Measuring Language Mindsets and Modeling Their Relations With Goal Orientations and Emotional and Behavioral Responses in Failure Situations

Abstract

Some people ascribe successful language learning to an innate aptitude that cannot be further developed, at least after a certain young age (i.e., an entity mindset), while other people believe that language learning ability can be improved (i.e., an incremental mindset). The purpose of this research is to (a) introduce the Language Mindsets Inventory (LMI), and (b) test the mindsets-goals-responses model, which maintains that learners' mindsets predict the goals that they set for language learning, and that these goals in turn affect how they respond to difficult academic and communication episodes. Correlational and factor analyses provided evidence of the LMI's valid and reliable use in research with university-level language students. Path analyses showed that regardless of their competence level, greater endorsement of an incremental mindset was associated with the goal of learning more about the language, and this learning goal in turn predicted greater mastery and less helpless responses in failure situations. Greater endorsement of an entity mindset predicted the goal of demonstrating competence (i.e., performance approach goals) when students believed that they had stronger language skills. The use of the LMI in future research and the importance of supporting incremental mindsets for language education are discussed.

Feedback‐Seeking Behavior in Language Learning: Basic Components and Motivational Antecedents

“…The second analysis was run with two factors predetermined to be extracted. The results showed that the two factors (Appendix ), representing a Growth L2 Mindset and a Fixed L2 Mindset , explained 75.7% of the variance, confirming the two‐factor solution emerged in the studies by Lou and Noels () and Waller and Papi (). In addition, Kaiser–Meyer–Olkin measure of sampling adequacy (.91) was excellent (i.e., >.05), and Bartlett's test of sphericity was statistically significant (χ2(29) = 1975.29, p < .001), indicating a good fit for the dataset.…”

“…In addition, the relationship between Growth L2 Mindset and Feedback Monitoring was partially mediated by Development-Approach. These results confirm that learners with a growth mindset and development-approach goals tend to perceive setbacks such as being corrected as opportunities for growth, attribute gaps in their competence to their efforts and strategies rather than their abilities, and take action to improve their competence through the use of extra effort and better strategies (e.g., Dweck, 1999;Hong et al, 1999;Lou & Noels, 2017;Mangels et al, 2006). Therefore, these learners are not typically concerned with the ego and self-presentation costs of feedback monitoring due to their tendency to focus on developing rather than validating their L2 abilities.…”

“…Waller and Papi () developed similar scales to measure L2 writing mindsets, which correlated with but were independent from Dweck's general scales; they also predicted L2 writing motivation and feedback orientations more strongly than the general scales. Similarly, Lou and Noels () found that language mindsets correlated with but were distinct from other mindsets including math, sports, and general mindsets. Therefore, in this study, L2‐specific versions of eight items from Dweck's questionnaire were used to measure language mindsets.…”

“…Whereas Dweck's theory has recently been employed in a few studies in the field of language learning (e.g., Lou & Noels, , ; Mercer & Ryan, ), the relationship between L2 learners’ mindsets, beliefs, or attitudes toward CF, and by extension, their FSB has remained unexplored. One notable exception is a study by Waller and Papi (), which found that a fixed L2 writing mindset positively predicted a feedback‐avoiding orientation whereas a growth mindset predicted a feedback‐seeking orientation and 50% of variance in L2 writing motivation, suggesting the importance of this theory in understanding the interface between motivation and FSB.…”

Data mining in foreign language learning

“…However, there are some papers in which DM is used for each isolated application, for instance learner motivation (13% of papers), learning styles (8%), provide feedback for instructors (9%), detecting language anxiety (6%), predicting performance (14%), L2 orientations (8%), language reading comprehension (5%), and detecting grammar issues and assessment (7%). In this analysis, the DM applications most frequently used in the context of FLL are: Predicting performance (Linck et al, 2013;Seker, 2016;Swanson et al, 2016;Wang & Cheng, 2016;Whitehill & Movellan, 2018), learner motivation (Apple, Falout, & Hill, 2013;Li & Zhou, 2017;Saeed et al, 2014;Tajeddin & Moghadam, 2012), provide feedback for instructors (Coskun & Mutlu, 2017;Jiang & Lee, 2017;Kaoropthai, Natakuatoong, & Cooharojananone, 2016;Kieffer & Lesaux, 2012;Rodriguez & Shepard, 2013;Zhao et al, 2015), learning styles (Aslan et al, 2014;Farrington et al, 2015;Hamedi, Pishghadam, & Ghazanfari, 2016;Hsiao, Lan, Kao, & Li, 2017), detecting language anxiety (Baghaei & Ravand, 2015;Cakir & Solak, 2014;Guntzviller et al, 2016;Martin & Valdivia, 2017), and L2 orientations (Allen et al, 2014;Lou & Noels, 2017;Maqsood et al, 2016;Winke, 2013). Figures 8 and 9 show the correlation between the educational level in where the articles mentioned to have developed their proposal and the EDM methods and applications that has been used, respectively.…”