TY - JOUR
T1 - An approach for enhancing and measuring information comprehensibility for engineering designers
T2 - Applied to patent documents
AU - McTeague, Chris
AU - Chatzimichali, Anna
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/9/20
Y1 - 2024/9/20
N2 - Computational simplification tools can make complex information sources easier to read for engineering designers. To guide and evaluate such approaches, it is necessary to understand how designers process information and how that information can be enhanced and measured. Here, we establish an approach for enhancing and measuring the comprehensibility of technical information for engineering designers. It is grounded in theories of document search and comprehension and provides theoretically supported principles for enhancing information and methods for measuring comprehension experimentally. It is tailored for engineering design in that it (i) does not summarize or remove potentially important information, (ii) is suitable for long, complex sources of information, (iii) can be applied in experiments that simulate real-life information sharing scenarios, and (iv) enables the measurement of domain-specific comprehension. The feasibility of the approach was tested by using patent documents as a test case since they represent a valuable but underutilized source of technical information. A 2 (patent documents) × 2 (conditions: control vs. modified) experiment was conducted with 28 professional engineering designers. Two patent documents were modified with six information design principles. Comprehension scores were higher for the modified patent than for the control, but the change was not statistically significant (p = 0.073). We attribute this either to redundancy effects causing a smaller than expected overall improvement in performance, or differences in prior knowledge for each patent. Overall, this approach offers a novel method for investigating and measuring information comprehensibility in engineering design; however, its effectiveness in enhancing information comprehensibility remains unvalidated.
AB - Computational simplification tools can make complex information sources easier to read for engineering designers. To guide and evaluate such approaches, it is necessary to understand how designers process information and how that information can be enhanced and measured. Here, we establish an approach for enhancing and measuring the comprehensibility of technical information for engineering designers. It is grounded in theories of document search and comprehension and provides theoretically supported principles for enhancing information and methods for measuring comprehension experimentally. It is tailored for engineering design in that it (i) does not summarize or remove potentially important information, (ii) is suitable for long, complex sources of information, (iii) can be applied in experiments that simulate real-life information sharing scenarios, and (iv) enables the measurement of domain-specific comprehension. The feasibility of the approach was tested by using patent documents as a test case since they represent a valuable but underutilized source of technical information. A 2 (patent documents) × 2 (conditions: control vs. modified) experiment was conducted with 28 professional engineering designers. Two patent documents were modified with six information design principles. Comprehension scores were higher for the modified patent than for the control, but the change was not statistically significant (p = 0.073). We attribute this either to redundancy effects causing a smaller than expected overall improvement in performance, or differences in prior knowledge for each patent. Overall, this approach offers a novel method for investigating and measuring information comprehensibility in engineering design; however, its effectiveness in enhancing information comprehensibility remains unvalidated.
KW - cognitive load
KW - comprehension
KW - engineering design
KW - patents
KW - simplification
UR - http://www.scopus.com/inward/record.url?scp=85205017089&partnerID=8YFLogxK
U2 - 10.1017/S0890060424000076
DO - 10.1017/S0890060424000076
M3 - Article
AN - SCOPUS:85205017089
SN - 0890-0604
VL - 38
JO - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
JF - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
M1 - e10
ER -