The Triple-Decker Model
The book presents the Triple-Decker Model that describes the interactive mechanism among key constituents of English for Specific Purposes Ability (ESPA): language knowledge, strategic competence and background knowledge (nursing knowledge).
First, the model reveals that ESPA constituents can be assigned to three groups according to their roles in determining ESPA in reading: automators (language knowledge and background knowledge) that respond most directly, assistants (the cognitive aspect of strategic competence) that come to assist when automators are insufficient or boggle down, and regulators (the metacognitive aspect of strategic competence) that supervise all cognitive activities. Second, the model demonstrates the effect of strategic competence and background knowledge on ESPA fluctuated with the continuous increase of language knowledge.
The book also demonstrates the use of two innovative analytical techniques: composite scores based on bifactor multidimensional item response theory for scoring ESP reading tests and the multi-layered-moderation analysis (MLMA) for detecting linear and nonlinear moderation relations.
Chapter 7 Conclusions
This concluding chapter summarizes the design of the study and all results corresponding to the research questions posited in Chapter 3. It then presents the theoretical, methodological and practical implications. This is followed by limitations of the study and recommendations for future enquiries. The chapter ends with a final commentary.
This research investigated the interaction among background knowledge, grammatical knowledge, and strategic competence in affecting ESPAR. A total of 1491 second-year nursing students from eight medical and health care colleges across China participated in the study by responding to four scales (i.e., the Medical and Nursing Knowledge Test, the Grammatical Knowledge Test, the Strategic Competence Questionnaire, and the Medical and Nursing English Reading Test) that were designed to measure medical and nursing knowledge, grammatical knowledge, strategic competence and medical and nursing English reading ability, respectively.
Primary data analyses involved four steps: a) using bifactor-M2PL/MGRM models to calibrate and score each scale and computing composite scores, b) performing CFAs on the bifactor-M2PL/MGRM composites to examine the validity of using these composites to recover their underlying factors, c) using SEM with latent interaction to explore the interaction (i.e., linear and quadratic moderations) among background knowledge, grammatical knowledge and strategic competence ←205 | 206→in affecting ESPAR, and d) using SEM with latent interaction to explore the interaction (i.e., linear and quadratic moderations) among particular elements of strategic competence (i.e., planning, monitoring, evaluating, comprehending, memory and retrieving), background knowledge, and grammatical knowledge in affecting ESPAR.
These analyses served...
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