Knowledge Structure Measures of Reader’s Situation Models Across Languages: Translation Engenders Richer Structure

Abstract

In order to further validate and extend the application of recent knowledge structure (KS) measures to second language settings, this investigation explores how second language (L2, English) situation models are influenced by first language (L1, Korean) translation tasks. Fifty Korean low proficient English language learners were asked to read an L2 story and then complete L2 concept map and summary writing tasks, with or without an intervening L1 production tasks (Translated versus Directed conditions). Posttest comprehension was measured using the TOEFL multiple-choice items associated with the story (both in L2). KS elicited as concept maps and as text summaries were used to represent the situation models before, during, and after writing. For analysis, all of the participants’ maps and writing artifacts were converted into Pathfinder Networks (PFNets) that were analyzed using two distinctly different approaches, correlation of the raw proximity data and also degree centrality of the PFNets, in order to analyze the PFNets statistically and to visually describe KS cognitive state changes over time. The correlation results showed that the Translated Writing participants’ L2 KS relative to the Directed Writing condition are more similar to that of an expert and are significantly correlated with comprehension posttest scores. Including L1 tasks substantially improved the qualities of the L2 KS artifacts and underlying mental structures related to reading comprehension. In addition, the average centrality results showed that the KS ‘form’ of the participants’ PFNets who translated were relational network structures relative to the Directed Writing group’s (English only) PFNets that had a more linear structure that matched the text surface structure, suggesting a fundamental way that L1 and L2 cognitive processing differs.