Weighing up Exercises on Phrasal Verbs: Retrieval Versus Trial-And-Error Practices

EFL textbooks and internet resources exhibit various formats and implementations of exercises on phrasal verbs. The experimental study reported here examines whether some of these might be more effective than others. EFL learners at a university in Japan were randomly assigned to four treatment groups. Two groups were presented first with phrasal verbs and their meaning before they were prompted to retrieve the particles from memory. The difference between these two retrieval groups was that one group studied and then retrieved items one at a time, while the other group studied and retrieved them in sets. The two other groups received the exercises as trial-and-error events, where participants were prompted to guess the particles and were subsequently provided with the correct response. One group was given immediate feedback on each item, while the other group tackled sets of 14 items before receiving feedback. The effectiveness of these exercise implementations was compared through an immediate and a 1-week delayed post-test. The best test scores were obtained when the exercises had served the purpose of retrieval, although this advantage shrank in the delayed test (where scores were poor regardless of treatment condition). On average 70% of the post-test errors produced by the learners who had tackled the exercises by trial-and-error were duplicates of incorrect responses they had supplied at the exercise stage, which indicates that corrective feedback was often ineffective

Ratings of the emotional valence and arousal of collocations and their constituent words: How can they be useful in L2 vocabulary research?

Supplementary Materials: Excel sheet showing focal vocabulary items and data These materials consist of conventional multiword expressions (MWEs) and various associated measures. All these MWEs are composed of two words. The most common structures are N-N, Adj-N, and V-_. There are essentially two sets of MWEs, although the MWEs within them are mostly the same. The first set figured in the study relating to emotional valence; the second set figured in the study relating to arousal. The whole MWE ratings of valence and arousal were crowd-sourced using Amazon Mechanical Turk (AMT) (https://www.mturk.com/), The corresponding ratings of the constituent words stem from a list compiled by Warriner, Kuperman, and Brysbaert (2013). The list itself is available at: http://crr.ugent.be/archives/1003. See the main article for further details. Associated with each set of MWEs is a smaller set of MWEs having both AMT ratings and ratings from Warriner et al. These matched, ‘overlapping’ ratings were used to assess the reliability of the new AMT ratings. In the main lists of MWEs these overlappers are given in red italics. Additionally, there are lists of regression residuals. The closer a residual is to zero, the more accurately MWE valence/arousal was predicted by the valence/arousal ratings of the mean of the constituent word ratings. Important abbreviations used in the spreadsheet are: AMT = Amazon Mechanical Turk; C-word = Constituent word; Geo.mean = Geometrical mean; Harm.mean = Harmonic mean; Most valenced = The C-word rating that is the furthest from 5 (i.e., neutral) either toward 1 or toward 9; SD = The standard deviation of the individual AMT ratings obtained for a given MWE; WKB = Warriner et al. (2013); NA = not available. NA was used in place of values (e.g., C-word ratings) that could not be found in the list of WKB. This abbreviation was chosen because the R functions used in the studies can handle datasets that include NA in place of a missing value. For instance, the appropriate calls in base R for calculating Spearman’s and Pearson’s correlations between the variables x and y, when NAs are present, are: cor(x, y, method = "s", use = "pairwise.complete.obs") and cor(x, y, method = "p", use = "pairwise.complete.obs"). The R functions of Wilcox (2012) that were used handle missing values even more automatically. For example, when Wilcox’s R functions are installed (https://dornsife.usc.edu/labs/rwilcox/software/), the call corb(x,y, corfun = spear, nboot = 20000) gives a bootstrap 95% confidence interval for Spearman’s correlation. The call for the median-based linear regression method that is mentioned would be: tsreg(x, y), where x and y are the independent and the dependent variables, respectively. Reference Warriner, A., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45, 1191–11207. https://doi.org/10.3758/s13428-012-0314-