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

Personalised trails and learner profiling in an e-learning environment

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

Degree of Scaffolding: Learning Objective Metadata: A Prototype Leaning System Design for Integrating GIS into a Civil Engineering Curriculum

Digital media and networking offer great potential as tools for enhancing classroom learning environments, both local and distant. One concept and related technological tool that can facilitate the effective application and distribution of digital educational resources is learning objects in combination with the SCORM (sharable content objects reference model) compliance framework. Progressive scaffolding is a learning design approach for educational systems that provides flexible guidance to students. We are in the process of utilizing this approach within a SCORM framework in the form of a multi-level instructional design. The associated metadata required by SCORM will describe the degree of scaffolding. This paper will discuss progressive scaffolding as it relates to SCORM compliant learning objects, within the context of the design of an application for integrating Geographic Information Systems (GIS) into the civil engineering curriculum at the University of Missouri - Rolla

[[alternative]]Enhancing On-Line Learning Using Standards and Web 2.0 Technologies---Using Multimedia Course as an Example

計畫編號：NSC96-2524-S032-001研究期間：200708~200807研究經費：4,080,000[[sponsorship]]行政院國家科學委員

From collaborative virtual research environment SOA to teaching and learning environment SOA

This paper explores the extension of the CORE VRE SOA to a collaborative virtual teaching and learning environment (CVTLE) SOA. Key points are brought up to date from a number of projects researching and developing a CVTLE and its component services. Issues remain: there are few implementations of the key services needed to demonstrate the CVTLE concept; there are questions about the feasibility of such an enterprise; there are overlapping standards; questions about the source and use of user profile data remain difficult to answer; as does the issue of where and how to coordinate, control, and monitor such a teaching and learning syste

A note on organizational learning and knowledge sharing in the context of communities of practice

Please, cite this publication as: Antonova, A. & Gourova, E. (2006). A note on organizational learning and knowledge sharing in the context of communities of practice. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. September 12th, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.orgThe knowledge management (KM) literature emphasizes the impact of human factors for successful implementation of KM within the organization. Isolated initiatives for promoting learning organization and team collaboration, without taking consideration of the knowledge sharing limitations and constraints can defeat further development of KM culture. As an effective instrument for knowledge sharing, communities of practice (CoP) are appearing to overcome these constraints and to foster human collaboration.This work has been sponsored by the EU project TENCompetenc

On the use of case-based planning for e-learning personalization

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students’ requirements. myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. © 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156

Virtual learning process environment (VLPE): a BPM-based learning process management architecture

E-learning systems have significantly impacted the way that learning takes place within universities, particularly in providing self-learning support and flexibility of course delivery. Virtual Learning Environments help facilitate the management of educational courses for students, in particular by assisting course designers and thriving in the management of the learning itself. Current literature has shown that pedagogical modelling and learning process management facilitation are inadequate. In particular, quantitative information on the process of learning that is needed to perform real time or reflective monitoring and statistical analysis of students’ learning processes performance is deficient. Therefore, for a course designer, pedagogical evaluation and reform decisions can be difficult. This thesis presents an alternative e-learning systems architecture - Virtual Learning Process Environment (VLPE) - that uses the Business Process Management (BPM) conceptual framework to design an architecture that addresses the critical quantitative learning process information gaps associated with the conventional VLE frameworks. Within VLPE, course designers can model desired education pedagogies in the form of learning process workflows using an intuitive graphical flow diagram user-interface. Automated agents associated with BPM frameworks are employed to capture quantitative learning information from the learning process workflow. Consequently, course designers are able to monitor, analyse and re-evaluate in real time the effectiveness of their chosen pedagogy using live interactive learning process dashboards. Once a course delivery is complete the collated quantitative information can also be used to make major revisions to pedagogy design for the next iteration of the course. An additional contribution of this work is that this new architecture facilitates individual students in monitoring and analysing their own learning performances in comparison to their peers in a real time anonymous manner through a personal analytics learning process dashboard. A case scenario of the quantitative statistical analysis of a cohort of learners (10 participants in size) is presented. The analytical results of their learning processes, performances and progressions on a short Mathematics course over a five-week period are also presented in order to demonstrate that the proposed framework can significantly help to advance learning analytics and the visualisation of real time learning data

Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching

This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students’ learning behaviors in an online business course in Taiwan. The study scientifically identified students’ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers

Design and Implementation Strategies for IMS Learning Design

SIKS Dissertation Series No. 2008-27The IMS Learning Design (LD) specification, which has been released in February 2003, is a generic and flexible language for describing the learning practice and underlying learning designs using a formal notation which is computer-interpretable. It is based on a pedagogical meta-model (Koper & Manderveld, 2004) and supports the use of a wide range of pedagogies. It supports adaptation of individual learning routes and orchestrates interactions between users in various learning and support roles. A formalized learning design can be applied repeatedly in similar situations with different persons and contexts. Yet because IMS Learning Design is a fairly complex and elaborate specification, it can be difficult to grasp; furthermore, designing and implementing a runtime environment for the specification is far from straightforward. That IMS Learning Design makes use of other specifications and e-learning services adds further to this complexity for both its users and the software developers. For this new specification to succeed, therefore, a reference runtime implementation was needed. To this end, this thesis addresses two research and development issues. First, it investigates research into and development of a reusable reference runtime environment for IMS Learning Design. The resulting runtime, called CopperCore, provides a reference both for users of the specification and for software developers. The latter can reuse the design principles presented in this thesis for their own implementations, or reuse the CopperCore product through the interfaces provided. Second, this thesis addresses the integration of other specifications and e-learning services during runtime. It presents an architecture and implementation (CopperCore Service Integration) which provides an extensible lightweight solution to the problem. Both developments have been tested through real-world use in projects carried out by the IMS Learning Design community. The results have generally been positive, and have led us to conclude that we successfully addressed both the research and development issues. However, the results also indicate that the LD tooling lacks maturity, particularly in the authoring area. Through close integration of CopperCore with a product called the Personal Competence Manager, we demonstrate that a complementary approach to authoring in IMS Learning Design solves some of these issues