6.6: Randomized MC True and False Statements

When to use MC statements vs matching sets

Statement-based multiple choice is best when you want students to evaluate ideas (truth or falsehood), not just map terms to definitions.

Good fits for MC statements:

• "Which statement is TRUE/FALSE?" conceptual checks
• Misconception diagnostics (wrong molecule, wrong direction, wrong location, sign flips)
• "Why" and "what changes" logic that is hard to make one-to-one

If your goal is to map terms or components to their matching definitions or functions (especially pathways and part-to-function), consider a matching set instead (see Randomized Matching Problems).

Copy and edit (RadioButtons per statement)

$tf = [ 'True', 'False' ];
$rb1 = RadioButtons($tf, 'True');
$rb2 = RadioButtons($tf, 'False');

BEGIN_PGML
1. The enzyme activity increases with temperature. [_]{$rb1}

2. The inhibitor decreases Vmax in competitive inhibition. [_]{$rb2}
END_PGML

Common failure and fix

Broken: parserCheckboxList.pl used for statement checks
What you will see: widget missing or render failure
Fix: use RadioButtons or PopUp per statement

Styling and emphasis

• Use HTML spans to emphasize key words like NOT or ONLY.
• Keep each statement short so students can scan quickly.

Creating statement banks with YAML

Instead of hand-coding each PGML problem, you can define pools of true and false statements in a YAML file and use a generator script to produce multiple-choice questions automatically. Each generated question presents one correct answer among several distractors drawn from the bank.

true_statements:
  truth3a: Enzymes lower activation energy.
  truth3b: Enzymes speed reactions by lowering activation energy.

replacement_rules:
  cAMP: "<strong>cAMP</strong>"
  cGMP: "<strong>cGMP</strong>"

Generating PGML files from YAML

The yaml_mc_statements_to_pgml.py script reads the same YAML schema described above and generates WeBWorK PGML problem files. This workflow uses the same statement bank format, so you can maintain one YAML file and produce both Blackboard export files and PGML problems from it.

source source_me.sh
python3 yaml_mc_statements_to_pgml.py -y your_set.yml -x 20

The -x flag controls how many question variants to generate. By default, the generator creates both "Which statement is TRUE?" and "Which statement is FALSE?" forms. You can disable either form with --no-true or --no-false.

Second messenger signaling example (complete YAML)

---
topic: second messenger signaling
learning_objective: Distinguish cAMP vs cGMP roles and enzymes that regulate them.

replacement_rules:
  cAMP: "<strong>cAMP</strong>"
  cGMP: "<strong>cGMP</strong>"

true_statements:
  truth1a: cAMP is a second messenger made from ATP
  truth1b: cyclic AMP (cAMP) is produced from ATP by adenylyl cyclase
  truth2: phosphodiesterase breaks down cyclic nucleotides to terminate a signal
  truth3: Protein kinase A (PKA) is activated by cAMP

false_statements:
  false1a: cAMP is a second messenger made from glucose
  false1b: cyclic AMP (cAMP) is produced from ADP by phosphodiesterase
  false2: phosphodiesterase makes cAMP from ATP
  false3: Protein kinase A (PKA) is activated by cGMP

Note the conflict groups: truth1a and truth1b both describe how cAMP is made, so they share number 1 and will not appear together. The same applies to false1a and false1b.

What makes a good false statement

False statements should be "plausible but wrong" in a way that diagnoses a specific misconception. Good patterns include:

• Wrong substrate or reactant: ATP vs GTP, NADH vs NADPH
• Wrong directionality: influx vs efflux, synthesis vs degradation
• Wrong cellular location: nucleus vs cytosol vs mitochondrion
• Swapped cofactors or ions: Mg2+ vs Ca2+
• Wrong sign or relationship: reversed thermodynamic logic using delta-G or equilibrium

Practical trick: write the true statement first, then create the false version by changing exactly one detail.

Avoiding accidental cues

Three quick checks that prevent students from using test-taking strategies instead of content knowledge:

• Similar length: Do not let one statement be noticeably longer or shorter than the others.
• Parallel grammar: Use the same sentence structure across all choices (all noun phrases, or all full sentences).
• No unique keywords: Avoid terms that appear in only one choice, as they can give away the answer.

Additional statement hygiene: one main idea per statement (avoid "and also..." bundles), use concrete biology verbs (binds, phosphorylates, opens, inhibits), avoid double negatives, and define abbreviations on first use.

Difficulty control

You can adjust question difficulty by varying how the false statements are constructed:

Quality checks for statement banks

Before using a bank in an assessment, run through this checklist:

• Misfiled statements: Confirm all truth* entries are under true_statements and all false* entries are under false_statements.
• ID collisions: Do not reuse the same ID for two unrelated meanings (e.g., two different truth5a ideas).
• Duplicate text: The generator fails if the exact same sentence appears twice, even across true and false sections.
• Replacement rule overlap: Use specific, non-overlapping phrases to avoid nested spans or partial matches.
• Grammar scan: Fix typos and subject/verb agreement. Correct grammar improves student trust even when the biology is right.

Biology examples to model

• Experimental design statements (controls, variables, replication).
• Pathway regulation claims (activation vs repression).

Complete working example

Enzyme true/false statements -- A full problem with per-statement RadioButtons evaluating claims about enzyme behavior.

## TITLE('Enzyme behavior true or false')
## DESCRIPTION
## Evaluate true/false statements about enzyme regulation
## and kinetics using per-statement RadioButtons.
## ENDDESCRIPTION
## KEYWORDS('enzymes','true false','RadioButtons','kinetics')
## DBsubject('Biochemistry')
## DBchapter('Enzymes')
## DBsection('Regulation')
## Date('2026-02-15')
## Author('Author name')
## Institution('Institution name')

DOCUMENT();
loadMacros(
	"PGstandard.pl",
	"PGML.pl",
	"parserRadioButtons.pl",
	"PGcourse.pl",
);

$tf = [ 'True', 'False' ];
$rb1 = RadioButtons($tf, 'True');
$rb2 = RadioButtons($tf, 'False');
$rb3 = RadioButtons($tf, 'True');
$rb4 = RadioButtons($tf, 'False');

BEGIN_PGML
For each statement about enzymes, select True or False.

1. Enzymes increase the rate of a reaction without being consumed. [_]{$rb1}

2. A competitive inhibitor decreases the Vmax of an enzyme. [_]{$rb2}

3. Allosteric regulation involves binding at a site other than the active site. [_]{$rb3}

4. Increasing temperature always increases enzyme activity. [_]{$rb4}
END_PGML

ENDDOCUMENT();

Apply it today

• Use one RadioButtons object per statement and keep answer keys aligned.
• Avoid checkbox macros not available in the PG 2.17 subset.
• Create YAML statement banks with 8 to 20 items per truth category for good question variety.
• Use conflict group IDs (e.g., truth4a, truth4b) for equivalent phrasings of the same concept.
• Write the true statement first, then create the false version by changing one detail.
• Run the quality checklist before deploying a bank to an assessment.