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Difficulties in understanding the transition between database design stages: An experiment from a semantic distance perspective.

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Title: Difficulties in understanding the transition between database design stages: An experiment from a semantic distance perspective.
Authors: Shin, Shin‐Shing1 (AUTHOR), Lin, Yu‐Shan1 (AUTHOR), Chen, Yi‐Cheng1 (AUTHOR), Chiou, Wei‐Ru2,3,4 (AUTHOR) clementchiou@gmail.com
Source: Journal of Engineering Education. Oct2025, Vol. 114 Issue 4, p1-19. 19p.
Subjects: Database design, Semantic networks (Information theory), Conceptual models, Teaching methods, Cognitive ability, Information modeling, Learning
Abstract: Background: Learners of database courses usually encounter difficulties in building entity‐relationship (ER) models and relational models for database problems. These difficulties may arise because of semantic gaps between the stages of database design. To investigate this issue, we employed semantic network theory—particularly the concept of semantic distance—as an analytical framework. Hypothesis: We hypothesized that (i) the transition from business requirements to ER models involves a greater semantic distance than the ER‐to‐relational conversion, and (ii) this greater distance results in imprecise semantic elaboration, leading to lower learning performance. Method: An experiment was designed in which participants, drawn from a database course, completed two sequential translation tasks: (a) business requirements to ER models, and (b) ER models to relational models. Participants' performances were assessed through problem‐solving effectiveness and efficiency measures. Results: The problem‐solving effectiveness and efficiency of task (b) surpassed those of task (a), suggesting that task (a) entailed greater semantic distance and less precise elaboration of semantic relationships than task (b). Conclusion: Our findings suggest that semantic distance and semantic elaboration are critical factors in database design education, aligning with semantic network theory. Instruction should reduce semantic distance and pay more attention to the teaching of the business requirement to ER model transition. This may prompt scholars to develop more effective teaching methods for database design learning from the perspective of semantic distance. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Difficulties in understanding the transition between database design stages: An experiment from a semantic distance perspective.
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  Data: <searchLink fieldCode="AR" term="%22Shin%2C+Shin‐Shing%22">Shin, Shin‐Shing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Yu‐Shan%22">Lin, Yu‐Shan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yi‐Cheng%22">Chen, Yi‐Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chiou%2C+Wei‐Ru%22">Chiou, Wei‐Ru</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> clementchiou@gmail.com</i>
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  Data: <searchLink fieldCode="DE" term="%22Database+design%22">Database design</searchLink><br /><searchLink fieldCode="DE" term="%22Semantic+networks+%28Information+theory%29%22">Semantic networks (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+models%22">Conceptual models</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Information+modeling%22">Information modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink>
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  Label: Abstract
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  Data: Background: Learners of database courses usually encounter difficulties in building entity‐relationship (ER) models and relational models for database problems. These difficulties may arise because of semantic gaps between the stages of database design. To investigate this issue, we employed semantic network theory—particularly the concept of semantic distance—as an analytical framework. Hypothesis: We hypothesized that (i) the transition from business requirements to ER models involves a greater semantic distance than the ER‐to‐relational conversion, and (ii) this greater distance results in imprecise semantic elaboration, leading to lower learning performance. Method: An experiment was designed in which participants, drawn from a database course, completed two sequential translation tasks: (a) business requirements to ER models, and (b) ER models to relational models. Participants' performances were assessed through problem‐solving effectiveness and efficiency measures. Results: The problem‐solving effectiveness and efficiency of task (b) surpassed those of task (a), suggesting that task (a) entailed greater semantic distance and less precise elaboration of semantic relationships than task (b). Conclusion: Our findings suggest that semantic distance and semantic elaboration are critical factors in database design education, aligning with semantic network theory. Instruction should reduce semantic distance and pay more attention to the teaching of the business requirement to ER model transition. This may prompt scholars to develop more effective teaching methods for database design learning from the perspective of semantic distance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2025
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