Zeynel Cebeci

Using Podcasts as Audio Learning Objects

Podcasting is an audio content syndication through RSS feeds in the audioblogs. As a new application of audioblogging, podcasting uses the enclosures in RSS feeds for syndication and distribution of audio content to mobile music players on the Web. Despite the advantages of podcasting, there is a need for research that focus on the use of podcasts as learning objects. Incorporating podcasts into e-learning systems require some design and translation work to achieve the pedagogical needs. This paper presents an introductory investigation on approaches to tailor and use audio podcasts as learning objects in learning management systems and learning object repositories.

Tree View Editing Learning Object Metadata

This paper introduces and examines an authoring tool called as “TreeLom” for producing the metadata compatible to IEEE LOM draft standard. TreeLom, has been developed with MS .NET framework technology, is an application of XML binding of the LOM. Its tree view editing interface provides rapid data input in building learning object metadata.

A novel technique for fast determination of K in partitioning cluster analysis

The input argument k refers to the number of clusters is needed to start all of the probabilistic and possibilistic partitioning algorithms. Although some progress has been made toward its solution, determining this user-specified argument is still one of the main issues in partitioning cluster analysis. Therefore, fast and even automated techniques are needed for determining k in partitioning clustering. In this paper, for determination of k, we proposed the KPEAKS, a simple and fast technique based on the descriptive statistics of peak counts of the features for clustering multidimensional datasets. The experiments on the synthetic and real datasets revealed that the mean of the largest two peak counts and the mean of third quartile and maximum peak count of the features can be successfully used for the estimates of k.

Validation of fuzzy and possibilistic clustering results

Unsupervised fuzzy clustering is an important tool for finding the meaningful patterns in data sets. In fuzzy clustering analyses, the performances of clustering algorithms are mostly compared using several internal fuzzy validity indices. However, since the well-known fuzzy indices have originally been proposed for working with membership degrees produced by the traditional Fuzzy c-means Clustering (FCM) algorithm, these indices cannot be used for possibilistic algorithms that produce typicality matrices instead of fuzzy membership matrices. Even more, the variants of FCM and PCM such as Possibilistic Fuzzy C-means (PFCM) and Fuzzy Possibilistic C-means (FPCM) simultaneously result with probabilistic and possibilistic membership degrees. Thus, some kind of validity indices are needed for working with both of these results. For this purpose, a few extended and generalized validity indices has been proposed in recent years. In this paper, the performances of these indices were examined for validating the clustering results from Unsupervised Possibilistic Fuzzy Clustering (UPFC), FCM and PCM algorithms. The findings showed that generalized versions of the fuzzy validity indices based on normalization of typicality degrees can be successfully used to validate the results from PCM, UPFC and the variants of FCM and PCM.

Agroecology for Farmers: The Linguistic Issue

Agroecology means that agriculture is a part of ecological systems. Agroecology thus promotes biodiversity and support multicultural production. Farmers are benefiting from the digital revolution that allow access to agroecological knowledge. Although internet access to information resources is becoming less problematic, the issue of language barrier is particularly critical. This chapter therefore focuses on the need for farmers to access useful information, with focus on language barriers. The linguistic issue is addressed using the Organic.Edunet experience (www.organic-edunet.eu). Organic.Edunet is a learning portal that provides access to high-quality and trusted digital learning resources on organic agriculture and agroecology. These resources are used by students, teachers and farmers, as well as the general public interested in the subject. Organic.Edunet is used in this chapter as a use-case for analysing the benefit of truly multilingual portal in the agroecological field. Automated multilingual services introduced in the portal are described as well as the study of the analytics that shows the need to access information without the language barrier. A professional approach is described for demonstrating the benefit for farmers and teachers to use such thematic and multilingual portal. Then the importance of new content is mentioned to ensure the update of the information as well as the sustainability of such tools.

TrAgLor, an implementation of IEEE LOM draft standard in agriculture and life sciences

The Turkish Agricultural Learning Objects Repository (TrAgLor) is a multilingual IEEE LOM Draft Standard compatible Learning Objects (LO) repository. It has been developed as a test-bed to enable the storage, search and retrieval of objects or their metadata related with agriculture, food, veterinary and environmental sciences for learners and educators in Turkey. The main purpose for developing TrAgLor was to implement a demonstrative repository system that focuses on various technical aspects related to storage, retrieval and exchange of LO produced for Turkish higher Institutions. Accordingly, this paper primarily aims to introduce and discuss the architecture and functions of the repository on LO and metadata management, searching and interoperability in addition to some preliminary findings on usage and contribution levels to the repository.