145,623 research outputs found
The Relationship of Perceptual Learning Modality Preference and the Use of an On-Line Learning Environment to Achieve Non-Technology Related Course Objectives
The purpose of this thesis study was to explore the possibility of a relationship between perceptual learning modality preference and efficacy in the use of an on-line learning environment to achieve non-technology related course objectives. Subjects were 30 adult students enrolled in the CaseNET course administered by the University of Virgina. Two research questions were explored: 1.) Is there a difference in feelings of student efficacy, in a course which uses Internet technology to achieve non-technology related course objectives, for auditory, visual, and tactile learners? 2.) Is there a difference in the use of student adaptation techniques for tactile, visual, and auditory learners in their use of Internet course materials to achieve course objectives? The students\u27 learning modality preferences were determined using a 25 item sensory modality preference inventory completed by the student on-line, which simultaneously returned their preference on the screen and recorded it in a data base. Levels of efficacy and adaptation were measured according to the students\u27 answers on an exit survey, also taken on-line, which were submitted by the student to the data base. Findings imply that no perceptual modality preference group had a particularly low sense of efficacy in the use of an on-line environment to achieve non-technology related course objectives. For those questions on the exit survey indicating high efficacy, with a range of 13-56 and a mean of 42.66, auditory learners averaged a score averaged a score of 49.60, visual learners scored an average of 41.00, and tactile learners scored an average of 41.58. A high score indicates high efficacy. Adaptation scores were calculated based on the students\u27 response to exit survey questions inquiring as to their attempts to manipulate the on-line environment. Auditory learners had an average adaptation score of 1.48, visual learners had an average adaptation score 1.66, and tactile learners had an average adaptation score of 1.63, with a range of 1-2 and a mean of 1.62. A high score indicates low adaptation. Tables reporting significant findings are included. It is contended that perceptual modality preference is a necessary criteria for the evaluation of on-line environments as an instructional tool. The author provides recommendations for further study
Profile transformation in mobile technology based educational systems : a thesis presented in partial fulfillment of the requirements for the degree of Master of Information Science in Information Systems at Massey University, Palmerston North, New Zealand
In order to meet the learning needs from various types of students, computer aided education systems try to include new methods to provide personalized education to every student. From the early 1970s, a lot of adaptive educational systems have been created to provide training on a variety of subjects. Combined with the Internet, the adaptive educational systems have become web-based and even more popular. Recently, the development of mobile technology has made the web-based adaptive educational systems accessible through mobile phones. It is necessary that the students can also receive adaptive educational contents on mobile phones. This research project investigated the possible student's preference differences between Personal Computer (PC) and mobile phone, and then proposed a student profile transformation framework to address such differences. This research project conducted two surveys on the student profile transformation between PC and mobile phone. A demo web-based educational system that could be accessed from both PC and mobile phone was also developed for participants of the surveys to give more real and precise responses. Based on Felder-Silverman Learning Style Theory (Felder, 1993; Felder & Silverman, 1988) and the results of the surveys, this thesis proposes a student profile template and a student profile transformation framework, which both fully considered the influences of device capabilities and locations on students' preferences on mobile phones. Furthermore, the proposed framework integrates a solution for unsupported preferences and preference conflicts. By implementing the proposed template and framework, the students' preference changes between PC and mobile phone are automatically updated according to various device capabilities and locations, and then the students can receive adaptive educational contents that meet their updated preferences
The design and implementation of an adaptive e-learning system
This paper describes the design and implementation of an adaptive e-learning system that provides a template for different learning materials as well as a student model that incorporates five distinct student characteristics as an aid to learning: primary characteristics are prior knowledge, learning style and the presence or absence of animated multimedia aids (multimedia mode); secondary characteristics include page background preference and link colour preference. The use of multimedia artefacts as a student characteristic has not previously been implemented or evaluated.
The system development consists of a requirements analysis, design and implementation. The design models including use case diagrams, conceptual design, sequence diagrams, navigation design and presentation design are expressed using Unified Modelling Language (UML). The adaptive e-learning system was developed in a template implemented using Java Servlets, XHTML, XML, JavaScript and HTML. The template is a domain-independent adaptive e-learning system that has functions of both adaptivity and adaptability
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
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Towards adaptive e-learning applications based on Semantic Web Services
The current state of the art in supporting E-Learning objectives is primarily based on providing a learner with learning content by using metadata standards like ADL SCORM 2004 or IMS Learning Design. By following this approach, several issues can be observed including high development costs due to a limited reusability across different standards and learning contexts. To overcome these issues, our approach changes this data-centric paradigm to a highly dynamic service-oriented approach. By following this approach, learning objectives are supported based on a automatic allocation of services instead of a manual composition of learning data. Our approach is fundamentally based on current Semantic Web Service (SWS) technology and considers mappings between different learning metadata standards as well as ontological concepts for E-Learning. Since our approach is based on a dynamic selection and invocation of SWS appropriate to achieve a given learning objective within a specific learning context, it enables the dynamic adaptation to specific learning needs as well as a high level of reusability across different learning contexts
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