| Personalized Network Updates: Increasing Social Interactions and Contributions in Social Networks | | BIBA | Full-Text | 1-13 | |
| Shlomo Berkovsky; Jill Freyne; Gregory Smith | |||
| Social networking systems originally emerged as tools for keeping up with the daily lives of friends and strangers. They have established themselves as valuable resources and means to satisfy information needs. The challenge with information seeking through social networks is that their immense success and popularity is also a weakness. The data deluge facing users has surpassed comfortably managed levels and can impact on the quality and relevance of the information consumed. We developed a personalized model for predicting the relevance of news feed items, in order to facilitate personalized feeds. Results of a live analysis show that our approach successfully identifies and promotes relevant feed items, with the knock-on effects of increasing interaction between users and the contribution of user generated content. | |||
| Realistic Simulation of Museum Visitors' Movements as a Tool for Assessing Sensor-Based User Models | | BIBA | Full-Text | 14-25 | |
| Fabian Bohnert; Ingrid Zukerman; David W. Albrecht | |||
| We present a realistic simulation framework to examine the impact of sensor noise on the performance of user models in the museum domain. Our contributions are (1) models to simulate noisy visit trajectories as time-stamped sequences of (x,y) positional coordinates which reflect walking and hovering behaviour; (2) a discriminative inference model that distinguishes between hovering and walking on the basis of (simulated) noisy sensor observations; (3) a model that infers viewed exhibits from hovering coordinates; and (4) a model that predicts the next exhibit on the basis of inferred (rather than known) viewed exhibits. Our staged evaluation assesses the effect of these models (in combination with sensor noise) on inferential and predictive performance, thus shedding light on the reliability attributed to inferences drawn from sensor observations. | |||
| GECKOmmender: Personalised Theme and Tour Recommendations for Museums | | BIBA | Full-Text | 26-37 | |
| Fabian Bohnert; Ingrid Zukerman; Junaidy Laures | |||
| We present Geckommender, a mobile system for personalised theme and tour recommendations in museums, based on a digital site-map representation. Star ratings provided by visitors for seen exhibits are used to predict ratings for unvisited exhibits. The predicted ratings in turn form the basis for recommendations. These recommendations are presented in one of three display modes: StarMap-- stars on the site map, HeatMap-- colours from green to red that indicate the interestingness of exhibits (from interesting to not interesting respectively), and TourPlann -- directed personalised tours through the museum. Geckommender was evaluated in a field study at Melbourne Museum (Melbourne, Australia). Our results show that (1) most participants enjoyed Geckommender, (2) Geckommender's recommendations often reflected the participants' personal interests, and (3) HeatMap was the most popular display mode. | |||
| Property-Based Interest Propagation in Ontology-Based User Model | | BIBA | Full-Text | 38-50 | |
| Federica Cena; Silvia Likavec; Francesco Osborne | |||
| We present an approach for propagation of user interests in ontology-based user models taking into account the properties declared for the concepts in the ontology. Starting from initial user feedback on an object, we calculate user interest in this particular object and its properties and further propagate user interest to other objects in the ontology, similar or related to the initial object. The similarity and relatedness of objects depends on the number of properties they have in common and their corresponding values. The approach we propose can support finer recommendation modalities, considering the user interest in the objects, as well as in singular properties of objects in the recommendation process. We tested our approach for interest propagation with a real adaptive application and obtained an improvement with respect to IS-A-propagation of interest values. | |||
| EEG Estimates of Engagement and Cognitive Workload Predict Math Problem Solving Outcomes | | BIBAK | Full-Text | 51-62 | |
| Federico Cirett Galán; Carole R. Beal | |||
| The study goal was to evaluate whether Electroencephalography (EEG)
estimates of attention and cognitive workload captured as students solved math
problems could be used to predict success or failure at solving the problems.
Students (N = 16) solved a series of SAT math problems while wearing an EEG
headset that generated estimates of sustained attention and cognitive workload
each second. Students also reported on their level of frustration and the
perceived difficulty of each problem. Results from a Support Vector Machine
(SVM) training indicated that problem outcomes could be correctly predicted
from the combination of attention and workload signals at rates better than
chance. EEG data were also correlated with students' self-report of problem
difficulty. Findings suggest that relatively non-intrusive EEG technologies
could be used to improve the efficacy of tutoring systems. Keywords: Machine Learning; Electroencephalography; Math; Intelligent Tutoring
Systems; physiology; behavior | |||
| Preference Relation Based Matrix Factorization for Recommender Systems | | BIBAK | Full-Text | 63-75 | |
| Maunendra Sankar Desarkar; Roopam Saxena; Sudeshna Sarkar | |||
| Users in recommender systems often express their opinions about different
items by rating the items on a fixed rating scale. The rating information
provided by the users is used by the recommender systems to generate
personalized recommendations for them. Few recent research work on rating based
recommender systems advocate the use of preference relations instead of
absolute ratings in order to produce better recommendations. Use of preference
relations for neighborhood based collaborative recommendation has been looked
upon in recent literature. On the other hand, Matrix Factorization algorithms
have been shown to perform well for recommender systems, specially when the
data is sparse. In this work, we propose a matrix factorization based
collaborative recommendation algorithm that considers preference relations.
Experimental results show that the proposed method is able to achieve better
recommendation accuracy over the compared baseline methods. Keywords: User feedback; Preference relations; Latent factors; Matrix Factorization | |||
| A Framework for Modeling Trustworthiness of Users in Mobile Vehicular Ad-Hoc Networks and Its Validation through Simulated Traffic Flow | | BIBA | Full-Text | 76-87 | |
| John Finnson; Jie Zhang; Thomas Tran; Umar Farooq Minhas; Robin Cohen | |||
| In this paper, we present an approach for modeling user trustworthiness when traffic information is exchanged between vehicles in transportation environments. Our multi-faceted approach to trust modeling combines priority-based, role-based and experience-based trust, integrated with a majority consensus model influenced by time and location, for effective route planning. The proposed representation for the user model is outlined in detail (integrating ontological and propositional elements) and the algorithm for updating trust values is presented as well. This trust modeling framework is validated in detail through an extensive simulation testbed that models vehicle route planning. We are able to show decreased average path time for vehicles when all facets of our trust model are employed in unison. Included is an interesting confirmation of the value of distinguishing direct and indirect observations of users. | |||
| A Comparative Study of Users' Microblogging Behavior on Sina Weibo and Twitter | | BIBAK | Full-Text | 88-101 | |
| Qi Gao; Fabian Abel; Geert-Jan Houben; Yong Yu | |||
| In this article, we analyze and compare user behavior on two different
microblogging platforms: (1) Sina Weibo which is the most popular microblogging
service in China and (2) Twitter. Such a comparison has not been done before at
this scale and is therefore essential for understanding user behavior on
microblogging services. In our study, we analyze more than 40 million
microblogging activities and investigate microblogging behavior from different
angles. We (i) analyze how people access microblogs and (ii) compare the
writing style of Sina Weibo and Twitter users by analyzing textual features of
microposts. Based on semantics and sentiments that our user modeling framework
extracts from English and Chinese posts, we study and compare (iii) the topics
and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore,
(v) we investigate the temporal dynamics of the microblogging behavior such as
the drift of user interests over time.
Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research. Keywords: user modeling; microblogging; comparative usage analysis | |||
| Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy | | BIBAK | Full-Text | 102-113 | |
| Yue Gong; Joseph E. Beck; Carolina Ruiz | |||
| In this paper, we propose a general approach to improve student modeling
predictive accuracy. The approach was designed based on the assumption that
student performance is sampled from multiple, rather than only one,
distribution and thus should be modeled by multiple classification models. We
applied k-means to identify student performances sampled from those multiple
distributions, using no additional features beyond binary correctness of
student responses. We trained a separate classification model for each
distribution and applied the learned models to unseen students to evaluate our
approach. The results showed that compared to the base classifier, our proposed
approach is able to improve predictive accuracy: 4.3% absolute improvement in
R² and 0.03 absolute improvement in AUC, which are not trivial
improvements considering the current state of the art in student modeling. Keywords: student modeling; predictive accuracy; multiple classifiers; multiple
distributions of student performances; performance factors analysis | |||
| A Simple But Effective Method to Incorporate Trusted Neighbors in Recommender Systems | | BIBA | Full-Text | 114-125 | |
| Guibing Guo; Jie Zhang; Daniel Thalmann | |||
| Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations. | |||
| Exploring Gaze Data for Determining User Learning with an Interactive Simulation | | BIBAK | Full-Text | 126-138 | |
| Samad Kardan; Cristina Conati | |||
| This paper explores the value of eye-tracking data to assess user learning
with interactive simulations (IS). Our long-term goal is to use this data in
user models that can generate adaptive support for students who do not learn
well with these types of unstructured learning environments. We collected gaze
data from users interacting with the CSP applet, an IS for constraint
satisfaction problems. Two classifiers built upon this data achieved good
accuracy in discriminating between students who learn well from the CSP applet
and students who do not, providing evidence that gaze data can be a valuable
source of information for building user modes for IS. Keywords: Eye-tacking; Eye Movement Data; Interactive Simulation Environments; User
Classification; User Modeling | |||
| Studies to Determine User Requirements Regarding In-Home Monitoring Systems | | BIBA | Full-Text | 139-150 | |
| Melanie Larizza; Ingrid Zukerman; Fabian Bohnert; R. Andrew Russell; Lucy Busija | |||
| The ageing of the world population is leading to an increased number of elderly people remaining in their homes, requiring different levels of care. MIA is a user-centric project aimed at monitoring elderly people in order to help them remain safely in their homes, where the design of the system is informed by the requirements of the stakeholders. In this paper, we present the results of two user studies that ascertain the views of elderly people and their informal carers regarding the acceptability and benefits of in-home monitoring technologies: (1) concept mapping coupled with brainstorming sessions, and (2) questionnaires. We then discuss how these requirements affect the design of our monitoring system. | |||
| Improving Tensor Based Recommenders with Clustering | | BIBAK | Full-Text | 151-163 | |
| Martin Leginus; Peter Dolog; Valdas Zemaitis | |||
| Social tagging systems (STS) model three types of entities (i.e.
tag-user-item) and relationships between them are encoded into a 3-order
tensor. Latent relationships and patterns can be discovered by applying tensor
factorization techniques like Higher Order Singular Value Decomposition
(HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse
data that restricts factorization techniques to detect latent relations and
also significantly slows down the process of a factorization. We propose to
reduce tag space by exploiting clustering techniques so that the quality of the
recommendations and execution time are improved and memory requirements are
decreased. The clustering is motivated by the fact that many tags in a tag
space are semantically similar thus the tags can be grouped. Finally, promising
experimental results are presented. Keywords: tensor factorization; HOSVD; clustering | |||
| Models of User Engagement | | BIBAK | Full-Text | 164-175 | |
| Janette Lehmann; Mounia Lalmas; Elad Yom-Tov; Georges Dupret | |||
| Our research goal is to provide a better understanding of how users engage
with online services, and how to measure this engagement. We should not speak
of one main approach to measure user engagement -- e.g. through one fixed set
of metrics -- because engagement depends on the online services at hand.
Instead, we should be talking of models of user engagement. As a first step, we
analysed a number of online services, and show that it is possible to derive
effectively simple models of user engagement, for example, accounting for user
types and temporal aspects. This paper provides initial insights into
engagement patterns, allowing for a better understanding of the important
characteristics of how users repeatedly interact with a service or group of
services. Keywords: diversity of user engagement; models; user type; temporal aspect | |||
| Improving the Performance of Unit Critiquing | | BIBAK | Full-Text | 176-187 | |
| Monika Mandl; Alexander Felfernig | |||
| Conversational recommender systems allow users to learn and adapt their
preferences according to concrete examples. Critiquing systems support such a
conversational interaction style. Especially unit critiques offer a low cost
feedback strategy for users in terms of the needed cognitive effort. In this
paper we present an extension of the experience-based unit critiquing
algorithm. The development of our new approach, which we call nearest neighbor
compatibility critiquing, was aimed at increasing the efficiency of unit
critiquing. We combine our new approach with existing critiquing strategies to
ensemble-based variations and present the results of an empirical study that
aimed at comparing the recommendation efficiency (in terms of the number of
critiquing cycles) of ensemble-based solutions with individual critiquing
algorithms. Keywords: Recommender Systems; Conversational Recommendation; Critiquing Systems | |||
| Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides | | BIBAK | Full-Text | 188-199 | |
| Cataldo Musto; Fedelucio Narducci; Pasquale Lops; Giovanni Semeraro; Marco de Gemmis | |||
| Personalized electronic program guides help users overcome information
overload in the TV and video domain by exploiting recommender systems that
automatically compile lists of novel and diverse video assets, based on
implicitly or explicitly defined user preferences. In this context, we assume
that user preferences can be specified by program genres (documentary, sports,
...) and that an asset can be labeled by one or more program genres, thus
allowing an initial and coarse preselection of potentially interesting assets.
As these assets may come from various sources, program genre labels may not be
consistent among these sources, or not even be given at all, while we assume
that each asset has a possibly short textual description. In this paper, we
tackle this problem by considering whether those textual descriptions can be
effectively used to automatically retrieve the most related TV shows for a
specific program genre. More specifically, we compare a statistical approach
called logistic regression with an enhanced version of the commonly used vector
space model, called random indexing, where the latter is extended by means of a
negation operator based on quantum logic. We also apply a new feature
generation technique based on explicit semantic analysis for enriching the
textual description associated to a TV show with additional features extracted
from Wikipedia. Keywords: Personalized Electronic Program Guides; Explicit Semantic Analysis; Vector
Space Model; Random Indexing; Logistic Regression | |||
| Attention and Selection in Online Choice Tasks | | BIBA | Full-Text | 200-211 | |
| Vidhya Navalpakkam; Ravi Kumar; Lihong Li; D. Sivakumar | |||
| The task of selecting one among several items in a visual display is extremely common in daily life and is executed billions of times every day on the Web. Attention is vital for selection, but the end-to-end process of what draws and sustains attention, and how that influences selection, remains poorly understood. We study this in a complex multi-item selection setting, where participants selected one among eight news articles presented in a grid layout on a screen. By varying the position, saliency, and topic of the news items, we identify the relative importance of these visual and semantic factors in attention and selection. We present a simple model of attention that predicts many key features such as attention shifts and dwell time per item. Potential applications of our findings include optimizing visual displays to drive user attention. | |||
| Investigating Explanations to Justify Choice | | BIBAK | Full-Text | 212-224 | |
| Ingrid Nunes; Simon Miles; Michael Luck; Carlos J. P. de Lucena | |||
| Many different forms of explanation have been proposed for justifying
decisions made by automated systems. However, there is no consensus on what
constitutes a good explanation, or what information these explanations should
include. In this paper, we present the results of a study into how people
justify their decisions. Analysis of our results allowed us to extract the
forms of explanation adopted by users to justify choices, and the situations in
which these forms are used. The analysis led to the development of guidelines
and patterns for explanations to be generated by automated decision systems.
This paper presents the study, its results, and the guidelines and patterns we
derived. Keywords: User Explanation; Guidelines; Patterns; Recommender Systems | |||
| The Effect of Suspicious Profiles on People Recommenders | | BIBA | Full-Text | 225-236 | |
| Luiz Augusto Pizzato; Joshua Akehurst; Cameron Silvestrini; Kalina Yacef | |||
| As the world moves towards the social web, criminals also adapt their activities to these environments. Online dating websites, and more generally people recommenders, are a particular target for romance scams. Criminals create fake profiles to attract users who believe they are entering a relationship. Scammers can cause extreme harm to people and to the reputation of the website. This makes it important to ensure that recommender strategies do not favour fraudulent profiles over those of legitimate users. There is therefore a clear need to gain understanding of the sensitivity of recommender algorithms to scammers. We investigate this by (1) establishing a corpus of suspicious profiles and (2) assessing the effect of these profiles on the major classes of reciprocal recommender approaches: collaborative and content-based. Our findings indicate that collaborative strategies are strongly influenced by the suspicious profiles, while a pure content-based technique is not influenced by these users. | |||
| Users and Noise: The Magic Barrier of Recommender Systems | | BIBAK | Full-Text | 237-248 | |
| Alan Said; Brijnesh J. Jain; Sascha Narr; Till Plumbaum | |||
| Recommender systems are crucial components of most commercial web sites to
keep users satisfied and to increase revenue. Thus, a lot of effort is made to
improve recommendation accuracy. But when is the best possible performance of
the recommender reached? The magic barrier, refers to some unknown level of
prediction accuracy a recommender system can attain. The magic barrier reveals
whether there is still room for improving prediction accuracy, or indicates
that any further improvement is meaningless. In this work, we present a
mathematical characterization of the magic barrier based on the assumption that
user ratings are afflicted with inconsistencies -- noise. In a case study with
a commercial movie recommender, we investigate the inconsistencies of the user
ratings and estimate the magic barrier in order to assess the actual quality of
the recommender system. Keywords: Recommender Systems; Noise; Evaluation Measures; User Inconsistencies | |||
| Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information | | BIBAK | Full-Text | 249-260 | |
| Michael A. Sao Pedro; Ryan S. J. d. Baker; Janice D. Gobert | |||
| Data-mined models often achieve good predictive power, but sometimes at the
cost of interpretability. We investigate here if selecting features to increase
a model's construct validity and interpretability also can improve the model's
ability to predict the desired constructs. We do this by taking existing models
and reducing the feature set to increase construct validity. We then compare
the existing and new models on their predictive capabilities within a held-out
test set in two ways. First, we analyze the models' overall predictive
performance. Second, we determine how much student interaction data is
necessary to make accurate predictions. We find that these reduced models with
higher construct validity not only achieve better agreement overall, but also
achieve better prediction with less data. This work is conducted in the context
of developing models to assess students' inquiry skill at designing controlled
experiments and testing stated hypotheses within a science inquiry microworld. Keywords: science microworlds; science inquiry; inquiry assessment; behavior detector;
educational data mining; construct validity; feature selection; J48 | |||
| Inferring Personality of Online Gamers by Fusing Multiple-View Predictions | | BIBAK | Full-Text | 261-273 | |
| Jianqiang Shen; Oliver Brdiczka; Nicolas Ducheneaut; Nicholas Yee; Bo Begole | |||
| Reliable personality prediction can have direct impact on many adaptive
systems, such as targeted advertising, interface personalization and content
customization. We propose an algorithm to infer a user's personality profile
more reliably by fusing analytical predictions from multiple sources including
behavioral traces, textual data, and social networking information. We applied
and validated our approach using a real data set obtained from 1,040 World of
Warcraft players. Besides behavioral and social networking information, we
found that text analysis of character names yields the strongest personality
cues. Keywords: personality; behavior analysis; social networks; sentiment analysis; virtual
worlds | |||
| Towards Adaptive Information Visualization: On the Influence of User Characteristics | | BIBAK | Full-Text | 274-285 | |
| Dereck Toker; Cristina Conati; Giuseppe Carenini; Mona Haraty | |||
| The long-term goal of our research is to design information visualization
systems that adapt to the specific needs, characteristics, and context of each
individual viewer. In order to successfully perform such adaptation, it is
crucial to first identify characteristics that influence an individual user's
effectiveness, efficiency, and satisfaction with a particular information
visualization type. In this paper, we present a study that focuses on
investigating the impact of four user characteristics (perceptual speed, verbal
working memory, visual working memory, and user expertise) on the effectiveness
of two common data visualization techniques: bar graphs and radar graphs. Our
results show that certain user characteristics do in fact have a significant
effect on task efficiency, user preference, and ease of use. We conclude with a
discussion of how our findings could be effectively used for an adaptive
visualization system. Keywords: User characteristics; User Evaluation; Adaptive Information Visualization | |||
| WTF? Detecting Students Who Are Conducting Inquiry Without Thinking Fastidiously | | BIBAK | Full-Text | 286-296 | |
| Michael Wixon; Ryan S. J. d. Baker; Janice D. Gobert; Jaclyn Ocumpaugh | |||
| In recent years, there has been increased interest and research on
identifying the various ways that students can deviate from expected or desired
patterns while using educational software. This includes research on gaming the
system, player transformation, haphazard inquiry, and failure to use key
features of the learning system. Detection of these sorts of behaviors has
helped researchers to better understand these behaviors, thus allowing software
designers to develop interventions that can remediate them and/or reduce their
negative impacts on user outcomes. In this paper, we present a first detector
of what we term WTF ("Without Thinking Fastidiously") behavior, based on data
from the Phase Change microworld in the Science ASSISTments environment. In WTF
behavior, the student is interacting with the software, but their actions
appear to have no relationship to the intended learning task. We discuss the
detector development process, validate the detectors with human labels of the
behavior, and discuss implications for understanding how and why students
conduct inquiry without thinking fastidiously while learning in science inquiry
microworlds. Keywords: student modeling; educational data mining; intelligent tutoring system;
science inquiry; off-task behavior | |||
| Adapting Performance Feedback to a Learner's Conscientiousness | | BIBA | Full-Text | 297-302 | |
| Matt Dennis; Judith Masthoff; Chris Mellish | |||
| To keep a learner motivated, an intelligent tutoring system may need to adapt its feedback to the learner's characteristics. We are particularly interested in adaptation of performance feedback to the learner's personality. Following on from an earlier study that investigated the effect of generalized self-efficacy, this study examines how feedback may need to be adapted to the trait Conscientiousness from the Five Factor Model. We used a User-as-Wizard approach, with participants taking the role of the adaptive feedback generator. Participants were presented with a fictional student with a validated polarized level of Conscientiousness, along with a set of marks the student had achieved in a test. They provided feedback to the learner in the form of a short statement. We examined the level to which participants bent the truth as adaptation to the learner's conscientiousness. The study suggests that adaptation to conscientiousness may be needed: using a positive slant for highly conscientious students with failing grades. | |||
| A Multi-faceted User Model for Twitter | | BIBAK | Full-Text | 303-309 | |
| John Hannon; Kevin McCarthy; Michael P. O'Mahony; Barry Smyth | |||
| In this paper we describe an initial attempt to build multi-faceted user
models from raw Twitter data. The key contribution is to describe a technique
for categorising users and their social ties according to a collection of
curated topical categories and in this way resolve much of the preference noise
that is inherent within user conversations. We go on to analyse and evaluate
this approach on a data set of over 240,000 Twitter users and discuss the
applications of these novel user models. Keywords: Twitter Lists; User Model; Tags | |||
| Evaluating Rating Scales Personality | | BIBAK | Full-Text | 310-315 | |
| Tsvi Kuflik; Alan J. Wecker; Federica Cena; Cristina Gena | |||
| User ratings are a valuable source of information for recommender systems:
often, personalized suggestions are generated by predicting the user's
preference for an item, based on ratings users explicitly provided for other
items. In past experiments that were carried out by us in the gastronomy
domain, results showed that rating scales have their own "personality" exerting
an influence on user ratings. In this paper, we aim at deepening our knowledge
of the effect of rating scale personality on user ratings by taking into
account new empirical settings and a different domain (a museum), and partially
different rating scales. We compare the results of these new experiments with
our previous ones. Our aim is to further validate in a different application
context, and domain, and with different rating scales, the fact that rating
scales have their own personality which affects users' rating behavior. Keywords: rating scales; user study; recommender systems | |||
| Automating the Modeling of Learners' Erroneous Behaviors in Model-Tracing Tutors | | BIBAK | Full-Text | 316-321 | |
| Luc Paquette; Jean-François Lebeau; André Mayers | |||
| Modeling learners is a fundamental part of intelligent tutoring systems. It
allows tutors to provide personalized feedback and to assess the learners'
mastery over a task domain. One aspect often overlooked is the modeling of
erroneous behaviors that can be used to provide error specific feedback. This
is especially true for model-tracing tutors that usually require erroneous
procedural knowledge associated to each of the possible error. This process can
be automated thanks to a task independent model describing the learners'
erroneous behaviors. The model proposed in this paper is inspired by the Sierra
theory of procedural error and is developed for ASTUS, an authoring framework
for model-tracing tutors. Keywords: Erroneous behaviors; learner modeling; model-tracing tutors | |||
| Using Touch as a Predictor of Effort: What the iPad Can Tell Us about User Affective State | | BIBAK | Full-Text | 322-327 | |
| David H. Shanabrook; Ivon Arroyo; Beverly Park Woolf | |||
| Touch is a new and significantly different method of interacting with a
computer and it is being adapted at a rapidly increasing rate with the
introduction of the tablet computer. We log the characteristics of a student's
touch interaction while solving math problems on a tablet. By correlating this
data to high and low effort problem solving conditions we demonstrate the
ability to predict student effort level. The technique is context free, thus
can potentially be applied to any computer tablet application. Keywords: touch; data mining; statistical analysis; intelligent tutoring systems;
human computer interaction; affective computing | |||
| Domain Ranking for Cross Domain Collaborative Filtering | | BIBAK | Full-Text | 328-333 | |
| Amit Tiroshi; Tsvi Kuflik | |||
| In recommendation systems a variation of the cold start problem is a
situation where the target user has few-to-none item ratings belonging to the
target domain (e.g., movies) to base recommendations on. One way to overcome
this is by basing recommendations on items from different domains, for example
recommending movies based on the target user's book item ratings. This
technique is called cross-domain recommendation. When basing recommendations on
a source domain that is different from the target domain a question arises,
from which domain should items be chosen? Is there a source domain that is a
better predictor for each target domain? Do books better predict a users' taste
in movies or perhaps it's their music preferences? In this study we present
initial results of work in progress that ranks and maps between pairs of
domains based on the ability to create recommendations in domain one using
ratings of items from the other domain. The recommendations are made using
cross domain collaborative filtering, and evaluated on the social networking
profiles of 2148 users. Initial results show that information that is freely
available in social networks can be used for cross domain recommendation and
that there are differences between the source domains with respect to the
quality of the recommendations. Keywords: cross-domain recommendation; cold-start problem; collaborative-filtering | |||
| User Modelling Ecosystems: A User-Centred Approach | | BIBAK | Full-Text | 334-339 | |
| Rainer Wasinger; Michael Fry; Judy Kay; Bob Kummerfeld | |||
| The recent exponential growth in mobile applications and the growing
reliance on and awareness of 'user models' by end-users have led to the need to
rethink the functional and end-user requirements of existing user modelling
systems. This paper has two goals. Firstly, leveraging a functioning user
modelling ecosystem that provides anywhere and anytime access to desktop-,
web-, and mobile- applications, this paper identifies a current opportunity
(and need) to enhance user interaction with existing user modelling frameworks,
by extending beyond the stereotypical cloud-based user modelling approach to
encompass also a client-based service and an accompanying synchronisation
module. Secondly, we draw on an analysis of previous work and a small user
study, to establish the need for a user-centred design focus for user modelling
frameworks. We also identify functionality that end-users (rather than
developers) need and want from a user modelling ecosystem. Keywords: User modelling ecosystems; client-side and cloud-based user models;
personalisation; framework requirements; user-centred design | |||
| Adaptive Score Reports | | BIBAK | Full-Text | 340-345 | |
| Diego Zapata-Rivera | |||
| This paper introduces the idea of adaptive score reports that can be used to
provide educational stakeholders with a personalized experience aimed at
facilitating student understanding and use of assessment information. These
reports can also provide additional learning opportunities for users based on
assessment results. An interactive score report for students is used to
illustrate opportunities for adaptation. Keywords: Adaptive score reports; Open student models; Educational assessment;
Adaptive learning environments | |||
| Improving Matrix Factorization Techniques of Student Test Data with Partial Order Constraints | | BIBA | Full-Text | 346-350 | |
| Behzad Beheshti; Michel Desmarais | |||
| Matrix factorization is a general technique that can extract latent factors from data. Recent studies applied matrix factorization to the problem of establishing which skills are required by question items, and for assessing student skills mastery from student performance data. A number of generic algorithms, such as Non-negative Matrix Factorization and Tensor factorization, are used in these studies to perform the factorization, but few have looked at optimizing these algorithms to the specific characteristics of student performance data. In this thesis, we explore how one such characteristic can lead to better factorization: the fact that items are learnt in a constrained order and allow such inferences as if a difficult item is succeeded, an easier one should also be succeeded. In particular, we want to address this question: can a partial order knowledge structure (POKS) be used to guide matrix factorization algorithms and lead to faster or better solutions to latent skills modelling? | |||
| Evaluating an Implementation of an Adaptive Game-Based Learning Architecture | | BIBA | Full-Text | 351-355 | |
| Florian Berger | |||
| Current Game-based Learning (GBL) applications often lack features that have been commonplace in conventional e-learning. One of these is the ability to provide players with a personalised experience. The author's dissertation aims at further establishing adaptivity in GBL as a practical feature through three contributions: An adaptive educational game that fulfills key demands for GBL; an architecture that adapts at runtime according to authored references; and a controlled trial to evaluate effects of adaptivity in a statistically sound way. | |||
| Towards a Generic Model for User Assistance | | BIBAK | Full-Text | 356-360 | |
| Blandine Ginon | |||
| This paper aims to present our generic model for user assistance. The
approach we propose allows a designer to describe the assistance requested for
an application using a common formalism; this description is then used by a
generic assistant. A set of epiphyte assistants is then involved to perform
assistance actions specified in the assistance description made by the
designer. Keywords: Assistance; user model; formalism; personalization | |||
| Resolving Data Sparsity and Cold Start in Recommender Systems | | BIBA | Full-Text | 361-364 | |
| Guibing Guo | |||
| Recommender systems (RSs) are heavily used in e-commerce to provide users
with high quality, personalized recommendations from a large number of choices.
Collaborative filtering (CF) is a widely used technique to generate
recommendations [1]. The main research problems we desire to address are the
two severe issues that original CF inherently suffers from:
* Data sparsity arises from the phenomenon that users in general rate only a
limited number of items; * Cold start refers to the difficulty in bootstrapping the RSs for new users or new items. The principle of CF is to aggregate the ratings of like-minded users. However, the reported matrix of user-item ratings is usually very sparse (up to 99%) due to users' lack of knowledge or incentives to rate items. In addition, for the new users or new items, in general, they report or receive only a few or no ratings. Both issues will prevent the CF from providing effective recommendations, because users' preference is hard to extract. Although many algorithms have been proposed to date, these issues have not been well-addressed yet. | |||
| Data Mining for Adding Adaptive Interventions to Exploratory and Open-Ended Environments | | BIBAK | Full-Text | 365-368 | |
| Samad Kardan | |||
| Due to the open ended nature of the interaction with exploratory
environments (EE) for learning, it is not trivial to add mechanisms for
providing adaptive support to users. Our goal is to devise and evaluate a data
mining approach for providing adaptive interventions that help users to achieve
better task performance during the interaction with an EE. Keywords: Educational Data Mining; Adaptive User Interfaces; Adaptive Interventions;
Exploratory Environments | |||
| Formalising Human Mental Workload as Non-monotonic Concept for Adaptive and Personalised Web-Design | | BIBAK | Full-Text | 369-373 | |
| Luca Longo | |||
| Web Design has been evolving with Web-based systems becoming more complex
and structured due to the delivery of personalised information adapted to
end-users. Although information presented can be useful and well formatted,
people have little mental workload available for dealing with unusable systems.
Subjective mental workload assessments tools are usually adopted to measure the
impact of Web-tasks upon end-users thanks to their ease of use and are aimed at
supporting design practices. The Nasa Task Load Index subjective procedure has
been taken as a reference technique for measuring mental workload, but it has a
background in aircraft cockpits, supervisory and process control environments.
We argue that the tool is not fully appropriate for dealing with
Web-information tasks, characterised by a wide spectrum of contexts of use,
cognitive factors and individual user differences such as skill, background,
emotional state and motivation. Furthermore, in this model, inputs are averaged
without considering their mutual interactions and relations. We propose to see
human mental workload as non-monotonic concept and to model it via
argumentation theory. The evaluation strategy includes comparisons with the
NASA-TLX in terms of statistical correlation, sensitivity, diagnosticity,
selectivity and reliability. Keywords: Human Mental Workload; Non-monotonic Reasoning; Argumentation Theory;
Human-Computer Interaction; Web Design | |||
| Detecting, Acquiring and Exploiting Contextual Information in Personalized Services | | BIBAK | Full-Text | 374-377 | |
| Ante Odic | |||
| The PhD research presented in this paper addresses some of the problems
involved in creating a context-aware personalized service. Our main interest is
in the steps of defining, detecting, acquiring and using real and relevant
context of users. Our goals are to: collect and publish a context-rich movie
recommender database, add theoretical requirements for contextual information
in existing definitions of context, develop a methodology for relevant-context
detection and inspect the impact of relevant and irrelevant context on the
rating prediction using the matrix-factorization algorithm. This paper presents
the work done so far and future plans with open issues. Keywords: user modeling; recommender systems; contextual information | |||
| Multi-source Provenance-aware User Interest Profiling on the Social Semantic Web | | BIBAK | Full-Text | 378-381 | |
| Fabrizio Orlandi | |||
| The creation of accurate user profiles of interest across heterogeneous
websites is a fundamental step for personalisation, recommendations and
analysis of social networks. The opportunities offered by the Web of Data and
Semantic Web technologies introduce new interesting challenges. In particular,
the main benefits for user profiling techniques are given by the extensive
amount of already available and structured information and the solution to the
"cold start" problem. On the other hand it is difficult to manage a massive
"open corpus" such as the Web of Data and select only the relevant features and
sources from an heterogeneous collection of datasets. Hence we propose semantic
technologies for interlinking social websites and provenance management on the
Web of Data to retrieve accurate information about data producers. The goal is
to build comprehensive user profiles based on qualitative and quantitative
measures about user activities across social sites. Keywords: Social Web; Semantic Web; User Modelling; User Profiles; Web of Data;
Provenance of Data | |||
| User Feedback and Preferences Mining | | BIBAK | Full-Text | 382-386 | |
| Ladislav Peska | |||
| In this paper, we present our vision and some initial experiments on how to
anticipate significance, similarity or polarity of various types of (preferably
implicit) user feedback and how to form individual user preference for
recommendation. Throughout the corporate web, we can observe the same patterns
or actions in user behavior (e.g. page-view, amount of scrolling, rating or
purchasing). Recorded user behavior -- user feedback -- is often used as base
for personalized recommendation, but the connection between the feedback and
user preference is often unclear or noisy.
Our goal is to analyze user behavior in order to understand its relation to the user preference. We report on some initial experiments on a real-world e-commerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences. Keywords: User preference; user behavior; implicit feedback; recommender systems | |||
| Ubiquitous Fuzzy User Modeling for Multi-application Environments by Mining Socially Enhanced Online Traces | | BIBAK | Full-Text | 387-390 | |
| Hilal Tarakci; Nihan Kesim Cicekli | |||
| In this paper, we propose an interoperable ubiquitous user model which
illustrates different aspects of the individual's interests, preferences and
personality. It is constructed by mining socially enhanced online traces of the
user and aggregating the partially obtained profiles. Those traces include
actions performed and relationships established in the social web accounts in
addition to the local machine traces such as bookmarks and web history.
Moreover, we claim that mining the content in a context-aware approach and
computing fuzziness values during the process results in a more reliable user
profile. Keywords: ubiquitous user model; social web mining; user profiles; user modeling | |||
| Facilitating Code Example Search on the Web through Expertise Personalization | | BIBA | Full-Text | 391-394 | |
| Annie T. T. Ying | |||
| The Web is an important resource for a programmer: as much as 20% of a programmer's time is spent on the Web [2]. When a programmer searches for information on the Web, two distinct information needs arise depending on the programmer's previous knowledge of a library's Application Programming Interfaces (APIs): learning how to invoke a software library versus reminding the programmers themselves the details deemed not worth remembering [2]. | |||