21,453 research outputs found

    Personalization in cultural heritage: the road travelled and the one ahead

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    Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

    Active Techniques Implemented in an Introductory Signal Processing Course to Help Students Achieve Higher Levels of Learning.

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    Holding students to high standards and assessing, measuring and evaluating their learning with challenging, authentic problems in the midterm and final exams is the goal of the professors who teach core signal processing concepts. However, the heavy reliance of these subjects on mathematics makes it difficult for students to genuinely grasp the concepts and relate to a conceptual framework. Specifically, analyzing the signals and the functionality of systems in Fourier domain; separating the system level analysis from signal level analysis; and understanding how they are related in time domain and frequency domain are among the most challenging concepts. Students’ lower grades observed over past years in the introductory signal processing course exposed a potential disconnect between the actual level of learning and the high expectations set by the professors. In this paper, we present the active learning techniques that we implemented in one of the summer session offerings of this course in our department. The research explored Peer Instruction, pre-class reading quizzes and post-lecture quizzes. In addition to the mid and end of the quarter survey results, the comparison analysis of the grades students achieved in the active learning integrated course in the second summer session and the standard course offered in first summer session is discussed. According to our results, the developed techniques helped students in the active classroom perform significantly better than their peers participating in standard lectures when tested by challenging questions in their exams

    New generation E-learning technology by Web Services.

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    This paper discusses a new approach to build infrastructures for E-Learning systems for Learning Software Organizations on the basis of Web Services. A requirements context is developed to determine which type of E-Learning applications that can be Web Service Enabled. This is illustrated with a case study on an Encapsulated Software Teaching Environment. Additional facilities, such as didactical agents and deep personalization to facilitate Learning Software Organizations are discussed at the end.

    Predicting Student Success Using Digital Textbook Analytics in Online Courses

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    In the digital era, students are generating and institutions are collecting more data than ever before. With the constant change in technology, new data points are being created. Digital textbooks are becoming more popular, and textbook publishers are shifting more of their efforts to creating digital content. This shift creates new data points that have the potential to show how students are engaging with course material. The purpose of this correlational study is to determine if digital textbook usage data, pages read, number of days, reading sessions, highlights, bookmarks, notes, searches, downloads and prints can predict student success. This study used a multiple regression to determine if digital textbook usage data is a predictor of course or quiz success in five online undergraduate courses at a private liberal arts university. The analysis used digital textbook data from VitalSource and consisted of 1,602 students that were enrolled in an eight-week online course at a private liberal arts university. The analysis showed that there is a significant relationship between digital textbook usage data and total points earned and average quiz grade. This study contributes to the limited knowledge on digital textbook analytics and provides valuable insight into how students engage with digital textbooks in online courses

    Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches

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    Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described

    Continued Progress: Promising Evidence on Personalized Learning

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    The findings are grouped into four sections. The first section on student achievement finds that there were positive effects on student mathematics and reading performance and that the lowest-performing students made substantial gains relative to their peers. The second section on implementation and the perceptions of stakeholders finds that adoption of personalized learning practices varied considerably. Personalized learning practices that are direct extensions of current practice were more common, but implementation of some of the more challenging personalized learning strategies was less common. The third section relates implementation features to outcomes and identifies three elements of personalized learning that were being implemented in tandem in the schools with the largest achievement effects. Finally, the fourth section compares teachers' and students' survey responses to a national sample and finds some differences, such as teachers' greater use of practices that support competency-based learning and greater use of technology for personalization in the schools in this study with implementation data

    Implementation of Skill-Based, Transdisciplinary Competencies and Their Influence on Teacher Practice

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    Competency-based education has been a growing movement in K-12 education but faces several implementation challenges. In addition, discussion in the field is lacking related to the competency-based principle: learning outcomes emphasize the authentic application of knowledge. New to the competency-based field are proficiencies called competencies, which are skill-based, transdisciplinary proficiencies that are structured across a continuum of varying performance levels. This study employed a case study methodology utilizing teacher and educational leader interviews as well as artifacts to explore the implementation of competencies in a competency-based organization and two schools that partnered with it. This study specifically investigated how competencies are implemented at these schools, how competencies influence teacher practice, and implementation challenges. Major themes in the findings include a comprehensive description of: (1) competencies and their deliberate connection to authentic, agentic learning experiences, (2) a dynamic of push and pull between competencies, content, and project-based learning, (3) notable mindsets amongst teachers, (4) characteristics of classroom preparation and practices, (5) professional development, and (6) challenges. This study concludes with implications that address: (1) increasing teacher capacity for competency-based education, (2) developing structures to support authenticity and agency, (3) using competencies as proficiencies and as a tool for teaching, (4) the intersection to other educational fields including social & emotional learning, project-based learning, and service learning, (5) potential solutions to identified challenges, and (6) system-wide structures that may better support competency-based education at scale
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