Editorial: Introduction to the special issue on conducting research syntheses on individual differences in SLA

As systematic research syntheses and meta-analytic studies are becoming more prominent in the social sciences, especially in the fields of psychology and ed-ucation, it appears that applied linguists have also started to follow suit (In’nami et al., 2019). One of the main reasons for this is that abundant knowledge has accumulated through the years about second and foreign lan-guage (L2) learning and teaching, making the time ripe to systematically syn-thesize the research findings in order to draw further conclusions and identify paths future studies could take. This is also true for the subfield of individual differences (IDs) research within applied linguistics, where proliferation in the number of studies focusing on individual learner differences with respect to a large variety of issues has been witnessed in the past decades. Hence, we saw it timely to compile a special issue on research synthesis in the subfield of IDs in L2 learning. We formulated the following aims to guide our venture: First of all, we intend to inform scholars of the nature and utility of research syntheses in our field. Second, we hope that the articles included in the special issue would serve as examples for researchers wishing to embark on conducting similar studies. Our third and not negligible aim was to see what tendencies regarding particular individual differences can be outlined based on previous research results. In order for the readers to make the most of these articles, in this editorial introduction we would like to articulate how we see the role of research synthesis in general and meta-analysis in particular in our field. To this end, we will offer relevant definitions and a short discussion on their utility. We will then move on to outline very generic guidelines for conducting sys-tematic research syntheses, and, finally, we will summarize the studies includ-ed in the volume and their contribution to the field of research on IDs.As systematic research syntheses and meta-analytic studies are becoming more prominent in the social sciences, especially in the fields of psychology and ed-ucation, it appears that applied linguists have also started to follow suit (In’nami et al., 2019). One of the main reasons for this is that abundant knowledge has accumulated through the years about second and foreign lan-guage (L2) learning and teaching, making the time ripe to systematically syn-thesize the research findings in order to draw further conclusions and identify paths future studies could take. This is also true for the subfield of individual differences (IDs) research within applied linguistics, where proliferation in the number of studies focusing on individual learner differences with respect to a large variety of issues has been witnessed in the past decades. Hence, we saw it timely to compile a special issue on research synthesis in the subfield of IDs in L2 learning. We formulated the following aims to guide our venture: First of all, we intend to inform scholars of the nature and utility of research syntheses in our field. Second, we hope that the articles included in the special issue would serve as examples for researchers wishing to embark on conducting similar studies. Our third and not negligible aim was to see what tendencies regarding particular individual differences can be outlined based on previous research results. In order for the readers to make the most of these articles, in this editorial introduction we would like to articulate how we see the role of research synthesis in general and meta-analysis in particular in our field. To this end, we will offer relevant definitions and a short discussion on their utility. We will then move on to outline very generic guidelines for conducting sys-tematic research syntheses, and, finally, we will summarize the studies includ-ed in the volume and their contribution to the field of research on IDs

Evaluative conditioning: recent developments and future directions

CONTINGENCY AWARENESS; ATTITUDE-CHANGE; EFFECTS DEPEND; IMPLICIT; ASSOCIATION; DISLIKES; VALENCE; LIKES; US; MISATTRIBUTION; Automaticity; Evaluative conditioning; Functional definition; Mental process theorie

Extending, broadening and rethinking existing research on transfer of training

Research on transfer of training has a long history, with thousands of empirical studies since the 1950s investigating whether, and under which conditions, knowledge and skills acquired during training are subsequently used in the work environment (see reviews by Baldwin and Ford, 1988, Blume et al., 2010 and Burke and Hutchins, 2007). The generation of such an abundance of research can be linked to organisations’ fundamental and ongoing concern to ensure that their employees possess the necessary knowledge and skills from their employer to maintain a competitive advantage and thrive economically. Training and development is, however, extremely costly to organisations, which has created the need to determine the effectiveness of training, and the conditions under which transfer of training is optimal. A recent overview of “what really matters” for successful transfer of training (Grossman & Salas, 2011), aimed at a training and development readership, summarized the most influential variables emerging from this vast body of research. Based on the expectation that the list of factors which may contribute to influence transfer could always be extended and that it would be impractical to incorporate every single factor in research designs, the authors recommended a shift in future research towards deeper investigations of the conditions under which selected variables are more or less influential in their relationship with training. This Special Issue contributes to this important research agenda and extends it further through the inclusion of a diverse collection of conceptual contributions and reviews, from several scientific disciplines, a plurality of theoretical perspectives and a range of methodological approaches. Expanding the theoretical grounding underpinning empirical work on transfer of training and scrutinizing existing conceptualizations of the notion of transfer is timely in light of widespread concerns from organisations about minimal return on investment in training, and repeated evidence in the transfer of training literature of an enduring “transfer problem”. The aim of this article is to explore the value of extending, broadening and rethinking existing research on transfer of training. The benefits of extending research on transfer of training is considered first, through examining how the contributions of this Special Issue add to the existing literature on transfer of training, and the implications of the new insights for addressing the “transfer problem”. How transfer of training research could be broadened, thus enriched, through incorporating ideas from recent literature on transfer of learning is considered next. Finally, proposals to rethink transfer as boundary crossing from an activity theory perspective are scrutinized for their potential to better understand the learning that takes place at the boundaries of training and work environments. The article concludes by elaborating on the conceptual value of a refocus on ‘transfer of learning from training’ within a perspective of adaptive learning, and a call for cross-fertilisation with the extensive theory grounded literatures on transfer of learning and boundary crossing

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts

Learnt Topology Gating Artificial Neural Networks

This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed Learnt Topology Gating Artificial Neural Networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are Artificial Neural Networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method

Reuse through rapid development

The general issue of reuse of digital resources, called Learning Objects (LOs), in education is discussed here. Ideas are drawn from software engineering which has long grappled with the reuse problem. Arguments are presented for rapid development methodologies and a corresponding method for generation of online mathematics question banks is described

Using styles for more effective learning in multicultural and e-learning environments

Purpose – This Special Issue contains selected papers from the thirteenth annual European Learning Styles Information Network (ELSIN) conference held in Ghent, Belgium in June 2008. One of the key aims of ELSIN is to promote understanding of individual learning and cognitive differences through the dissemination of international multidisciplinary research about learning and cognitive styles and strategies of learning and thinking. Design/methodology/approach – Three papers within this special issue consider how style differences can inform the development of e-learning opportunities to enhance the learning of all (Vigentini; Kyprianidou, Demetriadis, Pombortsis and Karatasios; Zhu, Valcke and Schellens). The influence of culture on learning is also raised in the paper of Zhu and colleagues and those of Sulimma and Eaves which both focus more directly on cultural influences on style, learning and teaching. Findings – A number of key themes permeate the studies included in this Special Edition such as: the nature of styles; the intrinsic difficulty of isolating style variables from other variables impacting on performance; inherent difficulties in choosing the most appropriate style measures; the potential of e-learning to attend to individual learning differences; the role of culture in informing attitudes and access to learning; the development of constructivist learning environments to support learning through an understanding of individual differences; and most importantly how one can apply such insights about individual differences to inform and enhance instruction. Originality/value – The papers in this Special Issue contribute to enhanced knowledge about the value of style differences to design constructive learning environments in multicultural and e-learning contexts

Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining

The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining process. In addition it abandons the so called black-box approach to algorithm description followed in meta-learning. Now in addition to the datasets, algorithms also have descriptors, workflows as well. For the latter two these descriptions are semantic, describing properties of the algorithms. With the availability of descriptors both for datasets and data mining workflows the traditional modelling techniques followed in meta-learning, typically based on classification and regression algorithms, are no longer appropriate. Instead we are faced with a problem the nature of which is much more similar to the problems that appear in recommendation systems. The most important meta-mining requirements are that suggestions should use only datasets and workflows descriptors and the cold-start problem, e.g. providing workflow suggestions for new datasets. In this paper we take a different view on the meta-mining modelling problem and treat it as a recommender problem. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulations is general enough so that it can be applied on problems with similar requirements