Analysis of Vocabulary Learning Strategies in Vocabulary Learning Software from the Perspective of Psycholinguistics

Abstract

The present paper selects four mainstream vocabulary learning software in the market, namely, BaiCiZhan, BuBeiDanCi, Maimemo, and Shanbay Words. Based on the psycholinguistic framework, and adopting the classification of vocabulary learning strategies by O'Malley & Chamot, the current study discusses the learning strategies in vocabulary learning software from three aspects: vocabulary understanding, memorizing, and reviewing. The results show that in terms of the number of strategies achieved, Maimemo uses the most vocabulary learning strategies, so the software can better meet the learning needs of most learners based on different learning strategies. BuBeiDanCi uses the least learning strategies and the learning mode is relatively flexible, which is more suitable for learners who use the fragmented time for vocabulary learning. In terms of the implementation form of the strategy, BaiCiZhan is illustrated with pictures and texts, and BuBeiDanCi has diverse audio, these two are more suitable for interest learning. Maimemo pays more attention to the actual use of vocabulary, so it is more suitable for the learning needs of improving skills. Shanbay Word is mainly based on the memory of English and Chinese definitions of words, which is closer to traditional book learning and more suitable for learners who tend to traditional learning mode. It hopes to help learners better understand the learning strategies in various software and help learners choose more suitable vocabulary learning software, to improve learners' vocabulary learning efficiency.