1,733 research outputs found
Learning meets Assessment: On the relation between Item Response Theory and Bayesian Knowledge Tracing
Few models have been more ubiquitous in their respective fields than Bayesian
knowledge tracing and item response theory. Both of these models were developed
to analyze data on learners. However, the study designs that these models are
designed for differ; Bayesian knowledge tracing is designed to analyze
longitudinal data while item response theory is built for cross-sectional data.
This paper illustrates a fundamental connection between these two models.
Specifically, the stationary distribution of the latent variable and the
observed response variable in Bayesian knowledge Tracing are related to an item
response theory model. This connection between these two models highlights a
key missing component: the role of education in these models. A research agenda
is outlined which answers how to move forward with modeling learner data.
%Furthermore, recent advances in network psychometrics demonstrate how this
relationship can be exploited and generalized to a network model
Mobile Multimedia Recommendation in Smart Communities: A Survey
Due to the rapid growth of internet broadband access and proliferation of
modern mobile devices, various types of multimedia (e.g. text, images, audios
and videos) have become ubiquitously available anytime. Mobile device users
usually store and use multimedia contents based on their personal interests and
preferences. Mobile device challenges such as storage limitation have however
introduced the problem of mobile multimedia overload to users. In order to
tackle this problem, researchers have developed various techniques that
recommend multimedia for mobile users. In this survey paper, we examine the
importance of mobile multimedia recommendation systems from the perspective of
three smart communities, namely, mobile social learning, mobile event guide and
context-aware services. A cautious analysis of existing research reveals that
the implementation of proactive, sensor-based and hybrid recommender systems
can improve mobile multimedia recommendations. Nevertheless, there are still
challenges and open issues such as the incorporation of context and social
properties, which need to be tackled in order to generate accurate and
trustworthy mobile multimedia recommendations
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Challenges in context-aware mobile language learning: the MASELTOV approach
Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment
A Multi-User Perspective for Personalized Email Communities
Email classification and prioritization expert systems have the potential to
automatically group emails and users as communities based on their
communication patterns, which is one of the most tedious tasks. The exchange of
emails among users along with the time and content information determine the
pattern of communication. The intelligent systems extract these patterns from
an email corpus of single or all users and are limited to statistical analysis.
However, the email information revealed in those methods is either constricted
or widespread, i.e. single or all users respectively, which limits the
usability of the resultant communities. In contrast to extreme views of the
email information, we relax the aforementioned restrictions by considering a
subset of all users as multi-user information in an incremental way to extend
the personalization concept. Accordingly, we propose a multi-user personalized
email community detection method to discover the groupings of email users based
on their structural and semantic intimacy. We construct a social graph using
multi-user personalized emails. Subsequently, the social graph is uniquely
leveraged with expedient attributes, such as semantics, to identify user
communities through collaborative similarity measure. The multi-user
personalized communities, which are evaluated through different quality
measures, enable the email systems to filter spam or malicious emails and
suggest contacts while composing emails. The experimental results over two
randomly selected users from email network, as constrained information, unveil
partial interaction among 80% email users with 14% search space reduction where
we notice 25% improvement in the clustering coefficient.Comment: 46 pages, 14 images, Accepted in Expert Systems with Applications
Journa
Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data
Mobile phones are quickly becoming the primary source for social, behavioral,
and environmental sensing and data collection. Today's smartphones are equipped
with increasingly more sensors and accessible data types that enable the
collection of literally dozens of signals related to the phone, its user, and
its environment. A great deal of research effort in academia and industry is
put into mining this raw data for higher level sense-making, such as
understanding user context, inferring social networks, learning individual
features, predicting outcomes, and so on. In this work we investigate the
properties of learning and inference of real world data collected via mobile
phones over time. In particular, we look at the dynamic learning process over
time, and how the ability to predict individual parameters and social links is
incrementally enhanced with the accumulation of additional data. To do this, we
use the Friends and Family dataset, which contains rich data signals gathered
from the smartphones of 140 adult members of a young-family residential
community for over a year, and is one of the most comprehensive mobile phone
datasets gathered in academia to date. We develop several models that predict
social and individual properties from sensed mobile phone data, including
detection of life-partners, ethnicity, and whether a person is a student or
not. Then, for this set of diverse learning tasks, we investigate how the
prediction accuracy evolves over time, as new data is collected. Finally, based
on gained insights, we propose a method for advance prediction of the maximal
learning accuracy possible for the learning task at hand, based on an initial
set of measurements. This has practical implications, like informing the design
of mobile data collection campaigns, or evaluating analysis strategies.Comment: 10 page
A scenario based approach for dealing with challenges in a pervasive computing environment
With the surge in modern research focus towards Pervasive Computing, lot of
techniques and challenges needs to be addressed so as to effectively create
smart spaces and achieve miniaturization. In the process of scaling down to
compact devices, the real things to ponder upon are the Information Retrieval
challenges. In this work, we discuss the aspects of multimedia which makes
information access challenging. An Example Pattern Recognition scenario is
presented and the mathematical techniques that can be used to model uncertainty
are also presented for developing a system that can sense, compute and
communicate in a way that can make human life easy with smart objects assisting
from around his surroundings.Comment: 8 pages, IJCSA, Vol 4, No.2,April 201
Science of Digital Libraries(SciDL)
Our purpose is to ensure that people and institutions better manage information through digital libraries (DLs). Thus we address a fundamental human and social need, which is particularly urgent in the modern Information (and Knowledge) Age. Our goal is to significantly advance both the theory and state-of-theart of DLs (and other advanced information systems) - thoroughly validating our approach using highly visible testbeds. Our research objective is to leverage our formal, theory-based approach to the problems
of defining, understanding, modeling, building, personalizing, and evaluating DLs. We will construct models and tools based on that theory so organizations and individuals can easily create and maintain fully functional DLs, whose components can interoperate with corresponding components of related DLs. This research should be highly meritorious intellectually. We bring together a team of senior researchers with expertise in information retrieval, human-computer interaction, scenario-based design, personalization, and componentized system development and expect to make important contributions in each of those areas. Of crucial import, however, is that we will integrate our prior research and experience to achieve breakthrough advances in the field of DLs, regarding theory, methodology, systems, and evaluation. We will extend the 5S theory, which has identified five key dimensions or onstructs underlying effective DLs: Streams, Structures, Spaces, Scenarios, and Societies. We will use that theory to describe and develop metamodels, models, and systems, which can be tailored to disciplines and/or groups, as well as personalized. We will disseminate our findings as well as provide toolkits as open source software, encouraging wide use. We will validate our work using testbeds, ensuring broad impact. We will put powerful tools into the hands of digital librarians so they may easily plan and configure tailored systems, to support an extensible set of services, including publishing, discovery, searching, browsing, recommending, and access control, handling diverse types of collections, and varied genres and classes of digital objects. With these tools, end-users will for be able to design personal DLs.
Testbeds are crucial to validate scientific theories and will be thoroughly integrated into SciDL research and evaluation. We will focus on two application domains, which together should allow comprehensive validation and increase the significance of SciDL's impact on scholarly communities. One is education (through CITIDEL); the other is libraries (through DLA and OCKHAM). CITIDEL deals with content from publishers (e.g, ACM Digital Library), corporate research efforts e.g., CiteSeer), volunteer initiatives (e.g., DBLP, based on the database and logic rogramming literature), CS departments (e.g., NCSTRL, mostly technical reports), educational initiatives (e.g., Computer Science Teaching Center), and universities (e.g., theses and dissertations). DLA is a unit of the Virginia Tech library that virtually publishes scholarly communication such as faculty-edited journals and rare and unique resources including image collections and finding aids from Special Collections. The OCKHAM initiative, calling for simplicity in the library world, emphasizes a three-part solution: lightweightprotocols, component-based development, and open reference models. It provides a framework to research the deployment of the SciDL approach in libraries. Thus our choice of testbeds also will nsure that our research will have additional benefit to and impact on the fields of computing and library and information science, supporting transformations in how we learn and deal with information
Apps, Places and People: strategies, limitations and trade-offs in the physical and digital worlds
Cognition has been found to constrain several aspects of human behaviour,
such as the number of friends and the number of favourite places a person keeps
stable over time. This limitation has been empirically defined in the physical
and social spaces. But do people exhibit similar constraints in the digital
space? We address this question through the analysis of pseudonymised mobility
and mobile application (app) usage data of 400,000 individuals in a European
country for six months. Despite the enormous heterogeneity of apps usage, we
find that individuals exhibit a conserved capacity that limits the number of
applications they regularly use. Moreover, we find that this capacity steadily
decreases with age, as does the capacity in the physical space but with more
complex dynamics. Even though people might have the same capacity, applications
get added and removed over time. In this respect, we identify two profiles of
individuals: app keepers and explorers, which differ in their stable (keepers)
vs exploratory (explorers) behaviour regarding their use of mobile
applications. Finally, we show that the capacity of applications predicts
mobility capacity and vice-versa. By contrast, the behaviour of keepers and
explorers may considerably vary across the two domains. Our empirical findings
provide an intriguing picture linking human behaviour in the physical and
digital worlds which bridges research studies from Computer Science, Social
Physics and Computational Social Sciences
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