Visualizing First and Second Language Interactions in Science Reading: A Knowledge Structure Network Approach

ABSTRACT

The present study considers the potential influence of first language (L1) in reading second language (L2) science text. University mixed proficiency Korean English language learners (n = 136) were asked to complete pre- and post-reading sorting maps in L1 or L2 (e.g., sort Korean, read text, sort English). All of the participants’ pre-to-post sorting artifacts were then converted into Pathfinder Networks (PFnets), a graph-theoretic psychometric scaling approach, to represent the most salient knowledge structure (KS) as their situation model of the L2 text. For the low proficiency readers, the results show that when the L2 science text was reframed by the L2 post-reading sorting map, their L2 post-reading KS was more like their pre-reading KS, a linear L2 KS that had no significant correlation with the comprehension posttest. However, when the L2 science text was reframed by the L1 post-reading sorting map, their L1 post-reading KS was more like the L2 text base, which resulted in a relational KS that was more like the content expert and significantly correlated with the comprehension posttest. In contrast, the high proficiency readers post-reading KSs were like the L2 lesson text and their post-reading KSs were all relational in both languages.