The potential applications of electronic corpora in language teaching and learning have received considerable attention in recent years, but their direct application to the classroom has not become part of mainstream practice. Indeed, the majority of published research in data-driven learning (DDL) focuses only on advanced learners using sophisticated equipment for complex language points. But ...

Corpus studies of native speaker and learner language have been influential in informing syllabus design and course content in foreign or second language (L2) teaching. Corpora can also be explored directly by the learner, in what Johns (1991) has called ―data-driven learning‖ (DDL). Such an approach is alleged to have many advantages, including fostering learner autonomy, increasing language a...

Carnegie Mellon is creating a Digital Dashboard for Learning (DDL) to enable faculty and students to tune their immediate actions (lectures, assignments, study) based on rapid feedback on student progress in learning. The DDL will provide faculty with a timely overview of student progress in blended learning environments supported by the Open Learning Initiative, Carnegie Mellon’s project to de...

To better understand the promising effects of data-driven learning (DDL) on language learning processes and outcomes, this study explored DDL learning events enabled by the Research Writing Tutor (RWT), a webbased platform that contains: an English language corpus annotated to enhance rhetorical input; a concordancer searchable for rhetorical functions; and an automated writing evaluation engin...

The recent adoption of deep learning for diverse applications has required scaling infrastructures both horizontally and vertically. As a result, efficient resource management for distributed deep learning (DDL) frameworks is becoming increasingly important. However, existing techniques for scaling DDL applications rely on general-purpose resource managers originally designed for data intensive...

Much traditional language teaching focuses on the learning and application of overt rules. While rules are intended to simplify learning, it often seems that learners have difficulty in applying them even if they understand and retain them. This is perhaps partly because the very concept of rules is to an extent artificial. However, one thing humans have evolved to be naturally good at is the d...

This paper presents a three-part methodology for identifying special-purposes words to teach in data-driven learning (DDL) vocabulary activities. Previous methods have focused on either important an English Specific Purposes (ESP) context or learner gaps—and little no research has addressed how determine whether specific are well suited teaching through DDL rather than more traditional, deducti...

Data-driven learning (DDL) involves learners exploring corpora to discover language rather than ‘being taught’. This is claimed to have a number of advantages, and while empirical evidence to date is encouraging, it is less conclusive than might be hoped – partly, it is argued, as it tends to focus on short-term goals by testing knowledge and retention of selected language items covered. It may...

Insights from corpus linguistics (CL) have informed language learning and materials design, among many other areas. An important nexus between CL is the use of Data-Driven Learning (DDL), which draws on data in classroom brings opportunities for inductive discovery. Within ethos DDL, learners are encouraged to discover patterns and, so doing, foster more complex cognitive processes such as maki...

This paper outlines the theoretical background of data-driven learning (DDL) and its hands-on application in French Language Teaching, as well didactic potential using text corpora functionalities to extract collocations. The second part focuses on analysis a project conducted among students Romance Philology. Its objective was corpus selected terms their Polish equivalents theme social issues....