1,022 research outputs found
Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends
Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learnerβs cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Rule-based User Characteristics Acquisition from Logs with Semantics for Personalized Web-Based Systems
Personalization of web-based information systems based on specialized user models has become more important in order to preserve the effectiveness of their use as the amount of available content increases. We describe a user modeling approach based on automated acquisition of user behaviour and its successive rule-based evaluation and transformation into an ontological user model. We stress reusability and flexibility by introducing a novel approach to logging, which preserves the semantics of logged events. The successive analysis is driven by specialized rules, which map usage patterns to knowledge about users, stored in an ontology-based user model. We evaluate our approach via a case study using an enhanced faceted browser, which provides personalized navigation support and recommendation
ΠΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΈ ΠΏΡΠΎΡΠ΅ΡΠΈ, Π΅ΠΌΠΎΡΠΈΠΈ ΠΈ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΡΠΈ
Π‘ΡΡΠ΄ΠΈΡΠ°ΡΠ° ΠΏΡΠ΅Π·Π΅Π½ΡΠΈΡΠ° ΠΈΡΡΡΠ°ΠΆΡΠ²Π°ΡΠ° ΠΎΠ΄ ΠΏΠΎΠ²Π΅ΡΠ΅ Π½Π°ΡΡΠ½ΠΈ Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½ΠΈ, ΠΊΠ°ΠΊΠΎ Π²Π΅ΡΡΠ°ΡΠΊΠ° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ°, Π½Π΅Π²ΡΠΎΠ½Π°ΡΠΊΠΈ, ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ°, Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΠΊΠ° ΠΈ ΡΠΈΠ»ΠΎΠ·ΠΎΡΠΈΡΠ°, ΠΊΠΎΠΈ ΠΈΠΌΠ°Π°Ρ ΠΏΠΎΡΠ΅Π½ΡΠΈΡΠ°Π» Π·Π° ΠΊΡΠ΅ΠΈΡΠ°ΡΠ΅ Π½Π° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈ Π°Π½ΡΡΠΎΠΏΠΎΠΌΠΎΡΡΠ½ΠΈ Π°Π³Π΅Π½ΡΠΈ ΠΈ ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΠ²Π½ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ. Π‘Π΅ ΡΠ°Π·Π³Π»Π΅Π΄ΡΠ²Π°Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠΈΡΠ΅ ΠΎΠ΄ ΡΠΈΠΌΠ±ΠΎΠ»ΠΈΡΠΊΠ° ΠΈ ΠΊΠΎΠ½Π΅ΠΊΡΠΈΠΎΠ½ΠΈΡΡΠΈΡΠΊΠ° Π²Π΅ΡΡΠ°ΡΠΊΠ° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ° Π·Π° ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠ°ΡΠ΅ Π½Π° ΡΠΎΠ²Π΅ΠΊΠΎΠ²ΠΈΡΠ΅ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΈ ΠΏΡΠΎΡΠ΅ΡΠΈ, ΠΌΠΈΡΠ»Π΅ΡΠ΅, Π΄ΠΎΠ½Π΅ΡΡΠ²Π°ΡΠ΅ ΠΎΠ΄Π»ΡΠΊΠΈ, ΠΌΠ΅ΠΌΠΎΡΠΈΡΠ° ΠΈ ΡΡΠ΅ΡΠ΅. Π‘Π΅ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠ°Π°Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠ΅ Π²ΠΎ Π²Π΅ΡΡΠ°ΡΠΊΠ° ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ° ΠΈ ΡΠΎΠ±ΠΎΡΠΈΠΊΠ° ΠΊΠΎΠΈ ΠΊΠΎΡΠΈΡΡΠ°Ρ Π΅ΠΌΠΎΡΠΈΠΈ ΠΊΠ°ΠΊΠΎ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·Π°ΠΌ Π·Π° ΠΊΠΎΠ½ΡΡΠΎΠ»Π° Π½Π° ΠΎΡΡΠ²Π°ΡΡΠ²Π°ΡΠ΅ Π½Π° ΡΠ΅Π»ΠΈΡΠ΅ Π½Π° ΡΠΎΠ±ΠΎΡΠΎΡ, ΠΊΠ°ΠΊΠΎ ΡΠ΅Π°ΠΊΡΠΈΡΠ° Π½Π° ΠΎΠ΄ΡΠ΅Π΄Π΅Π½ΠΈ ΡΠΈΡΡΠ°ΡΠΈΠΈ, Π·Π° ΠΎΠ΄ΡΠΆΡΠ²Π°ΡΠ΅ Π½Π° ΠΏΡΠΎΡΠ΅ΡΠΎΡ Π½Π° ΡΠΎΡΠΈΡΠ°Π»Π½Π° ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΡΠ° ΠΈ Π·Π° ΡΠΎΠ·Π΄Π°Π²Π°ΡΠ΅ Π½Π° ΠΏΠΎΡΠ²Π΅ΡΠ»ΠΈΠ²ΠΈ Π°Π½ΡΡΠΎΠΏΠΎΡΠΌΡΠ½ΠΈ Π°Π³Π΅Π½ΡΠΈ.
ΠΡΠ΅Π·Π΅Π½ΡΠΈΡΠ°Π½ΠΈΡΠ΅ ΠΈΠ½ΡΠ΅ΡΠ΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈ ΡΠ΅ ΠΌΠΎΡΠΈΠ²Π°ΡΠΈΡΠ° Π·Π° ΡΠΎΠ·Π΄Π°Π²Π°ΡΠ΅ Π½Π° Π°Π½ΠΈΠΌΠΈΡΠ°Π½ΠΈ Π°Π³Π΅Π½ΡΠΈ ΠΊΠΎΠΈ ΠΊΠΎΡΠΈΡΡΠ°Ρ Π³ΠΎΠ²ΠΎΡ, Π³Π΅ΡΡΠΎΠ²ΠΈ, ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΡΠ° ΠΈ Π΄ΡΡΠ³ΠΈ Π½Π΅Π²Π΅ΡΠ±Π°Π»Π½ΠΈ ΠΌΠΎΠ΄Π°Π»ΠΈΡΠ΅ΡΠΈ ΠΏΡΠΈ ΠΊΠΎΠ½Π²Π΅ΡΠ·Π°ΡΠΈΡΠ° ΡΠΎ ΠΊΠΎΡΠΈΡΠ½ΠΈΡΠΈΡΠ΅ Π²ΠΎ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠ½ΠΈΡΠ΅ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΡΠΈ
Runtime Requirements Monitoring Framework for Adaptive e-Learning Systems
International audienceAs academic learners and companies are turning to e-learning courses to achieve their personal and professional goals, it becomes more and more important to handle service quality in this sector. Despite scientific research conducted to personalize the learning process and meet learner's requirements under adaptive e-learning systems, however, the specification and management of quality attribute is particularly challenging due to problems arising from environmental variability. In our view, a detailed and high-level specification of requirements supported through the whole system lifecycle is needed for a comprehensive management of adaptive e-learning systems, especially in continuously changing environmental conditions. In this paper, we propose a runtime requirements monitoring to check the conformity of adaptive e-learning systems to their requirements and ensure that the activities offered by these learning environments can achieve the desired learning outcomes. As a result, when deviations (i.e., not satisfied requirements) occur, they are identified and then notified during system operation. With our approach, the requirements are supported during the whole system lifecycle. First, we specify system's requirements in the form of a dynamic software product line. This specification applies a novel requirements engineering language that combines goal-driven requirements with features and claims and avoid the enumeration of all desired adaptation strategies (i.e. when an adaptation should be applied) at the design time. Second, the specification is automatically transformed into a constraint satisfaction problem that reduces the requirements monitoring into a constraint program at runtime
Participatory learner modelling design: a methodology for iterative learner models development
Learner models are built to offer personalised solutions related to learning. They are often
developed in parallel to the development of adaptive learning systems and thus, linked to
the systemβs development. The adaptive learning systems literature reports numerous
accounts of learner model development, but there are no reports on the methodological
aspects of developing learner models and the relation between the development of the
learner model component and the rest of the system. This paper presents the
Participatory Learner Modelling Design methodology, which outlines the steps for learner
model development and their relation to the development of the system. The methodology
is illustrated with a case study of an adaptive educational system
Organization and Usage of Learning Objects within Personal Computers
Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 7.6To promote the integration of Desktop related Knowledge Management and Technology Enhanced Learning this deliverable aims at increasing the awareness of Desktop research within the Professional Learning community and at familiarizing the e-Learning researchers with the state-of-the-art in the relevant areas of Personal Information Management (PIM), as well as with the currently on-going activities and some of the regular PIM publication venues
Personalizing Interactions with Information Systems
Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the userβs mental model with the underlying information systemβs organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains
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