58,806 research outputs found
Construction and abstraction: contrasting methods of supporting model building in learning science
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A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
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Nursing Home Implementation of Health Information Technology: Review of the Literature Finds Inadequate Investment in Preparation, Infrastructure, and Training.
Health information technology (HIT) is increasingly adopted by nursing homes to improve safety, quality of care, and staff productivity. We examined processes of HIT implementation in nursing homes, impact on the nursing home workforce, and related evidence on quality of care. We conducted a literature review that yielded 46 research articles on nursing homes' implementation of HIT. To provide additional contemporary context to our findings from the literature review, we also conducted semistructured interviews and small focus groups of nursing home staff (n = 15) in the United States. We found that nursing homes often do not employ a systematic process for HIT implementation, lack necessary technology support and infrastructure such as wireless connectivity, and underinvest in staff training, both for current and new hires. We found mixed evidence on whether HIT affects staff productivity and no evidence that HIT increases staff turnover. We found modest evidence that HIT may foster teamwork and communication. We found no evidence that the impact of HIT on staff or workflows improves quality of care or resident health outcomes. Without initial investment in implementation and training of their workforce, nursing homes are unlikely to realize potential HIT-related gains in productivity and quality of care. Policy makers should consider creating greater incentives for preparation, infrastructure, and training, with greater engagement of nursing home staff in design and implementation
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Validating simulated interaction for retrieval evaluation
A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations
Developing and Researching PhET simulations for Teaching Quantum Mechanics
Quantum mechanics is difficult to learn because it is counterintuitive, hard
to visualize, mathematically challenging, and abstract. The Physics Education
Technology (PhET) Project, known for its interactive computer simulations for
teaching and learning physics, now includes 18 simulations on quantum mechanics
designed to improve learning of this difficult subject. Our simulations include
several key features to help students build mental models and intuitions about
quantum mechanics: visual representations of abstract concepts and microscopic
processes that cannot be directly observed, interactive environments that
directly couple students' actions to animations, connections to everyday life,
and efficient calculations so students can focus on the concepts rather than
the math. Like all PhET simulations, these are developed using the results of
education research and feedback from educators, and are tested in student
interviews and classroom studies. This article provides an overview of the PhET
quantum simulations and their development. We also describe research
demonstrating their effectiveness and share some insights about student
thinking that we have gained from our research on quantum simulations.Comment: accepted by American Journal of Physics; v2 includes an additional
study, more explanation of research behind claims, clearer wording, and more
reference
An empirical study of the “prototype walkthrough”: a studio-based activity for HCI education
For over a century, studio-based instruction has served as an effective pedagogical model in architecture and fine arts education. Because of its design orientation, human-computer interaction (HCI) education is an excellent venue for studio-based instruction. In an HCI course, we have been exploring a studio-based learning activity called the prototype walkthrough, in which a student project team simulates its evolving user interface prototype while a student audience member acts as a test user. The audience is encouraged to ask questions and provide feedback. We have observed that prototype walkthroughs create excellent conditions for learning about user interface design. In order to better understand the educational value of the activity, we performed a content analysis of a video corpus of 16 prototype walkthroughs held in two HCI courses. We found that the prototype walkthrough discussions were dominated by relevant design issues. Moreover, mirroring the justification behavior of the expert instructor, students justified over 80 percent of their design statements and critiques, with nearly one-quarter of those justifications having a theoretical or empirical basis. Our findings suggest that PWs provide valuable opportunities for students to actively learn HCI design by participating in authentic practice, and provide insight into how such opportunities can be best promoted
Children's creative collaboration during a computer-based music task
The purpose of this study was to identify and analyse specific instances of transactive communication as children engaged in a paired melody writing task using a computer-based composing environment. Transactive communication has been identified as one of the features of general collaborative engagement that is most helpful in an educational sense, and which makes collaborative learning an important tool for learning and teaching. The paper reports the results of an empirical study in which a group of 10 and 11 year olds worked in pairs to compose short melodies using computers. Analysis of between-pupil dialogue suggested that levels of transactive communication varied between pairs, and also within pairs as pupils took on different roles at the computer. Factors of individual difference, such as musical expertise or whether the pair were friends, did not appear to have a significant influence on the extent of, or nature or, transactive communication
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