Akin House Curriculum Development and Living History Programming

This unit plan is comprised of a variety of inquiry-based lessons that explore the culture and way of life of the Native Americans who occupied New England. After studying the Akin house documents, materials, and narratives, I chose to focus my unit on the land and the people who came before the Akin family so that students will learn the long-view of our rich New England history

From print to Web: issues in re-purposing for an Open Resources Repository

The Open Educational Resources (OER) movement has gained rapid support for its goals of universal access to education. The UK Open University's contribution is OpenLearn, a project funded by the William and Flora Hewlett Foundation which, over the next two years, aims to re-purpose several thousand study-hours of existing learning materials for online delivery. The UK Open University has gained a hard-won reputation for the quality of its learning materials and integrated Supported Open Learning model. However, these materials are, as a rule, developed within the framework of long courses that typically require between 300 and 600 hours of study. Furthermore, many of the courses are 'interdisciplinary' in that they are developed by teams that include members associated with different faculties. The courses are, therefore, generally structured in terms of themes that run throughout the course and may span a variety of academic disciplines. Also, despite the breadth of knowledge brought into play when a course is developed, the courses normally reflect pedagogical and disciplinary assumptions and views that are prevalent in the UK. Based on the authors' experience of the OpenLearn project, this paper explores some of the key issues encountered when re-purposing resources. These issues include how to provide material not supported by tutorial guidance, the suitability of media components for conversion and the inter-relationship between the multimedia components. The paper will also briefly discuss the requirements for evaluation of the re-purposing process. The issues raised are potentially of relevance to other re-purposing initiatives

Team-Based Learning in Law

Used for over thirty years in a wide variety of fields, Team-Based Learning is a powerful teaching strategy that improves student learning. Used effectively, it enables students to actively engage in applying legal concepts in every class -- without sacrificing coverage. Because this teaching strategy has been used in classes with over 200 students, it also provides an efficient and affordable way to provide significant learning. Based on the principles of instructional design, Team-Based Learning has built-in student accountability, promotes independent student preparation, and fosters professional skills. This article provides an overview of Team-Based Learning, reasons to adopt this teaching strategy in light of Best Practices for Legal Education and the Carnegie and MacCrate reports, concrete methods to use Team-Based Learning in Law School, and ways to address challenges to this teaching strategy. Co-authors Sophie M. Sparrow and Margaret Sova McCabe provide examples from their years of teaching a variety of courses using Team-Based Learning

Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers