Printer Friendly

Authoring of learning objects in context.

Learning objects and content interchange standards provide new possibilities for e-learning. Nevertheless the content often lacks context data to find appropriate use for adaptive learning on demand and personalized learning experiences. In the Remotely Accessible Field Trips (RAFT) project mobile authoring of learning content in context has shown the relevancy of contextual metadata for flexible access to learning objects. This article describes approaches for extending current metadata schemas with context metadata that can be captured together with the assets on the fly, giving them a learning context.


New requirements for personalized adaptive learning include (Information Society Technologies Priority, 2003) development of semantic-based and context-aware systems to acquire, organise, personalise, share, and use the knowledge embedded in web and multimedia content, achievement of semantic interoperability between heterogeneous information resources and services, and pioneering intelligent content, which is self-describing, adaptive to context and user needs, and exhibits a seamless interaction with its surroundings and the user. This research line addresses the boundaries between knowledge and content, combining new content architectures with emerging knowledge technologies to progress towards context-aware, self-describing, and adaptive "atomic" content objects that can seamlessly aggregate to create new content and services, for which the traditional boundaries of different media cease to exist.

Mobile technologies and ubiquitous computing raise new requirements regarding extensions on current standards and exchange formats for contextualisation of resources. The current metadata sets should be extended for capturing and handling additional context data. Authoring toolkits for creating contextualized materials should support contextualized collaboration and live interaction among users performing various roles. One of their primary objectives is to generate as much metadata as possible automatically, based on the current context and by means of sensors. This will enable more precise retrieval of the data when resources are elaborated by users or provided to learners.

In the last years several initiatives researched scenarios for learning and mobile information support in the classroom. According to Kling (2003), the classroom and research in the classroom might be one of the key drivers for a next generation of social software. The classroom gives a variety of scenarios and situations where ad hoc collaboration and the contextualization of information play an important role. In a study conducted in the PEP program (Tatar, Roschelle, Vahey, & Penuel, 2003) 84% of teachers strongly agreed that the quality of teaching was improved by handheld devices in the classroom. New possibilities where seen in the live interaction about data and the reflection about easily exchangeable and copied data sets. In the context of the m-Learn project, user studies analyzed the different scenarios that were relevant in the working context for learning (Atewell & Savoll-Smith, 2000). Most of such studies show a high potential and acceptance for supporting new forms of mobile and contextualized learning approaches in the classroom. From our point of view, the integration of focused applications with specialized interfaces and their integration in more complex task contexts are crucial for the design of contextualized learning experiences.

In the following, we mention personalization issues and challenges, describe our experience with contextualized learning in the Remotely Accessible Field Trips (RAFT) project, and outline an extended system architecture related to learning objects.


The major aims of personalized adaptive learning are improvements in effectiveness and efficiency of learning together with higher learner satisfaction. To increase the quality of technology enhanced learning it is important to distinguish what should be adapted, to what features should it be adapted, and how should it be adapted.

Additionally, to the traditional adaptive factors such as adaptive content selection, adaptive navigation support, and adaptive presentation, we should consider some new ones, like adaptive learning activity selection, adaptive resource recommendation, and adaptive service provision. According to the Adaptive Hypermedia Application Model (AHAM, [DeBra, Houbon, & Wu, 1999]) it is common to base the adaptation process on the domain model and the user (learner) model, possibly enhanced by the goal (task) model, but to provide adaptive services in mobile and ubiquitous computing the context model has to be added (Figure 1). To specify the adaptation itself in a reusable way the adaptation model has to be separated from the domain one and in educational settings enhanced by a pedagogical model (more generally it might be an activity or scenario model).

Integration of context modeling and user modeling with adaptation (Figure 2) will enable new forms of personalized and adaptive learning experiences. The user and context model specify to what parameters the application should adapt. The main challenges regarding context management include:

* extensions on current standards and exchange formats for contextualisation of resources;

* automatic acquisition of context metadata;

* understanding contexts and situated cognition;

* creation of tools for development of contextualized applications; and

* designing context-based activities involving groups of users interacting within a set of collaborative environments.




In the European project RAFT (RAFT, 2002), the consortium has created learning tools for field trips in schools. The system supports a variety of learners with different tasks either in the classroom or in the field. The main objectives of the RAFT project were to demonstrate the educational benefits and technical feasibility of remote field trips, to establish extensions on current learning material standards and exchange formats for contextualization of learning material (this is combined with the embedding of learning and teaching activities in an authentic real world context), and to establish new forms of contextualized learners' collaboration with real time video conferencing and audio communication in authentic contexts.

Through the RAFT trials, different phases (Figure 3) for preparing the field trip, experiencing the field trip in the classroom and in the field, and the evaluation after the field trip were identified. In those phases a variety of stakeholders and participants contribute to the field trip and take an active role in it.

From the usage of the RAFT applications by end users the following main activities can be considered as new qualities of contextualized learning approaches:

* Cooperative task work: The distributed work on a task focuses the interaction and communication between the learners, technology get into the background, the curiosity about the given task and its exploration in physical and knowledge space become the main interest. The context in this sense allows the learners to immerse in the learning subject at hand.


* Active construction of knowledge and learning materials: Users are much more motivated when "self made" learning material get integrated in the curriculum.

* Field trips are a blended learning process: Teachers need to specify preparation materials, distribute user roles, and define field and classroom tasks. After the field trip the collected materials need to be reviewed and archived in standardized formats to ensure reuse and quality assurance.

The RAFT tools support different phases of the whole process: preparation, field trip activity, or evaluation. Therefore different interfaces and widgets give the user access to the backend system and the live communication channels. The interface and interactions design depends heavily on the type of activity. During the field trip the selection of information and collaboration tools is based on the current position and user role. The implementation of different interfaces is not based on a software solution for intelligent rendering of interface components, but is developed specially for the different roles and role specific devices to fulfill the assigned tasks. The RAFT tools can be seen as different components in a blended learning process that is distributed in time, location, and social context in the different phases of the field trip.

The basic architecture in RAFT enables the creation of various widgets using different modalities for input and output. All messages go in a common backend by way of a web services interface and can be used with different rendering and display widgets. This ensures the most flexibility for communicating between different interfaces in the classroom and the field. Furthermore all clients are notified by a notification service when new messages are available and can subscribe to different communication channels.


Development of contextualized learning materials was a major focus in RAFT. Besides the traditional learning object metadata (LOM, SCORM) attached to materials, additional metadata were required for contextualized learning objects. Such metadata include information about the location where the materials were collected, information regarding the current time, or maybe the weather conditions on that day.

Already in an early prototype, called Mobile Collector (Kravcik, Kaibel, Specht, & Terrenghi, 2004), the learner could annotate a photo (Figure 4). The photo was shown together with all its metadata. The learner could assign the name and the related concepts (keywords) to the photo, or record audio annotation. Because of the difficulties with text input while on the move, the user could assign the concepts by simply checking them in a predefined list. Based on this manual indexing users could easily find all the photos related to a particular concept.


Later on, the RAFT consortium developed a specialized framework for collecting context sensor data (Figure 5) in real time together with the learning materials and used the context metadata to make the collected information accessible to other participants of a field trip (Specht, Kaibel, & Apelt, 2005). As an example, a learner performing the scout role can collect small pictures or audio annotations and tag them with the location information (sensor metadata) from a GPS device. This information instantly appears on the task lists of other team members and is highlighted in the user interface. Traditional learning object metadata can be helpful for adaptive methods on sequencing and selecting the appropriate learning objects for a learner. Context metadata enable new approaches for structuring and accessing shared assets and learning objects.


To realize the support for different metadata schemas and their usage in various learning scenarios it was needed to extend the existing learning object architecture with several new components:

* Flexible metadata schema support: In the LCMS ALE (Kravcik, Specht, & Oppermann, 2004; Kravcik & Specht, 2004) we implemented a framework to support different metadata schemas and a tool allowing creators of learning content to choose from different metadata schemas that are available.

* Sensor integration and sensor server: Based on the context metadata available on a field trip we integrated the possibility to record sensor measurements (Zimmermann, Lorenz, & Specht, 2005; Zimmermann, Specht, & Lorenz, in press) and combine them with data collections.

* Context metadata based filtering and presentation of learning objects: For simple mobile exploration tools based on PDAs or mobile devices we implemented content presentation tools that allow filtering of information based on contextual metadata.

As one example learners could browse a database of pictures in a biology field trip filtered by the location and the time of the year. Using this approach students could explore and learn about simple questions for example, "Which flowers grow here at a certain time of the year?"; additionally metadata such as the precise time when the picture was taken and the weather conditions on that day can give interesting materials for exploring and learning about important factors of flower growth.


The RAFT project revealed several technical and interaction issues related to the design of learning experiences for mobile and collaborative learning. Beside the backend technology that enables the combination of different client technologies from electronic whiteboards to mobile telephones, the synchronization and notification of heterogeneous clients accessing a persistent and consistent learning object repository became very important. Additionally, the distribution over the different phases of the field trip (preparation, field trip activity, and evaluation) appears to be an important aspect of nomadic activities for learning and exploration.

First insights have been gained on the extension of current learning material standards based on the semiautomatic collection of contextual metadata and their combination with assets and learning objects. As mentioned in (Duval et al., 2005) it is important to generate as much relevant metadata (both objective and subjective) as possible automatically, but also to enable manual creation of certain metadata. Metadata should enrich not only learning objects (and assets), but also queries to improve the precision and recall of information retrieval on one side, as well as personalization and adaptation on the other. Metadata generation has to be supported also by such facilities as inheritance, copying, default values, and by automatic generation from the (physical and semantic) context through appropriate sensors. User tracking is a way to collect usage metadata, especially important for adaptive delivery of learning.

According to our results, we suppose that integrating context modeling and user modeling will enable new forms of learning experiences, that mobile situated collaboration is a key for integrated learning support in nomadic activities, and that multimodal interfaces are crucial for ubiquitous information access and contextualized learning experiences. These hypotheses have to be further tested and verified.


Atewell, J, & Savoll-Smith, C. (2003). m-Learning and social inclusion--focussing on learner and learning. In Proceedings of MLEARN 2003. London: Learning and Skills Development Agency.

De Bra, P., Houben, G. J., & Wu, H. (1999) AHAM: A dexter-based reference model for adaptive hypermedia. Proceedings of the ACM Conference on Hypertext and Hypermedia. ACM, (pp. 147-156).

Duval, E. et al. (2005) A Learning Object Manifesto, this issue Information Society Technologies Priority (2002). Retrieved October 1, 2005, from

Kling, A. (2003) Social software. Tech Central Station. Retrieved October 1, 2005, from

Kravcik, M., Kaibel, A., Specht, M., & Terrenghi, L. (2004). Mobile collector for field trips. Educational Technology & Society, 7(2), 25-33. Retrieved October 1, 2005, from

Kravcik, M., & Specht M. (2004). Authoring adaptive courses--ALE approach. Advanced Technology for Learning, 1(4), 215-220. Retrieved October 1, 2005, from

Kravcik, M., Specht, M., & Oppermann, R. (2004). Evaluation of WINDS authoring environment. In P. De Bra & W. Nejdl (Eds.), Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems, (pp. 166-175), Eindhoven, The Netherlands. Heidelberg, Germany: Springer. Retrieved October 1, 2005, from

Remotely Accessible Field Trips (RAFT) Project (2002). Retrieved October 1, 2005, from

Specht, M., Kaibel, A., & Apelt, S. (2005). Extending LCMS for remote accessible field trips in RAFT. In Proceedings of the Third IEEE International Conference on Perasive Compputing and Communications Workshop, (pp. 302-306). Retrieved October 1, 2005, from

Tatar, D., Roschelle, J., Vahey, P., & Penuel, W.R. (2002). Handhelds go to school: Lessons learned. IEEE Computer, 36(9), 30-37.

Zimmermann, A., Lorenz, A., & Specht, M. (2005). Applications of a context-management system. In A.K. Dey, B. Kokinov, D. Leake & R. Turner (Eds.), Proceedings of the Fifth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-05), (pp. 556-569), Paris, France.

Zimmermann, A., Specht, M., & Lorenz, A. (in press). Personalization and context-management. User Modeling and User Adaptive Interaction.


Open University of the Netherlands, The Netherlands


Fraunhofer Institute for Applied Information Technology, Germany
COPYRIGHT 2006 Association for the Advancement of Computing in Education (AACE)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2006, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

Article Details
Printer friendly Cite/link Email Feedback
Author:Kravcik, Milos
Publication:International Journal on E-Learning
Geographic Code:1USA
Date:Jan 1, 2006
Previous Article:Learning group formation based on learner profile and context.
Next Article:Modularization and structured markup for learning content in an academic environment.

Related Articles
Patterns for E-learning content development.
Adopting SCORM 1.2 standards in a courseware production environment.
Learning Designer[TM]: a theory-based SCORM-compliant content development tool.
Modularization and structured markup for learning content in an academic environment.
A metadata profile to establish the context of small learning objects: the slicing book approach (1).
A knowledge-based approach to describe and adapt learning objects.
Progressive inquiry learning object templates (PILOT).
Towards next generation activity-based learning systems.
From traditional to constructivist epistemologies: a proposed theoretical framework based on activity theory for learning communities.
Developing interactive learning objects for a Computing Mathematics module.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters