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

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    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

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    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

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    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

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    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

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    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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