BAN2100 · Module 6 · Introducing Prescriptive Analytics
60%
Exercises weight
40%
Performance task weight
undergrad
Degree level
Meets Expectations (90–100%)
Correctly classifies all four analytics types (descriptive, diagnostic, predictive, prescriptive) with definitions accurately applied to the Tesco dashboard data; identifies an appropriate prescriptive analytics tool for the scenario with correct justification referencing the specific data type and decision context.
Mostly Meets (80–89%)
Classifies three of four analytics types correctly with appropriate definitions; tool selection is appropriate but justification is incomplete or misidentifies one feature of the recommended tool.
Somewhat Meets (70–79%)
Classifies two analytics types correctly; tool selection is plausible but unjustified or supported by incorrect feature identification; one analytics type is mislabeled or conflated with another.
Does Not Meet (<70%)
Cannot correctly distinguish the four analytics types; tool selection is inappropriate for the scenario or absent; definitions are absent or fundamentally incorrect.
Meets Expectations (90–100%)
Advisory brief traces a clear evidence chain from specific Tesco dashboard metrics to the prescriptive recommendation; each analytical claim cites a named data point or output; brief explicitly addresses why the recommended action is preferable to at least one alternative.
Mostly Meets (80–89%)
Evidence chain mostly present; most claims are supported by data references; one or two logical gaps where conclusions are asserted without citing specific dashboard data.
Somewhat Meets (70–79%)
Recommendation is stated but evidence chain is weak — data referenced generally (e.g., 'the sales data shows…') rather than with specific figures; does not address alternatives or competing interpretations.
Does Not Meet (<70%)
Recommendation is unsupported; no specific Tesco data cited; reasoning is circular (restates the recommendation as its own justification) or absent.
Meets Expectations (90–100%)
Identifies a non-obvious business implication from the data synthesis that goes beyond the scenario prompts — e.g., surfaces a risk, a constraint on tool deployment, or an opportunity visible only by integrating data across analytics types; frames the recommendation in terms of organizational decision impact.
Mostly Meets (80–89%)
Adds independent analytical framing beyond restating dashboard outputs; recommendation shows awareness of Tesco's business context and constraints.
Somewhat Meets (70–79%)
Advisory brief restates the scenario prompts without independent contribution; reads as a summary of the MCQ steps rather than an original advisory position.
Does Not Meet (<70%)
No evidence of analytical contribution; brief paraphrases given content without adding insight or framing.
Canvas import
Canvas does not support direct rubric CSV import. Use this view to manually create the rubric in Canvas, or download the JSON above for programmatic creation via the Canvas API.