Object lessons : a learning object approach to e-learning for social work education

Learning objects are bite-sized digital learning resources designed to tackle the e-learning adoption problem by virtue of their scale, adaptability, and interoperability. The learning object approach advocates the creation of small e-learning resources rather than whole courses: resources that can be mixed and matched; used in a traditional or online learning environment; and adapted for reuse in other discipline areas and in other countries. Storing learning objects within a subject specific digital repository to enable search, discovery, sharing and use adds considerable value to the model. This paper explores the rationale for a learning object approach to e-learning and reflects on early experiences in developing a national learning object repository for social work education in Scotland

A learning object success story

This paper outlines an approach to designing a course entirely in learning objects. It provides a theoretical basis for the design and then presents evaluation data from a master’s level course using this design. It also describes several re-uses of the learning objects on other courses and in different contexts. Each learning object is conceived as a whole learning experience, thus avoiding many of the problems associated with assembling components of disparate kinds

One-shot learning of object categories

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully

Learning patterns and learner profiles in learning object design

The questions that Andy Heath has posed are challenging and need more time for reflection than is possible here. The questions posed will inform the research as it develops further. However, in the interests of debate we would like to give our initial replies. We agree in general with Andy Heath's assessment of the limitations of the approach we are adopting. We recognise that this approach uses a very limited response to AccessForAll principles: our Transformation Augmentation and Substitution Service (TASS) is localised, not global, and relies on a limited set of learning patterns matched against a small subset of the potentially infinite set of learner profiles. Our intention is certainly not to reproduce the considerable efforts of the IMS AccessForAll or Dublin Core Adaptability working groups, but to interpret their potential impact on the thinking of courseware designers, tutors and students

Automatic validation of learning object compositions

Course construction using reusable learning objects is becoming ever more popular due to its’ efficiency. The course creator who uses this methodology may face problems due to the fact that he or she is not as intimately involved in the creation of every element of the course. In this paper we discuss one such problem faced by course creator known as “the competency gap”. Here, we define the competency gap, explain how it can be identified and suggest ways of correcting the problem