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   models of efl learners’ vocabulary development: spreading activation vs. hierarchical network model  
   
نویسنده abbasian gholam-reza ,farokhi fatemeh
منبع journal of language and translation - 2019 - دوره : 9 - شماره : 1 - صفحه:93 -108
چکیده    Semantic network approaches view organization or representation of internal lexicon in the form of either spreading or hierarchical system identified, respectively, as spreading activation model (sam) and hierarchical network model (hnm). however, the validity of either model is amongst the intact issues in the literature which can be studied through basing the instruction compatible with the principles of each model. in a bid to fill this gap, this study was designed to empirically verify the effectiveness of sam compared to hnm in both developing and retention rate of vocabulary knowledge. to this end, 67 iranian efl learners were divided into two experimental groups (34 and 33) and one was exposed to hnmbased while the other to sam-based vocabulary instruction for 10 sessions. in the light of groupcomparison experimental design, the participants' both immediate achievement and long-term storage were measured through an immediate and a delayed post-tests, respectively. the parametric statistical analyses showed that the group being exposed to hnm-based instruction outperformed the other group in both the immediate and delayed post-tests. the findings bear two distinct messages: yielding support to more validity of hnm as a model of internal lexicon organization and supporting the educational implications of cognitively compatible instruction of language components.
کلیدواژه hierarchical network model ,internal lexicon ,spreading activation model ,vocabulary development ,vocabulary retention
آدرس imam ali university, iran. islamic azad university, south tehran branch, faculty of persian literature and foreign languages, iran, islamic azad university, south tehran branch, faculty of persian literature and foreign languages, iran
پست الکترونیکی sarafarokhi.sf@gmail.com
 
     
   
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