Item Generation Using Constraint Coding

For our medical example, we defined the constraints in the cognitive model. While this format is convenient for reading, it is not convenient for machine processing. As a result, the constraints must be converted into bitmasks. While this process sounds complicated, it can be simplified and expedited with the use of checkboxes. An example of a checkbox representation is the element cough type using the correct options given as follows:

COUGH_TYPE

Common Cold

Seasonal Flu

mild

0

hacking

0

severe

0

This checkbox summary produces a relation matrix of:

When we consider all elements in the medical model, we get the full constraint matrix, which was presented in Chapter 4 as follows:

Element

Scenario

Cough Type

Body Aches

Onset

Results

Temperature

Element

Values

Common Cold

Seasonal Flu

mild

hacking

severe

slight body aches

slight body pains

severe body aches

severe body pains

over a few days

within 3-6 hours

suddenly

Positive

Negative

CO

VI

37.8

CO

CD

39.5

Scenario

Common Cold

1

0

1

1

0

1

1

0

0

1

0

0

0

1

1

1

0

0

Seasonal Flu

0

1

0

0

1

0

0

1

1

0

1

1

1

0

0

0

1

1

Cough Type

mild

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hacking

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

severe

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Body Aches

slight body aches

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

slight body pains

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

severe body aches

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

severe body pains

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Onset

over a few days

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

within 3-6 hours

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

suddenly

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Results

Positive

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Negative

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Temperature

37

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

37.8

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

39

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

39.5

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Systematic Distractor Generation

Our second example uses the key features model. To specify the distrac- tors for this model, we need to conduct three steps: identify the rationales, group distractors by rationales, and ensure that the model can generate enough incorrect options for each correct option. The features for the distractors, as presented in Chapter 5, can be summarized as follows:

1 .

Bronchitis

(CC, SF)

2 .

Streptococcal infection

(CC, SF)

3 .

Hay Fever

(CC, SF)

4 .

Otitis Media

(CC, SF)

5 .

Acute Sinusitis

(CC)

6 .

Bacterial Pneumonia

(SF)

From this summary, we can then specifically identify two rationales: rationales related to the correct option and rationales not related to the correct option.

Related

Not related

Acute Sinusitis, Bacterial Pneumonia

Bronchitis, Streptococcal Infection, Hay Fever, Otitis Media

The total number of options in the parent item is five. To simplify our presentation, we generated items with four options in total, meaning we need three distractors for each generated item. The first distractor is easy to identify: flu for cold and cold for flu.

Distractorl: (1) Common Cold, (2) Seasonal Flu

The second distractor can be located in the list of distractors related to key features:

Distractor2: (1) Acute Sinusitis, (2) Bacterial Pneumonia

The remaining distractors can be randomly selected from the list of the values not related to the key features. We just need to ensure that the generation process does not produce duplicates among the item options:

Distractor3: (1) Bronchitis, (2) Streptococcal Infection (3) Hay Fever, (4) Otitis Media

Distractor4: (1) Bronchitis, (2) Streptococcal Infection (3) Hay Fever, (4) Otitis Media

Once we have defined all four distractor values, we can produce the item model in its final form:

Stem

[AGE] female sees her doctor and reports that she's been experiencing a [COUGH TYPE] cough and [BODY_ACHES] that have developed [ONSET]. Upon examination, she presents with an oral temperature of [TEMPERATURE]°C. What is the most likely diagnosis?

Elements

AGE: (1) An 18, (2) A [AGEJRANGE]

AGE RANGE: 19 to 30, by

COUGH_TYPE: (1) mild, (2) hacking, (3) severe

BODY ACHES: (1) slight body aches, (2) slight body pains, (3) severe body aches, (4) severe body pains

ONSET: (1) over a few days, (2) within 3-6 hours, (3) suddenly

TEMPERATURE: (1) 37, (2) 37.8, (3) 39, (4) 39.5

Key

Common Cold, Seasonal Flu

Distractors

Distractorl: (1) Common Cold, (2) Seasonal Flu

Distractor2: (1) Acute Sinusitis, (2) Bacterial Pneumonia

Distractor3: (1) Bronchitis, (2) Streptococcal Infection, (3) Hay Fever,

(4) Otitis Media

Distractor4: (1) Bronchitis, (2) Streptococcal Infection, (3) Hay Fever,

(4) Otitis Media

A Sample of Generated Medical Items

Using the method presented in this chapter, a total of 1,248 medical items were generated. When the second layer was added to the medical example (see Chapter 5, Table 5.7), 1 1,232 items were generated. A sample of the generated content from the 1-layer medical model created using the example in this chapter includes the following items:

1 An 18-year-old female sees her doctor and reports that she's been experiencing a mild cough and slight body aches that have developed over a few days. Upon examination, she presents with an oral temperature of 37°C. What is the most likely diagnosis?

A. Bronchitis

B. Seasonal flu

C. Common cold *

D. D. Bacterial pneumonia

E. E. Streptococcal infection

2 A 19-year-old female sees her doctor and reports that she's been experiencing a mild cough and slight body aches that have developed over a few days. Upon examination, she presents with an oral temperature of 37.8°C. What is the most likely diagnosis?

A. Seasonal flu

B. Otitis media

C. Common cold *

D. Bacterial pneumonia

E. Streptococcal infection

3 A 29-year-old female sees her doctor and reports that she's been experiencing a hacking cough and slight body aches that have developed over a few days. Upon examination, she presents with an oral temperature of 37°C. What is the most likely diagnosis?

A. Bronchitis

B. Hay fever

C. Seasonal flu

D. Common cold *

E. Bacterial pneumonia

4 А 21 -year-old female sees her doctor and reports that she's been experiencing a severe cough and severe body aches that have developed within three to six hours. Upon examination, she presents with an oral temperature of 39°C. What is the most likely diagnosis?

A. Bronchitis

B. Seasonal flu *

C. Common cold

D. Acute sinusitis

E. Streptococcal infection

5 A 24-year-old female sees her doctor and reports that she's been experiencing a severe cough and severe body aches that have developed suddenly. Upon examination, she presents with an oral temperature of 39.5°C. What is the most likely diagnosis?

A. Seasonal flu *

B. Otitis media

C. Common cold

D. Acute sinusitis

E. Streptococcal infection

6 A 23-year-old female sees her doctor and reports that she's been experiencing a severe cough and severe body pains that have developed suddenly. Upon examination, she presents with an oral temperature of 39°C. What is the most likely diagnosis?

A. Hay fever

B. Seasonal flu *

C. Common cold

D. Acute sinusitis

E. Streptococcal infection

Methods for Validating Generated Items

 
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