Choosing specialized vocabulary to teach with data-driven learning: An example from civil engineering

Abstract

This paper presents a three-part methodology for identifying special-purposes words to teach in data-driven learning (DDL) vocabulary activities. Previous methods have focused on either identifying important words for an English for Specific Purposes (ESP) context or identifying learner vocabulary gaps—and little or no research has addressed how to determine whether specific words are well suited to teaching through DDL rather than through a more traditional, deductive approach. The system in this study used a corpus-based approach to identify words that are (1) important to the ESP context, (2) difficult for students, and (3) well suited to teaching through DDL. This study applied the system to the context of civil engineering and found that it was overall effective in identifying 18 words that are prevalent in civil engineering writing, that were problematic for the students whose writing was examined, and that showed indications of being well suited to DDL. This paper discusses a drawback of the system—the time required to apply it—and discusses two valuable strengths: revealing how the words functioned in civil engineering discourse and identifying words not overtly connected to engineering (e.g., existing or using) that could be easily overlooked by an instructor.