PRIMMBASE: A PRIMM-Based E-Worksheet to Enhance Logical Thinking in Database Learning

Abstract

Learning Data Manipulation Language requires students not only to memorize SQL syntax but also to analyze table relationships, predict query outputs, identify errors, and construct appropriate queries. This study aimed to develop PRIMMBASE, a web-based electronic student worksheet integrating the Predict, Run, Investigate, Modify, and Make stages, and to examine students’ logical thinking improvement and responses to its use. The study employed a Research and Development approach using the ADDIE model and a one-group pretest–posttest design. PRIMMBASE was implemented with 35 eleventh-grade Software Engineering students at a vocational high school, while the N-Gain analysis used 31 complete pretest–posttest pairs. Expert validation results reached 89.52% for media and content quality, 95.38% for conformity with PRIMM, and 100% for the virtual assistant’s scaffolding functions. Students’ logical thinking achieved an average N-Gain of 0.755, categorized as high. Analysis, synthesis, comparison, and classification showed high improvement, whereas generalization reached a moderate category. Students’ responses obtained 87.11%, indicating a very positive evaluation of usability. These findings suggest that PRIMMBASE is feasible and potentially supports logical thinking through structured query-learning activities, although further studies involving control groups are required to confirm its effectiveness.