Discovering indicators of successful collaboration using tense: Automated extraction of patterns in discourse

This paper describes a technique for locating indicators of success within the data collected from complex learning environments, proposing an application of e‐research to access learner processes and measure and track group progress. The technique combines automated extraction of tense and modality via parts‐of‐speech tagging with a visualisation of the timing and speaker for each utterance developed to code and analyse learner discourse, exploiting the results of previous, non‐automated analyses for validation. The work is developed using a dataset of interactions within a multi‐user virtual environment and extended to a more complex dataset of synchronous chat texts during a collaborative design task. This methodology extends natural language processing into computer‐based collaboration contexts, discovering the linguistic micro‐events that construct the larger phases of successful design‐based learning.     

Document vector representations for feature extraction in multi-stage document ranking

We consider a multi-stage retrieval architecture consisting of a fast, “cheap” candidate generation stage, a feature extraction stage, and a more “expensive” reranking stage using machine-learned models. In this context, feature extraction can be accomplished using a document vector index, a mapping from document ids to document representations. We consider alternative organizations of such a data structure for efficient feature extraction: design choices include how document terms are organized, how complex term proximity features are computed, and how these structures are compressed. In particular, we propose a novel document-adaptive hashing scheme for compactly encoding term ids. The impact of alternative designs on both feature extraction speed and memory footprint is experimentally evaluated. Overall, results show that our architecture is comparable in speed to using a traditional positional inverted index but requires less memory overall, and offers additional advantages in terms of flexibility.     

Bringing web‐enhanced courses to communities of learners in early childhood teacher education

Abstract

In our graduate programs in early childhood education, we model constructivist practice in the belief that teachers are better able to understand and implement constructivist principles having experienced them in their work. In this practice we attempt to be explicit about constructivist practice in our program and in elementary classrooms. As we examine and modify our constructivist pedagogy, one key question for teachers and ourselves emerges: What does a classroom based on constructivist pedagogy look like in early childhood (K‐2)? The goal of this research is a clearer understanding of constructivist pedagogy in the context of primary classrooms. The study was designed using qualitative methods in order to understand the experiences in classrooms guided by constructivist theory. The classroom participants are six teachers who graduated from a Master's degree program based on constructivist principles. Analysis of the six classrooms indicated 24 key elements of constructivist classrooms. A focused analysis of three constructivist teachers and their classroom supports these elements and indicates three broader characteristics: the important role of children, authentic and purposeful interactions among classroom participants, and engagement in academic activity. A vignette of a constructivist classroom is provided to present the essence of our findings while maintaining participants' views and voices.     

Developing the Media Zoo in Second Life

Second Life (SL), an immersive virtual environment, is increasingly being adopted in education for both formal and informal teaching and learning uses. This paper addresses the process of developing a demonstrator presence in SL, the potential of the system for formal teaching and some practical ideas on how to successfully integrate such emerging technologies into practice.     

Impact of Diagnosticity on the Adequacy of Models for Cognitive Diagnosis under a Linear Attribute Structure: A Simulation Study

Compared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM‐based data with a linear attribute structure. The study utilizes a procedure to make the IRM and CDM frameworks comparable and investigates how estimation accuracy is affected by test diagnosticity and the match between the true and fitted models. The study shows that comparable results can be obtained when highly diagnostic IRM data are retrofitted with CDM, and vice versa, retrofitting CDMs to IRM‐based data in some conditions can result in considerable examinee misclassification, and model fit indices provide limited indication of the accuracy of item parameter estimation and attribute classification.     

Adrian Peterson and the “Wussification of America”: Football and Myths of Masculinity

Twitter has become a unique site for making sense of public discussions about authority and morality in the United States. This article addresses these discussions by analyzing the elements of conservative discourse that claim the health of the nation is in decline because of a diminished emphasis on masculine leadership. There is a long tradition of conservative politicians in the United States warning against “soft” or “weak” figures of authority. Such conversations take place within a range of contexts, including the sport of football. As a means to engage hegemonic masculine authority from a critical perspective, this article investigates Twitter users’ various reactions to Adrian Peterson’s suspension due to his disciplining his child with a “switch.”     

The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education

Web 2.0 sociable technologies and social software are presented as enablers in health and health care, for organizations, clinicians, patients and laypersons. They include social networking services, collaborative filtering, social bookmarking, folksonomies, social search engines, file sharing and tagging, mashups, instant messaging, and online multi-player games. The more popular Web 2.0 applications in education, namely wikis, blogs and podcasts, are but the tip of the social software iceberg. Web 2.0 technologies represent a quite revolutionary way of managing and repurposing/remixing online information and knowledge repositories, including clinical and research information, in comparison with the traditional Web 1.0 model. The paper also offers a glimpse of future software, touching on Web 3.0 (the Semantic Web) and how it could be combined with Web 2.0 to produce the ultimate architecture of participation. Although the tools presented in this review look very promising and potentially fit for purpose in many health care applications and scenarios, careful thinking, testing and evaluation research are still needed in order to establish 'best practice models' for leveraging these emerging technologies to boost our teaching and learning productivity, foster stronger 'communities of practice', and support continuing medical education/professional development (CME/CPD) and patient education.     

Syntactic sentence compression in the biomedical domain: facilitating access to related articles

We explore a syntactic approach to sentence compression in the biomedical domain, grounded in the context of result presentation for related article search in the PubMed search engine. By automatically trimming inessential fragments of article titles, a system can effectively display more results in the same amount of space. Our implemented prototype operates by applying a sequence of syntactic trimming rules over the parse trees of article titles. Two separate studies were conducted using a corpus of manually compressed examples from MEDLINE: an automatic evaluation using Bleu and a summative evaluation involving human assessors. Experiments show that a syntactic approach to sentence compression is effective in the biomedical domain and that the presentation of compressed article titles supports accurate “interest judgments”, decisions by users as to whether an article is worth examining in more detail.