Project Based Learning Gold Standard Design Elements Lookup

Compare observed project-design evidence with independently source- and edition-verified Gold Standard PBL requirements. Retains missing evidence and overlapping candidates; not engagement, learning efficacy or program certification.

Description

Project design can be reviewed against explicit evidence requirements for a particular PBL framework edition.

Project design can be reviewed against explicit evidence requirements for a particular PBL framework edition.^[1]

This tool compares your positive evidence requirements with known observations and keeps unknown evidence visible.

When to use Project Based Learning Gold Standard Design Elements Lookup

Use this tool to calculate or review the supplied observations under the stated rules. Establish the learning task, observed evidence and applicable framework or policy edition.

How to use Project Based Learning Gold Standard Design Elements Lookup and understand its results

Establish the learning task, observed evidence and applicable framework or policy edition.
Enter callerProjectDesignClassIds, callerProjectDesignRequirements, observedProjectDesignEvidence, knownProjectDesignFlags, using the field meanings and rules below, then run the tool.
Read compatibleCallerClasses, fullyWitnessedCallerClasses. Inspect nested result fields where present, and review unknown records separately from known matches.
Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.

Input field	Meaning	Value type	Requirement
callerProjectDesignClassIds	Caller project design class ids	list of string	Required
callerProjectDesignRequirements	Caller project design requirements	numeric matrix	Required
observedProjectDesignEvidence	Observed project design evidence	list of number	Required
knownProjectDesignFlags	Known project design flags; binary 1 means the matched observation is known, 0 means unknown	list of number	Required

Use unique nonblank class IDs and a nonempty binary requirement matrix aligned by class and evidence column. Every class must require at least one positive feature. A one requires presence; zero ignores the feature and does not require absence. Supply binary observed evidence and known flags. A known required zero excludes a class; an unknown required feature retains compatibility but prevents full witnessing. Return all compatible classes and the subset with every required feature known and present. Classes may overlap.

compatible_c=∀j(requirement_cj=0 or known_j=0 or evidence_j=1); witnessed_c=∀j(requirement_cj=0 or (known_j=1 and evidence_j=1))

Use the matching field units and the exact boundary, ordering and reference rules above.

Output field	Meaning	Value type
compatibleCallerClasses	Compatible caller classes	list of string
fullyWitnessedCallerClasses	Fully witnessed caller classes	list of string

json

{
  "input": {
    "callerProjectDesignClassIds": [
      "a",
      "b"
    ],
    "callerProjectDesignRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedProjectDesignEvidence": [
      1,
      0
    ],
    "knownProjectDesignFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Supplied annotations and framework choices determine the result. The tool does not measure learning effects, assess learner intent or independently establish policy applicability. Compatible classes with unknown evidence are not positive identifications. Fully witnessed classes satisfy only the declared positive requirements.

Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above.

Alternative or Complementary methods

For a related step, use Problem Based Learning Maastricht Seven Step Process: Problem-based learning organizes inquiry around a problem and a declared sequence of discussion and independent study. For a complementary comparison, use Backward Design Three Stage Wiggins Mctighe UbD Lookup: Understanding by Design starts with desired results, then considers acceptable evidence and a learning plan.

References

Project Based Learning Gold Standard Design Elements Lookup — tool.io

Similar or alternative tools

Problem Based Learning Maastricht Seven Step Process
Problem-based learning organizes inquiry around a problem and a declared sequence of discussion and independent study.
Backward Design Three Stage Wiggins Mctighe UbD Lookup
Understanding by Design starts with desired results, then considers acceptable evidence and a learning plan.