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.