Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Improved computation of individual ZPD in a distance learning system

This paper builds upon theoretical studies in the field of social constructivism. Lev Vygotsky is considered one of the greatest representatives of this research line, with his theory of the Zone of Proximal Development (ZPD). Our work aims at integrating this concept in the practice of a computer-assisted learning system. For each learner, the system stores a model summarizing the current Student Knowledge (SK). Each educational activity is specified through the deployed content, the skills required to tackle it, and those acquired, and is further annotated by the effort estimated for the task. The latter may change from one student to another, given the already achieved competence. A suitable weighting of the robustness (certainty) of student’s skills, stored in SK, and their combination are used to verify the inclusion of a learning activity in the student’s ZPD. With respect to our previous work, the algorithm for the calculation of the ZPD of the individual student has been optimized, by enhancing the certainty weighting policy, and a graphical display of the ZPD has been added. Thanks to the latter, the student can get a clear vision of the learning paths that he/she can presently tackle. This both facilitates the educational process, and helps developing the metacognitive ability self-assessment

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

Graduate Catalog, 2005-2006

https://scholar.valpo.edu/gradcatalogs/1032/thumbnail.jp

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Graduate Catalog, 2002-2003

https://scholar.valpo.edu/gradcatalogs/1029/thumbnail.jp