Barriers and enablers in integrating cognitive apprenticeship methods in a Web-based educational technology course for K-12 (primary and secondary) teacher education

The purpose of this study is to investigate the integration of a cognitive apprenticeship model into an educational technology Web‐based course for pre‐service primary through secondary teacher education. Specifically, this study presents an overview of methods, tools and media used to foster the integration of a cognitive apprenticeship model, and presents the types of barriers and enablers encountered when attempting to participate in a computer‐mediated cognitive apprenticeship. The methodological framework for this investigation is a qualitative case study of an educational technology course for pre‐service primary through secondary teacher education. The findings of this study reveal that various tools, methods and media were used to varying degrees of success to foster cognitive apprenticeship methods in a Web‐based learning environment. The goal of this study was to better understand the pragmatics, suitability, affordances and constraints of integrating cognitive apprenticeship methods in a Web‐based distance education course for teacher education

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201