Face-to-face and online interactions - is a task a task?

This study contrasts two different ways of analysing interaction and participation in language learning tutorials: Social network analysis of frequency and QSR analysis of type of interaction. One task from three German beginners' language tutorials (one delivered face-to-face, the other two online) is analysed. A description of the background and method of the study is provided together with some examples of the findings. As this is work in progress, only tentative conclusions can be provided at this stage

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

ASSESSING THE EFFECT OF INTERACTIVITY ON VIRTUAL REALITY SECOND LANGUAGE LEARNING

Virtual Reality (VR) being used as a helpful tool in language education is widely supported by the current literature. It can provide a variety of stimulating scenarios that keep learner engagement high. The use of VR for language learning is a research area that has shown promise in recent years. This makes it necessary for further research to be conducted in the field to determine ways to maximize its potential. This thesis aims to determine if the level of interactivity present in a VR Language Learning Application is a factor that will impact a user\u27s capability to successfully learn a second language. Also, to discover an optimal level of interactivity needed to foster successful language learning in VR can be identified when dealing with subjects with different age, gender, and previous VR experience. To satisfy these aims, 3 versions of a VR Language Learning Application were created with varying levels of interactivity. Data collected from participants of this study were used to test efficiency of the VR language learning application. Results of this analysis determine that the level of interactivity present in a VR Language Learning Application is a factor that will impact a user\u27s capability to successfully learn a second language. Also, according to the results, all interactivity levels are useful but no interactivity is the most optimal for successful language learning in VR

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Evaluation of an anthropomorphic user interface in a travel reservation context and affordances

This paper describes an experiment and its results concerning research that has been going on for a number ofyears in the area of anthropomorphic user interface feedback. The main aims of the research have been to examine theeffectiveness and user satisfaction of anthropomorphic feedback in various domains. The results are of use to all interactivesystems designers, particularly when dealing with issues of user interface feedback design. There is currently somedisagreement amongst computer scientists concerning the suitability of such types of feedback. This research is working toresolve this disagreement. The experiment detailed, concerns the specific software domain of Online Factual Delivery in thespecific context of online hotel bookings. Anthropomorphic feedback was compared against an equivalent non-anthropomorphicfeedback. Statistically significant results were obtained suggesting that the non-anthropomorphic feedback was more effective.The results for user satisfaction were however less clear. The results obtained are compared with previous research. Thissuggests that the observed results could be due to the issue of differing domains yielding different results. However the resultsmay also be due to the affordances at the interface being more facilitated in the non-anthropomorphic feedback

The Tutor's Role

This chapter addresses three questions about being an effective online tutor: 1. Why do we still think that online tutoring can principally draw its basis from face-to-face group processes and dynamics or traditional pedagogy? 2. Does the literature tell us anything more than we would make as an intelligent guess? 3. Do we really know what an ‘effective’ online tutor would be doing? The OTiS participants have gone some way to answering these questions, through the presentation and discussion of their own online tutoring experiences. Literature in this area is still limited, and suffers from the need for timeliness of publication to be useful. Intelligent guesses are all very well, but much better as a source of information for online tutors are the reflections and documented experiences of practitioners. These experiences reveal that face-to-face pedagogy has some elements to offer the online tutor, but that there are key differences and there is a need to examine the processes and dynamics of online learning to inform online tutoring

A Study of Feedback Strategies in Foreign Language Classrooms and Tutorials with Implications for Intelligent Computer-Assisted Language Learning Systems

This paper presents two new corpus-based studies of feedback in the domain of teaching Spanish as a foreign language, concentrating on the type and frequency of different feedback moves, as well as what happens in the moves that follow the feedback. In particular, as well as looking at positive feedback, it concentrates on two general kinds of negative feedback strategies: (1) Giving-Answer Strategies (GAS), where the teacher directly gives the desired target form or indicates the location of the error, and (2) Prompting-Answer Strategies (PAS), where the teacher pushes the student less directly to notice and repair their own error. Investigating the GAS/PAS distinction sheds light on the relative importance for Intelligent Computer-Assisted Language Learning (ICALL) systems of knowledge construction from interaction, which many believe is crucial for effective learning from ITS. The main finding here is that, although GAS occur more frequently than PAS in both corpora, it is the PAS that are more effective, in terms of eliciting explicit repairs by the students. The first study takes place in a classroom context, whereas the second, smaller, study looks at tutorial interactions. This makes it possible to investigate the extent to which the mode of interaction influences the frequency and effectiveness of feedback moves, as well as to look at how concepts such as “wait time” are relevant to explain moves that are ineffective. The paper concludes by using these results to make recommendations about how to choose appropriate feedback moves in ICALL systems