How to represent adaptation in e-learning with IMS learning design

Burgos, Daniel; Tattersall, Colin; Koper, Rob

doi:10.1080/10494820701343736

Cited by 76 publications

(51 citation statements)

References 18 publications

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“…Moreover, the definition of properties and conditions makes possible the implementation of adaptive learning material, as pointed out in (Towle & Halm, 2005, Ch. 12) and (Burgos, Tattersall, & Koper, 2007): course participants may follow different learning paths, or work at different paces with the same course material.…”

Section: Learning Scriptsmentioning

confidence: 99%

Addressing drop-out and sustained effort issues with large practical groups using an automated delivery and assessment system

de-la-Fuente-Valentín

Pardo

Kloos

2013

Computers & Education

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Abstract:The acquisition of programming skills specially in introductory programming courses poses an important challenge for freshmen students of engineering programs. These courses require students to devote a sustained effort during the whole course and a failure to do so may contribute to not passing the course. However, it is difficult for the teaching staff to deploy measures to enforce a pattern of continuous work without significantly increasing the required management tasks. A significant reduction of this workload can be achieved with the automation of time consuming tasks such as the delivery of activities or submission grading. This paper presents a case of study where a technology based orchestration of learning scripts was applied in a large enrollment course to promote student sustained effort through the course and keep the workload on teaching staff within reasonable margins. The orchestration system, based on IMS Learning Design and Generic Service Integration, automatically evaluated the student submissions and gradually unlocked the following activities depending on the received results. Such system was used during a semester supporting 425 students and 8 instructors. The analysis of the case of study followed a mixed method based on qualitative and quantitative data, and revealed a significant reduction of the orchestration workload on the teaching staff, allowing the strategy of continuous work to be applied in a course with high enrollment. Additionally, the application of these techniques show statistically significant differences in the drop out rate with respect to previous editions of the course.

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Section: Learning Scriptsmentioning

confidence: 99%

Addressing drop-out and sustained effort issues with large practical groups using an automated delivery and assessment system

de-la-Fuente-Valentín

Pardo

Kloos

2013

Computers & Education

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“…The IMS LD framework also supports the creation of adaptive content material [4]. The offered mechanism is based on the use of the so-called properties and conditions.…”

Section: Course Flow Management Technologiesmentioning

confidence: 99%

System Orchestration Support for a Collaborative Blended Learning Flow

de-la-Fuente-Valentín

Pérez‐Sanagustín

Santos

et al. 2012

Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

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Abstract. Portable and interactive technologies are changing the nature of collaborative learning practices and open up new possibilities for Computer Supported Collaborative Learning (CSCL). Now, activities occurring in and beyond the classroom can be combined and integrated leading to a new type of complex collaborative blended learning scenarios. However, to organize and structure these scenarios is challenging and represent a workload for practitioners, which hinder the adoption of these technology-enhanced practices. As an approach to alleviate this workload, this paper proposes a proof of concept of a technological solution to overcome the limitations detected in an analysis of an actual collaborative blended learning experiment carried out in a previous study. The solution consists on a Unit of Learning suitable to be instantiated with IMS Learning Design and complemented by a Generic Service Integration system. This chapter also discusses to which extent the proposed solution covers the limitations detected in the previous study and how useful could be for reducing the orchestration effort in future experiences.

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“…IMS LD supports the formalization of a wide range of pedagogical models, including collaborative and adaptive learning flows [33,34]. The course flow of the experience described in this document was formalized as a UoL so that all the activities are orchestrated and managed by an IMS LD player.…”

Section: Technological Support For the Orchestrationmentioning

confidence: 99%