Applying Cognitive Linguistics to teaching English prepositions in the EFL classroom

This paper reports on a quasi-experimental study investigating the effect of cognitive linguistics-grounded instruction on learning the prepositions in, on, and at, which are known to pose tremendous difficulty to English language learners due to their language-specific features and polysemous nature. The participants (N = 44) were adolescent learners at a school in Indonesia. They were assigned into the cognitive group and the rule group. The cognitive group was presented with pictorial representations of the prepositions and cognitive tools used to motivate non-spatial uses, while the rule group was provided with rules. Participants’ performance on the three uses (i.e. spatial, temporal and abstract) was measured with pre-, post-, and delayed post-tests in a form of gap filling. The study yielded mixed results. The findings demonstrate that the cognitive group outperformed the rule group in the overall immediate and delayed post-tests. The cognitive group improved significantly in the immediate post-test; however, the positive effect did not last until the delayed post-test. On the other hand, the rule group gained a little in the immediate post-test, but the group’s performance decreased significantly in the delayed post-test. Although there was no indication of long-term effects of the cognitive instruction, the results still indicate a value of applying cognitive linguistics to teaching the prepositions, and thus lend support to the applicability of cognitive linguistic theory in second language instruction.

The Neural Correlates of Similarity- and Rule-based Generalization

The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405–415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks–Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.

Exploration of the Factor Structure of the Kirton Adaption–Innovation Inventory Using Bootstrapping Estimation

Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants—members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. ( SD = 13.9)—showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions.

Revisiting the Factor Structure of the Kirton Adaption-Innovation Inventory

The original Kirton Adaption-Innovation Inventory, used to measure innovative (as opposed to adaptive) individual cognitive styles, has been reported to have three factors: Sufficiency of Originality, Efficiency, and Rule/Group Conformity. In exploring the construct validity of the Kirton Adaption-Innovation Inventory, findings from a 2003 study by Im, Hu, and Toh showed the existence of two subdimensions of the Sufficiency of Originality factor—Idea Generation and Preference for Change. In this study, using a sample of 356 household participants, with an average age of 56.0 yr. ( SD = 14.0), average income of $39,700 ( SD = $19,200), and average of 15.0 yr. of education ( SD = 2), from the Arkansas Household Research Panel, we conducted factor analyses. The results specific to our selected sample indicate that a four-factor model recognizing the two subdimensions of Sufficiency of Originality has a better fit than the original three-factor model.

Exploring the Dimensionality of the Originality Subscale of the Kirton Adaption-Innovation Inventory

The Kirton Adaption-Innovation Inventory, which is a widely used measure of innovative (as opposed to adaptive) cognitive individual style, is believed to have three dimensions: Sufficiency of Originality, Efficiency, and Rule/Group Conformity. Several studies have raised concerns regarding its construct validity, specifically with respect to the Sufficiency of Originality subscale. Within this subscale, exploratory factor analysis identified two distinct subdimensions, Idea Generation and Preference for Change. In this study, we used a sample of 356 household participants (with an average age of 56 yr., average income of $39,700, and average of 15 yr. of education) from the Arkansas Household Research Panel. We then employed Bollen and Grandjean's approach based on confirmatory factor analysis to assess whether there are actually two distinct subdimensions instead of one. Our study shows that within the Sufficiency of Originality subscale, there are indeed two distinct subdimensions, Idea Generation and Preference for Change. Further analyses indicate that dropping double-loaded items identified through exploratory factor analysis significantly improves the fit statistics. Also, allowing correlated errors for the measurement items that belong to the same subdimension can also significantly improve the overall fit of the model based on chi-square statistics.

Factor Analysis of the Kirton Adaption-Innovation Inventory

Kirton (7) reported that the factor structure of the Kirton Adaption-Innovation Inventory involved three factors which were labeled, (a) originality, (b) efficiency, and (c) rule-group conformity. The present study is a replication of Kirton's study, using a sample of 249 American college students and 40 university administrators. The items found in each of the three factors replicated Kirton's original analysis in items placed in each factor. This result and the similarity of factor loadings of the original sample (from England) and the present one supports the cross-cultural factor stability of the inventory.