Learning Theory Alignment
Learning Theory Selection and Design Rationale for the Minicourse
The minicourse is driven by a strategic combination of Constructivist Learning Theory, Cognitive Load Theory, Andragogy, and operationalized through SAM 2 as the procedural framework. Constructivism supports authentic artifact creation, peer review, and iterative revision. Learners construct knowledge through workflow mapping and artifact development. Cognitive Load Theory informs scaffolding decisions. Structured templates, comparison grids, and sequenced objectives reduce extraneous load while increasing germane processing. Andragogy ensures relevance to professional contexts, autonomy in tool selection, and immediate application.SAM 2 does not function as a learning theory, but operationalizes the learning theories through iterative design cycles. SAM 2 functions as the iterative engine that operates these theories. During the preparation phase, learning objectives are defined and aligned. During iterative design and development phases, artifacts are prototyped, piloted, revised, and refined based on feedback. Thus, SAM 2 connects theoretical principles to observable performance improvement.Theory 1: Cognitive Load Theory
Cognitive Load Theory explains that working memory capacity is limited, and that instruction should reduce unnecessary processing while supporting the mental work required to learn complex skills (Sweller, 1988). In an online learning environment, this is especially important because learners can experience overload when content is dense, unstructured, or filled with distracting information.
How CLT drives the design of learning activities In this minicourse, learners are asked to evaluate A.I. generated instructional content for credibility, bias, hallucinations, and ethical risk. This task involves multiple interacting elements, including content accuracy, context relevance, alignment to objectives, and justification quality. CLT supports the decision to:
1. Sequence skills from simple to complex, so learners first practice identifying errors before they must defend evaluative judgments and revise content (Instructional Design Team, n.d., Sweller, 1988).2. Chunk information into manageable steps, using structured templates and short, focused practice activities, rather than long, open ended tasks that increase extraneous load (Malamed, n.d., Pappas, 2016).3. Use consistent tools and formats, such as a repeated rubric structure and standardized checklists, which lowers cognitive friction and lets learners focus on the evaluation skill rather than navigation or formatting.
Operational implementation in Module 2 CLT directly supports the module design decisions you are using:
-The module begins with a multiple choice pre test to establish baseline recognition patterns with minimal cognitive demand.
- Learners then complete a structured analysis checklist that reduces overload by narrowing attention to specific defect categories.
- Only after structured practice do learners complete the evaluative memo and revised artifact, which are higher demand tasks.Theory 2: Constructivism and Social Constructivism
Constructivism emphasizes that learners build knowledge by actively interpreting information and integrating it with prior understanding. Social constructivism extends this idea by emphasizing learning through dialogue, feedback, and meaning making within a community (Vygotsky, 1978).
How constructivism drives the design of learning activities Evaluating A.I. generated instruction is not a single right answer skill. It requires interpretation, professional judgment, and justification using criteria. Constructivism supports instructional choices that require learners to:
- Work with realistic artifacts that contain imperfect content and incomplete transparency. - Apply evaluation criteria, explain why an issue matters, and decide what to do next. - Compare their judgment to standards, exemplars, and peer perspectives.
How social constructivism fits an online, asynchronous course In asynchronous courses, discussion boards and structured peer review can function as a learning engine, not just participation. Learners refine their reasoning when they see how others interpret the same flawed content and when they must defend their own evaluation decisions. In this minicourse, peer dialogue is a practical way to strengthen:
- Bias detection awareness - Ethical risk reasoning - Calibration of rubric scoring consistency
Operational implementation in Module 2. Social constructivism is embedded through:
- A discussion forum where learners post one identified issue, explain why it matters, and propose a revision.
- A peer feedback protocol where learners comment using the same rubric language, which increases reliability and shared expectations.
- Instructor feedback that focuses on justification quality and alignment to objectives, reinforcing a shared standard of evidence.
This approach aligns with the course expectation that activities and assessments should support learning progression and reduce learner confusion by clarifying purpose and evaluation criteria (Hartley & Cha, n.d., Kurt, 2020).Theory 3: Adult Learning Theory
Adult Learning Theory, also called andragogy, emphasizes that adult learners are goal oriented, practical, internally motivated, and benefit from autonomy, relevance, and problem centered learning (Knowles et al., 2015).
How adult learning theory drives the design of learning activities Many learners in this minicourse will be educators, trainers, or professionals who want immediately usable evaluation skills. Adult learning theory supports design choices such as:
- Clear performance relevance, learners evaluate content they could realistically encounter at work. - Choice within constraints, learners follow required criteria but can propose revisions that match their context. - Reflection on professional practice, learners explain how they would apply ethical oversight in their real environment.
Operational implementation in Module 2. Andragogy is visible in:
- The Ethical Decision Memo, which mirrors a real workplace deliverable and requires a recommendation, risks, and mitigation steps.
- The Summative Revised Artifact and Change Log, which resembles a professional quality assurance workflow and documents human oversight decisions.
- The inclusion of transparency and attribution decisions, supporting accountability practices adults may need in real instructional settings.
How the Theories Work Together in One Aligned System
This blended approach is intentionally aligned to the assessment system you are building:
• Cognitive Load Theory shapes structure, chunking, and sequencing to make complex evaluation skills learnable in an online format (Sweller, 1988, Malamed, n.d.).
• Constructivism and Social Constructivism shape the use of realistic artifacts, justification, peer dialogue, and feedback cycles that develop professional reasoning (Vygotsky, 1978).
• Adult Learning Theory ensures the work is relevant, problem centered, and autonomy supportive, which improves motivation and transfer (Knowles et al., 2015).
Together, these theories justify why the learning activities and assessments progress from recognition, to structured analysis, to evaluative judgment, to authentic revision and documentation, which is consistent with strong course alignment principles (Hartley & Cha, n.d., Kurt, 2020).
References
Allen, M. W. (2012). Leaving ADDIE for SAM: An agile model for developing the best learning experiences. ASTD Press.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction (8th ed.). Pearson.
Knowles, M. S. (1984). The adult learner: A neglected species (3rd ed.). Gulf Publishing.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Morrison, G. R., Ross, S. M., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction (8th ed.). Wiley.
OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com
Reiser, R. A., & Dempsey, J. V. (2018). Trends and issues in instructional design and technology (4th ed.). Pearson.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
University of Maryland Global Campus. (n.d.). LDTC 605 course materials. University of Maryland Global Campus.
Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). ASCD.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction (8th ed.). Pearson.
Knowles, M. S. (1984). The adult learner: A neglected species (3rd ed.). Gulf Publishing.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Morrison, G. R., Ross, S. M., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction (8th ed.). Wiley.
OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com
Reiser, R. A., & Dempsey, J. V. (2018). Trends and issues in instructional design and technology (4th ed.). Pearson.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
University of Maryland Global Campus. (n.d.). LDTC 605 course materials. University of Maryland Global Campus.
Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). ASCD.