EFFECTS OF COLLABORATION AND ISOMORPHIC MODELS ON TRANSFER: AN L2 WRITING INVESTIGATION

How can feedback become a productive resource for students? Much of the research investigating the role of feedback in second language (L2) writing has set out to find an answer to this question. Based on the principle that feedback is given to students as a means of providing useful information to improve their writing (Bitchener, 2008, 2009; Hanaoka & Izumi, 2012), the discussion on feedback includes the idea that learners will transfer knowledge from feedback to improve subsequent writing (Hyland, 1998; Storch & Wigglesworth, 2010). When learners apply feedback to their subsequent writing, they are using collected knowledge, which is the essence of learning transfer (Schwartz, Bransford, & Sears, 2005). Unfortunately, no method of writing feedback has been deemed the frontrunner for improving learner texts (Ferris & Roberts, 2001; Hyland & Hyland, 2006; Storch & Wigglesworth, 2010) or for helping learners transfer writing knowledge across writing situations (James, 2006a,b, 2008, 2009, 2010). While this outlook may seem bleak for writing instructors, recent research provides evidence for presenting learners with expert models as a fruitful way of offering feedback

Effects of collaboration and isomorphic models on transfer: An L2 English writing investigation

How can feedback become a productive resource for students? Much of the research investigating the role of feedback in second language (L2) writing has set out to find an answer to this question. In the preparation for future learning (PFL) framework, collaborative interactions engage learners in innovation tasks that push learners to reach beyond their individual dispositions and to build knowledge and understanding through each other’s contributions. Specifically, innovation tasks necessitate the use of prior knowledge in learner attempts to construct, or at a minimum offer, solutions to problems that are unfamiliar or unknown to them (Sears, 2006). A primary objective for having students innovate their initial solution is to prepare them to perceive and appreciate an expert solution. This approach is opposed to a more traditional efficiency model of learning tasks that embroils canonical solutions that can be rote and practice for mastery without generating novel ideas or solutions (Sears). The PFL framework posits that the innovation of ideas and solutions through dyadic interaction, with each other and with materials, can be a valuable catalyst for learning transfer of the deep structures of knowledge that apply across multiple contexts. Evidence of transfer for deep structures within the PFL framework can be seen by assessing learner performance on complex tasks (often called, target transfer tasks) that were not contained in the learning materials of a task, and not a focus of the individual or collaborative tasks (Schwartz et al.). This change in perspective from SPS to PFL has important implications for how language classrooms might design, employ, and measure effective learning tasks. This dissertation explored the usefulness of an expert model and a structured task in an L2 writing classroom. Two interaction levels—individual and collaborative—were examined for their facility of descriptive language related to data integration of graphical information from model feedback in a controlled pre/posttest experiment with international university students enrolled in an L2 English composition course. Two approaches to coding the data were taken. The first approach employed a coding scheme that provided a percentage of content overlap with the expert model—an indicator of factual recall and transfer. This was done by a line-by-line coding scheme (Glaser, 1978). The second approach considered how well the essays “fit” the expected data integrations provided in the model—an indicator of transfer of deep writing structure based on the relative balance of global versus local integrations. This was calculated with a Chi-square test of fit. The transfer of deep structures was further measured through an analysis of if students could identify a data interaction that did not exist in the model description. The results showed that learners in the dyad condition significantly outperformed learners in the individual and control conditions on content overlap and expected data integrations. The dyad condition also surpassed a truth-wins comparison, which provides a comparison of actual dyads to the theoretical pooling of knowledge individuals (Lorge & Solomon, 1955), and dyads were the only condition to include the target transfer item in their posttest revisions, indicating dyads were able to understanding complex data integrations in ways not available to learners in the individual and control conditions. (Abstract shortened by ProQuest.

The IVS data input to ITRF2014

2015ivs..data....1N - GFZ Data Services, Helmoltz Centre, Potsdam, GermanyVery Long Baseline Interferometry (VLBI) is a primary space-geodetic technique for determining precise coordinates on the Earth, for monitoring the variable Earth rotation and orientation with highest precision, and for deriving many other parameters of the Earth system. The International VLBI Service for Geodesy and Astrometry (IVS, http://ivscc.gsfc.nasa.gov/) is a service of the International Association of Geodesy (IAG) and the International Astronomical Union (IAU). The datasets published here are the results of individual Very Long Baseline Interferometry (VLBI) sessions in the form of normal equations in SINEX 2.0 format (http://www.iers.org/IERS/EN/Organization/AnalysisCoordinator/SinexFormat/sinex.html, the SINEX 2.0 description is attached as pdf) provided by IVS as the input for the next release of the International Terrestrial Reference System (ITRF): ITRF2014. This is a new version of the ITRF2008 release (Bockmann et al., 2009). For each session/ file, the normal equation systems contain elements for the coordinate components of all stations having participated in the respective session as well as for the Earth orientation parameters (x-pole, y-pole, UT1 and its time derivatives plus offset to the IAU2006 precession-nutation components dX, dY (https://www.iau.org/static/resolutions/IAU2006_Resol1.pdf). The terrestrial part is free of datum. The data sets are the result of a weighted combination of the input of several IVS Analysis Centers. The IVS contribution for ITRF2014 is described in Bachmann et al (2015), Schuh and Behrend (2012) provide a general overview on the VLBI method, details on the internal data handling can be found at Behrend (2013)