This artifact demonstrates AECT Standard 5.2 through the explicit application of research methods to the design of an AI-enabled early warning system. The project outlines a clear methodological framework, including cohort selection, feature engineering, model training and validation, performance metrics, and simulation-based outcome projections. Quantitative research methods such as longitudinal data analysis, cross-validation, and predictive performance evaluation are applied to examine how instructional and behavioral indicators relate to dropout risk.

Importantly, the artifact does not merely reference these methods abstractly; it operationalizes them within a coherent analytic plan. This demonstrates the ability to apply research methodology as a problem-solving tool in educational technology contexts, rather than treating methods as purely academic exercises.

AECT Standard 5.4 – Ethics in Research Practice (Secondary Alignment)

Candidates demonstrate the ability to conduct research and inquiry in an ethical and responsible manner.

This artifact provides strong secondary support for ethical research practice through explicit consideration of data stewardship, algorithmic fairness, transparency, and human oversight. The proposed system includes FERPA-aligned data governance, bias auditing procedures, explainability mechanisms, and human-in-the-loop decision-making to mitigate risks associated with automated prediction.

Ethical inquiry is integrated into the research design rather than treated as an afterthought. By anticipating risks such as disparate impact, data misuse, and over-reliance on algorithmic outputs, the project demonstrates responsible research practice aligned with ethical standards in educational data science.

Competencies Demonstrated

• Competency: Synthesize research literature

This artifact demonstrates Synthesize research literature through integration of findings from learning analytics, dropout prevention, and artificial intelligence research into a coherent conceptual framework. The synthesis informs both system design and projected instructional impact.

• Competency: Apply research methods to educational problems

This artifact demonstrates Apply research methods to educational problems by using predictive modeling methodologies to address student attrition. Research techniques are used to analyze risk factors, validate models, and inform intervention strategies.

• Competency: Design research-informed instructional systems

This artifact demonstrates Design research-informed instructional systems through the creation of a two-tier early warning architecture that links analytic outputs to evidence-based interventions within an MTSS framework.

• Competency: Demonstrate ethical research judgment

This artifact demonstrates Demonstrate ethical research judgment by incorporating safeguards related to privacy, fairness, transparency, and human oversight into the research design and proposed implementation.