| Preface to the special issue on data mining for personalised educational systems | | BIB | Full-Text | 1-3 | |
| Cristóbal Romero; Sebastián Ventura | |||
| A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments | | BIBAK | Full-Text | 5-49 | |
| Jun-Ming Su; Shian-Shyong Tseng; Huan-Yu Lin | |||
| With the heterogeneous proliferation of mobile devices, the delivery of
learning materials on such devices becomes subject to more and more
requirements. Personalized learning content adaptation, therefore, becomes
increasingly important to meet the diverse needs imposed by devices, users,
usage contexts, and infrastructure. Historical server logs offer a wealth of
information on hardware capabilities, learners' preferences, and network
conditions, which can be utilized to respond to a new user request with the
personalized learning content created from a previous similar request. In this
paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM),
which applies data mining techniques, including clustering and decision tree
approaches, to efficiently manage a large number of historical learners'
requests. The proposed method will intelligently and directly deliver proper
personalized learning content with higher fidelity from the Sharable Content
Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by
means of the proposed adaptation decision and content synthesis processes.
Furthermore, the experimental results indicate that it is efficient and is
expected to prove beneficial to learners. Keywords: Personalized learning content; Content adaptation; Mobile learning
environment; Data mining; Learning object repository | |||
| Activity sequence modelling and dynamic clustering for personalized e-learning | | BIBAK | Full-Text | 51-97 | |
| Mirjam Köck; Alexandros Paramythis | |||
| Monitoring and interpreting sequential learner activities has the potential
to improve adaptivity and personalization within educational environments. We
present an approach based on the modeling of learners' problem solving activity
sequences, and on the use of the models in targeted, and ultimately automated
clustering, resulting in the discovery of new, semantically meaningful
information about the learners. The approach is applicable at different levels:
to detect pre-defined, well-established problem solving styles, to identify
problem solving styles by analyzing learner behaviour along known learning
dimensions, and to semi-automatically discover learning dimensions and concrete
problem solving patterns. This article describes the approach itself,
demonstrates the feasibility of applying it on real-world data, and discusses
aspects of the approach that can be adjusted for different learning contexts.
Finally, we address the incorporation of the proposed approach in the
adaptation cycle, from data acquisition to adaptive system interventions in the
interaction process. Keywords: Adaptivity; User modeling; E-learning; Data mining; Clustering; Unsupervised
learning | |||
| An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts | | BIBAK | Full-Text | 99-135 | |
| Kasia Muldner; Winslow Burleson | |||
| Students who exploit properties of an instructional system to make progress
while avoiding learning are said to be "gaming" the system. In order to
investigate what causes gaming and how it impacts students, we analyzed log
data from two Intelligent Tutoring Systems (ITS). The primary analyses focused
on six college physics classes using the Andes ITS for homework and test
preparation, starting with the research question: What is a better predictor of
gaming, problem or student? To address this question, we developed a
computational gaming detector for automatically labeling the Andes data, and
applied several data mining techniques, including machine learning of Bayesian
network parameters. Contrary to some prior findings, the analyses indicated
that student was a better predictor of gaming than problem. This result was
surprising, so we tested and confirmed it with log data from a second ITS (the
Algebra Cognitive Tutor) and population (high school students). Given that
student was more predictive of gaming than problem, subsequent analyses focused
on how students gamed and in turn benefited (or not) from instructional
features of the environment, as well as how gaming in general influenced
problem solving and learning outcomes. Keywords: Educational data mining; Gaming; Utility of hints; Bayesian network
parameter learning | |||
| Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies | | BIBAK | Full-Text | 137-180 | |
| Min Chi; Kurt VanLehn; Diane Litman | |||
| For many forms of e-learning environments, the system's behavior can be
viewed as a sequential decision process wherein, at each discrete step, the
system is responsible for selecting the next action to take. Pedagogical
strategies are policies to decide the next system action when there are
multiple ones available. In this project we present a Reinforcement Learning
(RL) approach for inducing effective pedagogical strategies and empirical
evaluations of the induced strategies. This paper addresses the technical
challenges in applying RL to Cordillera, a Natural Language Tutoring System
teaching students introductory college physics. The algorithm chosen for this
project is a model-based RL approach, Policy Iteration, and the training corpus
for the RL approach is an exploratory corpus, which was collected by letting
the system make random decisions when interacting with real students. Overall,
our results show that by using a rather small training corpus, the RL-induced
strategies indeed measurably improved the effectiveness of Cordillera in that
the RL-induced policies improved students' learning gains significantly. Keywords: Reinforcement learning; Pedagogical strategy; Machine learning; Human
learning | |||
| Content-free collaborative learning modeling using data mining | | BIBAK | Full-Text | 181-216 | |
| Antonio R. Anaya; Jesús G. Boticario | |||
| Modeling user behavior (user modeling) via data mining faces a critical
unresolved issue: how to build a collaboration model based on frequent analysis
of students in order to ascertain whether collaboration has taken place.
Numerous human-based and knowledge-based solutions to this problem have been
proposed, but they are time-consuming or domain-dependent. The diversity of
these solutions and their lack of common characteristics are an indication of
how unresolved this issue remains. Bearing this in mind, our research has made
progress on several fronts. First, we have found supportive evidence, based on
a collaborative learning experience with hundreds of students over three
consecutive years, that an approach using domain independent learning that is
transferable to current e-learning platforms helps both students and teachers
to manage student collaboration better. Second, the approach draws on a
domain-independent modeling method of collaborative learning based on data
mining that helps clarify which user-modeling issues are to be considered. We
propose two data mining methods that were found to be useful for evaluating
student collaboration, and discuss their respective advantages and
disadvantages. Three data sources to generate and evaluate the collaboration
model were identified. Third, the features being modeled were made accessible
to students in several meta-cognitive tools. Their usage of these tools showed
that the best approach to encourage student collaboration is to show only the
most relevant inferred information, simply displayed. Moreover, these tools
also provide teachers with valuable modeling information to improve their
management of the collaboration. Fourth, an ontology, domain independent
features and a process that can be applied to current e-learning platforms make
the approach transferable and reusable. Fifth, several open research issues of
particular interest were identified. We intend to address these open issues
through research in the near future. Keywords: Collaborative learning; Collaboration modeling; Data mining; Open models;
Collaboration evaluation; Meta-cognitive tools in collaborative learning | |||
| A data mining approach to guide students through the enrollment process based on academic performance | | BIBAK | Full-Text | 217-248 | |
| César Vialardi; Jorge Chue; Juan Pablo Peche | |||
| Student academic performance at universities is crucial for education
management systems. Many actions and decisions are made based on it,
specifically the enrollment process. During enrollment, students have to decide
which courses to sign up for. This research presents the rationale behind the
design of a recommender system to support the enrollment process using the
students' academic performance record. To build this system, the CRISP-DM
methodology was applied to data from students of the Computer Science
Department at University of Lima, Perú. One of the main contributions of
this work is the use of two synthetic attributes to improve the relevance of
the recommendations made. The first attribute estimates the inherent difficulty
of a given course. The second attribute, named potential, is a measure of the
competence of a student for a given course based on the grades obtained in
related courses. Data was mined using C4.5, KNN (K-nearest neighbor),
Naïve Bayes, Bagging and Boosting, and a set of experiments was developed
in order to determine the best algorithm for this application domain. Results
indicate that Bagging is the best method regarding predictive accuracy. Based
on these results, the "Student Performance Recommender System" (SPRS) was
developed, including a learning engine. SPRS was tested with a sample group of
39 students during the enrollment process. Results showed that the system had a
very good performance under real-life conditions. Keywords: Data mining; Enrollment process; Supervised classification; Machine
learning; Recommender systems; Predictive accuracy | |||
| Evaluating and improving adaptive educational systems with learning curves | | BIBAK | Full-Text | 249-283 | |
| Brent Martin; Antonija Mitrovic | |||
| Personalised environments such as adaptive educational systems can be
evaluated and compared using performance curves. Such summative studies are
useful for determining whether or not new modifications enhance or degrade
performance. Performance curves also have the potential to be utilised in
formative studies that can shape adaptive model design at a much finer level of
granularity. We describe the use of learning curves for evaluating personalised
educational systems and outline some of the potential pitfalls and how they may
be overcome. We then describe three studies in which we demonstrate how
learning curves can be used to drive changes in the user model. First, we show
how using learning curves for subsets of the domain model can yield insight
into the appropriateness of the model's structure. In the second study we use
this method to experiment with model granularity. Finally, we use learning
curves to analyse a large volume of user data to explore the feasibility of
using them as a reliable method for fine-tuning a system's model. The results
of these experiments demonstrate the successful use of performance curves in
formative studies of adaptive educational systems. Keywords: Empirical evaluation; Intelligent tutoring systems; Student modeling; User
modelling; Domain modelling; Learning curves | |||
| User model interoperability: a survey | | BIBAK | Full-Text | 285-331 | |
| Francesca Carmagnola; Federica Cena | |||
| Nowadays a large number of user-adaptive systems has been developed.
Commonly, the effort to build user models is repeated across applications and
domains, due to the lack of interoperability and synchronization among
user-adaptive systems. There is a strong need for the next generation of user
models to be interoperable, i.e. to be able to exchange user model portions and
to use the information that has been exchanged to enrich the user experience.
This paper presents an overview of the well-established literature dealing with
user model interoperability, discussing the most representative work which has
provided valuable solutions to face interoperability issues. Based on a
detailed decomposition and a deep analysis of the selected work, we have
isolated a set of dimensions characterizing the user model interoperability
process along which the work has been classified. Starting from this analysis,
the paper presents some open issues and possible future deployments in the
area. Keywords: User model interoperability; User modeling; Interoperability; User-adaptive
systems | |||
| Preface to the special issue on personalization for e-health | | BIB | Full-Text | 333-340 | |
| Floriana Grasso; Cécile Paris | |||
| Motivating reflection about health within the family: the use of goal setting and tailored feedback | | BIBAK | Full-Text | 341-376 | |
| Nathalie Colineau; Cécile Paris | |||
| It is widely acknowledged that obesity is a serious health issue. Despite
governments' campaigns and initiatives to raise the level of awareness, the
proportion of adults classified as overweight or obese is increasing steadily.
As a result, there has been a growing interest in Human Computer Interaction
and User Modelling to study how to support health behaviour change. While most
of the work to date has focused on individuals, medical research has shown that
family engagement plays an important role on health behaviour. To consider the
family context, we are developing technology that facilitates health
discussions and encourages supportive behaviour within the family. We
investigate how to motivate members of a family to reflect upon their lifestyle
and think of ways in which they can make it healthier. In particular, we have
looked at whether providing explicit goals and tailored feedback can have an
impact. During a two week trial with families in which we explored these
strategies, we found that setting a collective goal for the family influenced
how much the family as a whole contributed, and that feedback increased
significantly mothers' participation. Our results also suggest that
establishing a family goal encouraged families to work together and, in
particular, to help each other find ways to be healthier. Finally, 76% of
participants reported discussing the task with someone in their family, and,
also discussing it together as a family (57%). Keywords: Motivation strategies; Goal setting theory; Lifestyle and wellbeing; Family
support; Health behaviour; Evaluation | |||
| Towards personalized decision support in the dementia domain based on clinical practice guidelines | | BIBAK | Full-Text | 377-406 | |
| Helena Lindgren | |||
| A set of evidence-based clinical practice guidelines has been synthesized
and integrated in the clinical decision support system DMSS-R (Dementia
Management and Support System) to support clinical routines and reasoning
processes as performed by individual health professionals in daily practice.
DMSS-R provides advice, tailored to individual and often exceptional patient
cases, to the user while providing guidance to the next step in the assessments
and support for hypothesis generation and evaluation throughout the process.
This paper describes DMSS-R and the results of a case study in clinical
practice where the system was used. The case study included interviews and
observations of the clinical investigation process as undertaken in 41 real
patient cases with suspected dementia. Two physicians participated, one of whom
was considered a novice regarding dementia while the other had a moderate level
of skills. Initially, both physicians were unfamiliar with DMSS-R. A group of
nurses together with care personnel and relatives were also involved. The most
important contribution of DMSS-R at the point of care, apart from the tailored
explanatory support related to a patient case, was the educational support it
provided. This was partly manifested in a change of routines in the encounter
with patients. Aspects regarding the individual health care professional's need
for a personalized support system are discussed and put in relation to the
team's need for support, and in relation to the diversity of disease
manifestations in this group of patients, which reinforces patient-centric
assessments. Keywords: Clinical decision-support systems; Dementia; Activity theory; Continuing
medical education; Clinical practice guidelines; Clinical practice; Interaction
design; Activity-centered design; Evaluation; Case study | |||
| Personalized emergency medical assistance for disabled people | | BIBAK | Full-Text | 407-440 | |
| Luca Chittaro; Elio Carchietti; Luca De Marco | |||
| Being able to promptly and accurately choose a proper course of action in
the field is a crucial aspect of emergency response. For this reason, emergency
medical services (EMS) rely on well established procedures that apply to the
most frequent cases first responders encounter in their practice, but do not
include special cases concerning (sensory, motor or cognitive) disabled
persons. In these cases, first responders may end up applying suboptimal or
possibly wrong procedures or lose precious time trying to adapt on-the-fly to
the special case. This paper proposes both (i) a detailed patient model for EMS
that can account for peculiar aspects of the many existing disabilities and
(ii) an adaptive information system called PRESYDIUM (Personalized Emergency
System for Disabled Humans) that provides tailored instructions in the field
for helping medical first responders in dealing with disabled persons. More
precisely, we will illustrate and discuss: (i) the design and development
process of PRESYDIUM, (ii) the patient model, which is partly based on the ICF
(International Classification of Functioning, Disability and Health) standard
proposed by the World Health Organization, (iii) the knowledge base used by the
system to provide tailored instructions to medical first responders, (iv) the
Web-based architecture of the system, (v) the different interfaces--including
one for mobile devices--the system provides to enable all the identified
stakeholders (disabled persons, their families, clinicians, EMS call center
operators, medical first responders) to easily access and possibly provide data
to the system, (vi) the evaluation of the validity of the patient model and of
the system usability which has been conducted with end users. Keywords: Personalized e-health information systems; Patient models; Disabled
patients; First responders; Emergency medical services; Tailored instructions;
Tailored decision support; Knowledge-based systems; Web-based systems; Mobile
applications | |||
| A user modeling approach for reasoning about interaction sensitive to bother and its application to hospital decision scenarios | | BIBAK | Full-Text | 441-484 | |
| Robin Cohen; Hyunggu Jung; Michael W. Fleming | |||
| In this paper, we present a framework for interacting with users that is
sensitive to the cost of bother and then focus on its application to decision
making in hospital emergency room scenarios. We begin with a model designed for
reasoning about interaction in a single-agent single-user setting and then
expand to the environment of multiagent systems. In this setting, agents
consider both whether to ask other agents to perform decision making and at the
same time whether to ask questions of these agents. With this fundamental
research as a backdrop, we project the framework into the application of
reasoning about which medical experts to interact with, sensitive to possible
bother, during hospital decision scenarios, in order to deliver the best care
for the patients that arrive. Due to the real-time nature of the application
and the knowledge-intensive nature of the decisions, we propose new parameters
to include in the reasoning about interaction and sketch their usefulness
through a series of examples. We then include a set of experimental results
confirming the value of our proposed approach for reasoning about interaction
in hospital settings, through simulations of patient care in those
environments. We conclude by pointing to future research to continue to extend
the model for reasoning about interaction in multiagent environments for the
setting of time-critical care in hospital settings. Keywords: Reasoning about interaction; Modeling bother; Multiagent systems; Hospital
decision making; Coordination of teams of health professionals; Choosing
medical experts | |||
| Design and implementation of a web-based Tailored Gymnasium to enhance self-management of Fibromyalgia | | BIBAK | Full-Text | 485-511 | |
| Luca Camerini; Michele Giacobazzi | |||
| The aim of this article is to describe the design and development of an
online gymnasium that proposes personalized exercise videos to users affected
by fibromyalgia. Fibromyalgia syndrome is a chronic condition characterized by
widespread pain in muscles, ligaments and tendons, usually associated with
sleep disorders and fatigue. Physical exercise is considered as an important
component of non-pharmacological treatments of this pathology, and the internet
is praised as a powerful resource to promote and improve physical exercise.
Yet, while online personalization of health interventions to consumers must be
grounded on empirically based guidelines, guidelines for fibromyalgia-targeted
exercises are scant. The achievements presented in this paper are twofold.
Firstly, we illustrate how we reached definition of the relevant factors for
tailoring exercise videos in relation to fibromyalgia. Secondly, we explain the
general framework of the application that is composed of an interview module
(that investigates the determinant values of a specific user), an adaptation
module (presenting the tailored set of exercises) and a logging component (used
to monitor users' interactions with the website). The paper concludes with a
discussion on the strengths and weaknesses of the proposed approach. Keywords: Personalized online information system; Tailoring health communication;
Health technology; Fibromyalgia; Self-management | |||