A Wiki-Based Teaching Material Development Environment with Enhanced Particle Swarm Optimization

By Lin, Yen-Ting; Lin, Yi-Chun et al. | Educational Technology & Society, April 2013 | Go to article overview

A Wiki-Based Teaching Material Development Environment with Enhanced Particle Swarm Optimization


Lin, Yen-Ting, Lin, Yi-Chun, Huang, Yueh-Min, Cheng, Shu-Chen, Educational Technology & Society


Introduction

Over the last decade, e-learning has become widely applied in the educational domain. A major aim of e-learning is to increase interoperability and reusability of learning objects. Thanks to the establishment of various standards such as IEEE Learning Object Metadata (LOM) and Sharable Content Object Reference Model (SCORM), several authoring tools have been developed to assist instructors in producing and packaging learning objects with metadata that are compliant with the standards to enhance the interoperability. For example, in 2005, Garcia and Garcia complied with LOM to propose an authoring tool, namely HyCo, to facilitate the composition of hypertext, which are stored as semantic learning objects in a backend database (Garcia & Garcia, 2005). Furthermore, Wang et al. (2007) designed a rich-client authoring environment for creating learning contents that are compatible with various e-learning standards without redundant efforts. Additionally, Kuo and Huang (2009) presented an authoring tool that can produce adaptable learning content to support both e-learning and m- learning, complying with SCORM standard. Although the above approaches significantly enhanced the interoperability of the learning objects, the support of the reusability for such learning objects is not enough.

In fact, teachers often have to design and produce individual teaching materials for specific subject matter by themselves. Moreover, a typical approach to content design consists of five stages, known as ADDIE (ADDIE, 2004), short for analysis, design, develop, implement, and evaluate, and this process requires that teachers spend a considerable amount of time and effort. Furthermore, such costs obviously increase unnecessarily when different individuals are working to develop similar teaching materials for the same course units simultaneously. Therefore, e- learning materials could be very useful resources for further education, because instructors can reuse existing learning objects to re-produce specific teaching materials more efficiently and effectively for different contexts.

As mentioned above, in order to solve this we first have to consider how to assist instructors in assembling such materials, and one problem with this is the huge amount of learning objects that may need to be considered. Furthermore, in order to form well-structured teaching materials, instructors also have to take more than one factor or criterion into account simultaneously, adding to their already challenging workload. Although previous studies have applied query expansion techniques to address the first problem, they do not take multi-criteria into account to fit the real-world situation (Jou & Liu, 2011; Shih, Tseng, & Yang, 2008).

Bearing this in mind, this study aims to develop a rapid prototyping approach by employing particle swarm optimization (PSO) with multi-criteria to accelerate the development of drafts of teaching materials, as well as utilizing wiki-based techniques to enhance the revision quality of the materials thus produced. The ultimate aim of the study is to reduce the time, effort, and cost associated with the development of high-quality teaching materials.

Background and related work

Particle swarm optimization

PSO is a population-based optimization algorithm. Kennedy and Eberhart proposed the algorithm in 1995, inspired by the social behaviors of fish schooling and bird flocking, because they thought swarm intelligence could increase both the speed and the success rate for certain processes (Kennedy & Eberhart, 1995).

To carry out the PSO, each investigator has to formulate a fitness function according to the requirements of different optimization problems. Following this, a swarm of particles is generated and then distributed over a problem space, where each particle represents a potential solution to the optimization problem and is able to "remember" its own past status. …

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