One aspect of Japanese that continues to challenge learners is the idiosyncratic writing system, a complex orthography consisting of two phonetic syllabaries and thousands of morphophonemic ‘kanji’ characters. The perceived difficulty of kanji can affect student motivation (Mori & Shimizu, 2007) and can be responsible for higher attrition rates among students who are not familiar with morphographic writing systems (Grainger, 2005). Experimental data on kanji processing models suggests that L1 learning methodologies could be a potentially valuable reference for LX pedagogical research. Studies on cross-linguistic transfer provide evidence that LX decoding efficiency can be impacted negatively among learners with incongruent L1 orthographic backgrounds (Hamada & Koda, 2008, p. 23), suggesting that alphabetic learners may have particular difficulty with kanji. One recommended pedagogical response is to employ a ‘component analysis’ learning strategy that targets awareness on the individual components within kanji characters (Chikamatsu, 2005; Hagiwara, 2016), allowing learners to more efficiently extract semantic and phonological information from the characters. This study tests the recommendation by (1) designing and implementing teaching materials that enact component analysis in a real classroom situation, (2) monitoring changes in kanji processing by alphabetic learners of differential proficiencies, and (3) analysing the nature and possible causes of any changes observed. Implementing the strategy over a 12-week semester, data indicates that a group of ab-initio learners developed more efficient responses in kanji decomposition than a group of lower-intermediate learners with prior kanji learning experience. The finding is interpreted as evidence that using component analysis could facilitate compositional awareness in beginners, despite having a limited lexicon, while learners with prior kanji knowledge may experience some difficulty adjusting to this unfamiliar strategy. Feedback responses indicate that the success of component analysis may be contingent upon factors such as workload, enjoyment, and perceived effectiveness of specific tasks in the prescribed exercises.