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CLOs vs. Learning Objectives Within the Minicourse

Course Learning Outcomes (CLOs), also referred to as terminal outcomes, define the overarching competencies learners are expected to demonstrate at the conclusion of a course. They reflect cumulative achievement and often require higher-order cognitive processing such as analysis, evaluation, or transfer. CLOs function as the anchor for backward design because they determine what evidence of learning must be collected and how instruction should be structured (Wiggins & McTighe, 2011). In my minicourse, the CLOs define the terminal competencies learners must demonstrate by the end of the course. For example:
  • Learners will evaluate the credibility of digital and A.I. generated information using structured evaluative criteria to determine reliability, bias, and contextual relevance in instructional settings.
  • Learners will embed A.I. tools intentionally into learning design workflows while maintaining documented human judgment and ethical transparency.
  • These outcomes represent performance expectations that require higher-order thinking and transfer.
  • In contrast, learning objectives, sometimes referred to as enabling objectives or module-level objectives, represent incremental steps that scaffold learners toward achieving the broader CLOs. They are narrower in scope, time-bound, and directly measurable.
  • Module-level learning objectives break these terminal outcomes into measurable steps. Within the module titled:
Module 2: Evaluating A.I. Generated Instructional Content and Ethical Risk
The enabling objectives include:
  • Learners will analyze a generative A.I. lesson draft to identify factual inaccuracies, hallucinations, and indicators of bias.
  • Learners will apply a structured evaluation rubric to assess reliability, instructional alignment, and contextual appropriateness.
  • Learners will evaluate the ethical implications of using the output in a defined instructional context.
  • Learners will revise the A.I. generated artifact to meet clearly defined instructional and ethical standards.
These objectives directly scaffold CLO 1, CLO 2, and CLO 5, for instance, the Analyze objective operationalizes CLO 2 by requiring learners to break down the output into component parts and identify limitations. The Apply objective operationalizes CLO 1 by requiring the use of defined criteria rather than intuitive judgment. The Revise objective begins to support CLO 4 by modeling intentional integration with human oversight. While CLOs define the destination, learning objectives define the measurable milestones that guide learners toward that destination.

Heading

Bloom’s Taxonomy provides a hierarchical framework for classifying cognitive processes, ranging from lower-order thinking skills to higher-order reasoning. The six revised levels include:
  • Remembering, recalling relevant knowledge.
  • Understanding, explaining ideas or interpreting meaning.
  • Applying, using knowledge in new situations.
  • Analyzing, breaking information into parts to identify relationships.
  • Evaluating, making judgments based on criteria.
  • Creating, producing new or original work.
When drafting objectives for my minicourse, I intentionally selected verbs aligned with the cognitive demand of each activity. For instance, identifying bias reflects analysis, while constructing a revised instructional artifact reflects creation. This intentional verb alignment ensures that assessment matches cognitive expectations and supports progressive skill development.

Bloom’s Taxonomy and Cognitive Alignment

Bloom’s Taxonomy guided the verb selection for this module. I intentionally sequenced the objectives to reflect increasing cognitive demand:
  • Analyze, learners identify structural flaws and bias patterns.
  • Apply, learners use a structured framework rather than informal reasoning.
  • Evaluate, learners justify whether the content should be used.
  • Revise, learners produce a corrected artifact.
This progression ensures learners move beyond recognition toward production and justification. The verbs were selected only after confirming that the activity truly required that cognitive process, for example, I initially drafted “identify ethical concerns,” but revised it to “evaluate ethical implications” because the activity requires judgment based on defined criteria rather than simple recognition.
Content and Feedback Loop Within the Module
SME and Resource Alignment
Learners begin by reviewing a flawed A.I. generated lesson. They complete a guided analysis worksheet, then participate in a calibration exercise using a shared sample artifact to align interpretation of rubric criteria. After peer discussion, learners submit a revised lesson draft with a written justification explaining their evaluation decisions and ethical reasoning.
Instructor feedback is provided using a structured rubric aligned directly to the module objectives. This ensures that Bloom’s verbs are not symbolic but measurable.
As the instructional designer and course developer, I serve as the primary subject matter expert for instructional design integration. However, the module content is informed by:
-Pappas (2015), who emphasizes structured collaboration with SMEs to ensure accuracy and alignment.-Raymond (2023), who highlights the necessity of human oversight in A.I. generated content.-Peck (2021), who provides applied guidance on writing measurable objectives aligned with Bloom’s levels.
These resources directly inform how evaluation criteria, ethical standards, and objective measurability are defined within the module.

References

Allen, M. W., & Sites, R. (2012). Leaving ADDIE for SAM: An agile model for developing the best learning experiences. ASTD Press. Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press. Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4), 212–218. https://doi.org/10.1207/s15430421tip4104_2 OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com Pappas, C. (2015, September 15). Working with subject matter experts: The ultimate guide. eLearning Industry. https://elearningindustry.com/working-subject-matter-experts-ultimate-guide Shabatura, J. (2022, July 26). Using Bloom’s taxonomy to write effective learning objectives. University of Arkansas. https://tips.uark.edu/using-blooms-taxonomy/ Wiggins, G., & McTighe, J. (2011). The understanding by design guide to creating high-quality units. ASCD.

Dr. J. Ryner, Ed.D.

PHONE: 954-404-4499 Email: J.Ryner@aol.com
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