2,010 research outputs found
Multi-Armed Bandits for Intelligent Tutoring Systems
We present an approach to Intelligent Tutoring Systems which adaptively
personalizes sequences of learning activities to maximize skills acquired by
students, taking into account the limited time and motivational resources. At a
given point in time, the system proposes to the students the activity which
makes them progress faster. We introduce two algorithms that rely on the
empirical estimation of the learning progress, RiARiT that uses information
about the difficulty of each exercise and ZPDES that uses much less knowledge
about the problem.
The system is based on the combination of three approaches. First, it
leverages recent models of intrinsically motivated learning by transposing them
to active teaching, relying on empirical estimation of learning progress
provided by specific activities to particular students. Second, it uses
state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the
exploration/exploitation challenge of this optimization process. Third, it
leverages expert knowledge to constrain and bootstrap initial exploration of
the MAB, while requiring only coarse guidance information of the expert and
allowing the system to deal with didactic gaps in its knowledge. The system is
evaluated in a scenario where 7-8 year old schoolchildren learn how to
decompose numbers while manipulating money. Systematic experiments are
presented with simulated students, followed by results of a user study across a
population of 400 school children
Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding
In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual partsâ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance
Sample Efficient Policy Search for Optimal Stopping Domains
Optimal stopping problems consider the question of deciding when to stop an
observation-generating process in order to maximize a return. We examine the
problem of simultaneously learning and planning in such domains, when data is
collected directly from the environment. We propose GFSE, a simple and flexible
model-free policy search method that reuses data for sample efficiency by
leveraging problem structure. We bound the sample complexity of our approach to
guarantee uniform convergence of policy value estimates, tightening existing
PAC bounds to achieve logarithmic dependence on horizon length for our setting.
We also examine the benefit of our method against prevalent model-based and
model-free approaches on 3 domains taken from diverse fields.Comment: To appear in IJCAI-201
Classifying Smart Personal Assistants: An Empirical Cluster Analysis
The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazonâs Alexa or Appleâs Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes: Adaptive Voice (Vision) Assistants, Chatbot Assistants, Embodied Virtual Assistants, Passive Pervasive Assistants, and Natural Conversation Assistants
Developing personalized education. A dynamic framework
Personalized educationâthe systematic adaptation of instruction to individual learnersâhas been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptationsâranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization. (DIPF/Orig.
An Online Tutor for Astronomy: The GEAS Self-Review Library
We introduce an interactive online resource for use by students and college
instructors in introductory astronomy courses. The General Education Astronomy
Source (GEAS) online tutor guides students developing mastery of core
astronomical concepts and mathematical applications of general astronomy
material. It contains over 12,000 questions, with linked hints and solutions.
Students who master the material quickly can advance through the topics, while
under-prepared or hesitant students can focus on questions on a certain topic
for as long as needed, with minimal repetition. Students receive individual
accounts for study and course instructors are provided with overview tracking
information, by time and by topic, for entire cohorts of students. Diagnostic
tools support self-evaluation and close collaboration between instructor and
student, even for distance learners. An initial usage study shows clear trends
in performance which increase with study time, and indicates that distance
learners using these materials perform as well as or better than a comparison
cohort of on-campus astronomy students. We are actively seeking new
collaborators to use this resource in astronomy courses and other educational
venues.Comment: 15 pages, 9 figures; Vogt, N. P., and A. S. Muise. 2015. An online
tutor for general astronomy: The GEAS self-review library. Cogent Education,
2 (1
Psychophysiology in games
Psychophysiology is the study of the relationship between psychology
and its physiological manifestations. That relationship is of particular importance
for both game design and ultimately gameplaying. Playersâ psychophysiology offers
a gateway towards a better understanding of playing behavior and experience.
That knowledge can, in turn, be beneficial for the player as it allows designers to
make better games for them; either explicitly by altering the game during play or
implicitly during the game design process. This chapter argues for the importance
of physiology for the investigation of player affect in games, reviews the current
state of the art in sensor technology and outlines the key phases for the application
of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject
(project no: 318803).peer-reviewe
Online Optimization and Personalization of Teaching Sequences
International audienceIn this work we are focused on the tutoring model, that is, how to choose the activities that provide a better learning experience based on the estimation of the student competence levels and progression, and some knowledge about the cognitive and student model. We can imagine a student wanting to acquire many different skills, e.g. adding, subtracting and multiplying numbers. A teacher can help by proposing activities such as: multiple choice questions, abstract operations to compute with a pencil, games where items need to be counted through manipulation, videos, or others. The challenge is to decide what is the optimal sequence of activities that maximizes the average competence level over all skills
Online Optimization and Personalization of Teaching Sequences
International audienceIn this work we are focused on the tutoring model, that is, how to choose the activities that provide a better learning experience based on the estimation of the student competence levels and progression, and some knowledge about the cognitive and student model. We can imagine a student wanting to acquire many different skills, e.g. adding, subtracting and multiplying numbers. A teacher can help by proposing activities such as: multiple choice questions, abstract operations to compute with a pencil, games where items need to be counted through manipulation, videos, or others. The challenge is to decide what is the optimal sequence of activities that maximizes the average competence level over all skills
A Literature Review on Intelligent Services Applied to Distance Learning
Distance learning has assumed a relevant role in the educational scenario. The use of
Virtual Learning Environments contributes to obtaining a substantial amount of educational data.
In this sense, the analyzed data generate knowledge used by institutions to assist managers and
professors in strategic planning and teaching. The discovery of studentsâ behaviors enables a wide
variety of intelligent services for assisting in the learning process. This article presents a literature
review in order to identify the intelligent services applied in distance learning. The research covers
the period from January 2010 to May 2021. The initial search found 1316 articles, among which
51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning
systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems
or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the
principal services offered are recommendation systems and learning systems. In these services, the
analysis of student profiles stands out to identify patterns of behavior, detect low performance, and
identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio
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