Artifact: Writing Behavioral Performance Objectives (EDF 6284, Problems in Instructional Design for Computers)
Role: Sole Author
Type of Project: Instructional objectives design document

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates applied pedagogical knowledge through the systematic construction of clear, measurable behavioral performance objectives. The assignment required translating instructional intent into observable learner outcomes that align with instructional activities and assessment criteria. As a foundational instructional design task, the artifact provides direct evidence of content pedagogy by showing how learning theory, task analysis, and assessment considerations inform the design of instructional outcomes.

AECT Standard 2.1 – Content Pedagogy

Candidates demonstrate the ability to apply content pedagogy to design appropriate learning experiences and outcomes.

This artifact demonstrates AECT Standard 2.1 through the deliberate formulation of behavioral performance objectives that reflect sound pedagogical alignment among content, learner actions, and evaluation criteria. Objectives were written to include clearly defined conditions, observable behaviors, and performance standards, ensuring that learning outcomes are measurable and instructionally actionable. This structure reflects an understanding of how pedagogical theory supports the design of learning outcomes that can be reliably assessed.

The objectives were developed across varying levels of cognitive demand, demonstrating awareness of progression in learning complexity and instructional scaffolding. By aligning objectives with learner capabilities and instructional context, the artifact illustrates the application of content pedagogy to ensure that instructional outcomes are appropriate, coherent, and instructionally meaningful rather than vague or activity-based.

Competencies Demonstrated

• Competency: Write performance-based objectives

This artifact directly demonstrates Write performance-based objectives through the creation of objectives that specify observable learner behaviors and measurable criteria for success. Each objective clearly articulates what learners are expected to do, under what conditions, and to what level of performance, reflecting professional standards for instructional clarity and assessment validity.

• Competency: Perform job, task, and/or content analysis

This artifact demonstrates Perform job, task, and/or content analysis by breaking down instructional goals into discrete knowledge and skill components prior to objective formulation. Task analysis informed the identification of prerequisite knowledge, learner actions, and performance expectations, ensuring that objectives were grounded in an accurate understanding of what learners must know and be able to do to demonstrate mastery.

• Competency: Develop training program materials

This artifact demonstrates Develop training program materials by producing a set of instructional objectives that serve as foundational design components for lesson planning, instructional activities, and assessment development. The objectives function as reusable instructional materials that guide instructional decision-making and support consistent implementation across instructional contexts.

Artifact: Final Project – Digital Media & Learning (EME 6347, Digital Media & Learning)
Role: Sole Author
Type of Project: Research-based analytical paper examining digital media integration and learning theory

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates informed pedagogical decision-making regarding the implementation of digital media in instructional contexts. The project required synthesizing learning theory, cognitive development research, and Digital Media and Learning (DML) scholarship to examine how instructional environments and media choices shape learner reasoning and outcomes. Rather than focusing on a single tool, the analysis emphasizes theory-driven decision-making about when, why, and how digital media should be integrated to support learning, providing evidence of pedagogical judgment grounded in research.

AECT Standard 2.2 – Implementing

Candidates demonstrate the ability to implement educational technologies and processes based on appropriate pedagogical theory.

This artifact demonstrates AECT Standard 2.2 through a theory-driven examination of how digital media can be implemented to support learning across instructional contexts. The paper analyzes multiple educational environments—including traditional classroom instruction, inquiry-based learning, constructionist approaches, and participatory digital cultures—using established pedagogical frameworks such as constructivism, constructionism, situated cognition, and Digital Media and Learning theory.

Implementation decisions are evaluated in terms of alignment between media affordances, learner experience, and instructional goals. The analysis illustrates how digital media environments can be intentionally designed to support learner agency, scaffold inquiry, and promote movement between concrete and abstract reasoning. By grounding media integration decisions in pedagogical theory rather than technological novelty, the artifact demonstrates applied content pedagogy related to instructional implementation.

AECT Standard 2.4 – Ethics (Secondary Support)

Candidates demonstrate the ability to apply ethical principles to instructional design and pedagogical practice.

This artifact provides secondary support for AECT Standard 2.4 through its critical examination of instructional responsibility in digital media integration. The analysis emphasizes restraint and intentionality in media use, questioning assumptions that digital technologies are inherently beneficial. By foregrounding learner cognition, developmental appropriateness, and instructional consequences, the paper reflects ethical pedagogical judgment concerned with learner impact rather than technology adoption for its own sake.

The artifact also addresses ethical considerations implicitly by evaluating how different instructional environments shape reasoning, agency, and access to learning opportunities. These considerations reflect responsible instructional decision-making aligned with ethical practice, even though ethics is not the primary analytical focus of the project.

Competencies Demonstrated

• Competency: Select instructional media

This artifact demonstrates Select instructional media through critical evaluation of digital media affordances within different instructional contexts. Media selection is framed as a pedagogical decision informed by learning theory, learner needs, and instructional intent rather than availability or novelty. The analysis evaluates how specific media environments support or constrain learning outcomes.

• Competency: Recommend instructional strategies

This artifact demonstrates Recommend instructional strategies by synthesizing research findings into theory-grounded instructional implications. The paper identifies strategies such as scaffolded inquiry, participatory learning, reflective practice, and learner-centered design as conditions under which digital media can support meaningful learning.

• Competency: Evaluate instruction

This artifact demonstrates Evaluate instruction through analysis of how media-rich instructional environments influence learner reasoning and cognitive engagement. Instructional effectiveness is evaluated using pedagogical and developmental criteria rather than surface-level engagement measures, demonstrating systematic evaluative reasoning.

Artifact: Evaluating Online Course Design (M4 – EME 6457, Distance Education)
Role: Sole Analyst
Type of Project: Formal course-design evaluation using established quality standards

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates the ability to systematically evaluate an existing online course using established instructional design criteria rather than personal preference or anecdotal judgment. The assignment required analyzing course structure, assessment transparency, interaction design, instructional materials, technology integration, and accessibility through the lens of recognized quality frameworks. As such, it provides direct evidence of pedagogical judgment and evaluative reasoning applied to real instructional environments, which is central to effective distance education design and review.

AECT Standard 2.3 – Evaluating

Candidates demonstrate the ability to evaluate the impact of educational technologies and instructional processes on learning and performance.

This artifact demonstrates AECT Standard 2.3 through a structured evaluation of an existing online course using Quality Matters–aligned criteria. The analysis examines how instructional design choices—such as course navigation, assessment transparency, learner interaction, and technology use—support or hinder learning. Strengths of the course design were identified, including clear entry points for learners, transparent grading structures, and opportunities for learner-learner interaction, demonstrating the ability to recognize effective instructional practices.

Equally important, the artifact demonstrates evaluative rigor by identifying gaps in the course design that could negatively impact learning and performance. These include the absence of measurable learning objectives, incomplete documentation of instructional materials, lack of stated accessibility considerations, and insufficient communication expectations. The evaluation moves beyond surface-level critique by explaining why these omissions matter pedagogically and how they affect alignment, learner support, and instructional clarity. This reflects an understanding of evaluation as a process of evidence-based judgment rather than checklist completion.

Competencies Demonstrated

• Competency: Evaluate instruction

This artifact demonstrates Evaluate instruction through systematic analysis of instructional alignment, assessment design, interaction structures, and learner support mechanisms within an online course. The evaluation applies explicit criteria to determine how well instructional components support learning outcomes, demonstrating the ability to assess instructional effectiveness using established standards rather than subjective impressions.

• Competency: Evaluate instructional strategies and technologies

This artifact demonstrates Evaluate instructional strategies and technologies by examining how tools such as Canvas, Flipgrid, and Lucid are integrated into the course and whether their use is pedagogically justified. The analysis considers not only the presence of technology, but also its instructional purpose, accessibility implications, and alignment with learning activities, reflecting informed evaluative judgment.

• Competency: Recommend instructional strategies

This artifact demonstrates Recommend instructional strategies through evidence-based improvement suggestions grounded in instructional design theory and quality standards. Recommendations include adding measurable objectives, improving alignment documentation, clarifying communication and technology expectations, and addressing accessibility requirements. These recommendations demonstrate the ability to translate evaluative findings into actionable design improvements.

Artifact: Comparing Large Language Models for Classroom Use (M10 – EME 6936)
Role: Sole Author
Type of Project: Comparative analysis of artificial intelligence models for instructional use

Rationale for Selecting This Artifact

This artifact was selected because it demonstrates pedagogical decision-making in the selection, implementation, and evaluation of emerging educational technologies. The assignment required a structured comparison of GPT-style and BERT-style large language models, focusing on how architectural differences influence instructional affordances, limitations, and classroom applicability. The analysis emphasizes instructional purpose, learner needs, and assessment validity, providing evidence of content pedagogy grounded in learning theory rather than technology-driven adoption.

AECT Standard 2.2 – Implementing

Candidates demonstrate the ability to implement educational technologies and processes based on appropriate content pedagogy.

This artifact demonstrates AECT Standard 2.2 through theory-informed analysis of how different large language model architectures can be implemented to support specific instructional goals. The paper distinguishes between generative instructional tasks (e.g., brainstorming, drafting, exploratory learning) and comprehension-oriented tasks (e.g., reading support, question answering, text analysis), aligning each task type with the affordances of GPT-style and BERT-style models respectively.

Implementation decisions are framed through alignment between instructional objectives, learner cognitive demands, and technological capabilities. The analysis demonstrates that effective implementation requires matching model behavior to pedagogical intent, rather than applying artificial intelligence tools uniformly across learning contexts. This reflects applied content pedagogy by situating AI-supported instruction within instructional design principles and classroom realities.

AECT Standard 2.3 – Assessing/Evaluating (Secondary Support)

Candidates demonstrate an inquiry process that assesses the adequacy of learning and evaluates the instruction and implementation of educational technologies and processes grounded in reflective practice.

This artifact provides secondary support for AECT Standard 2.3 through evaluative analysis of the instructional suitability and limitations of different language models. The paper evaluates model reliability, contextual accuracy, transparency, and instructional risk, particularly in relation to assessment, feedback, and student learning outcomes. These evaluative considerations demonstrate reflective practice by weighing instructional benefits against potential drawbacks such as hallucination, misalignment with learning goals, and misuse in assessment contexts.

Evaluation is conducted using pedagogical criteria rather than technical benchmarks alone, reflecting an understanding of how educational technologies should be assessed in relation to learning quality and instructional validity.

Competencies Demonstrated

• Competency: Select instructional media

This artifact demonstrates Select instructional media by comparing AI models as instructional resources based on their pedagogical affordances and constraints. Model selection is justified through analysis of instructional purpose, learner task demands, and cognitive outcomes, illustrating disciplined and theory-driven media selection.

• Competency: Assess the relevant characteristics of the target audience

This artifact demonstrates Assess the relevant characteristics of the target audience by considering how different learners interact with generative versus comprehension-oriented AI tools. The analysis accounts for learner developmental level, metacognitive skill, and instructional context when evaluating model suitability, reflecting awareness that learner characteristics influence effective technology integration.

• Competency: Assess the relevant characteristics of the setting

This artifact demonstrates Assess the relevant characteristics of the setting through consideration of classroom constraints, assessment environments, and instructional use cases. The analysis evaluates how instructional context—such as formative versus summative assessment, guided versus independent work, and classroom norms—affects the appropriateness of AI tool implementation.

• Competency: Evaluate instructional strategies and technologies

This artifact demonstrates Evaluate instructional strategies and technologies through systematic comparison of how GPT-style and BERT-style models support or hinder instructional goals. The analysis evaluates instructional effectiveness, reliability, and alignment with learning outcomes, demonstrating evidence-based evaluative judgment.

• Competency: Recommend instructional strategies

This artifact demonstrates Recommend instructional strategies by translating comparative findings into actionable guidance for instructional practice. Recommendations include aligning generative models with exploratory learning tasks and using comprehension-focused models for text analysis and support, reflecting the ability to move from analysis to pedagogically grounded instructional guidance.