Quantitative Analysis of Learning Object Repositories Xavier Ochoa Escuela Superior Polit��cnica del Litoral (ESPOL), Guayaquil, Ecuador xavier@cti.espol.edu.ec Erik Duval Katholieke Universiteit Leuven (KULeuven), Leuven, Belgium erik.duval@cs.kuleuven.ac.be Learning Objects Repositories are the backbone of the Learning Object Economy, however little is known about their operation and much less on how that operation should be measured. Some Learnometrics (metrics for Learning Objects) need to be researched and implemented. This paper is a first step in that direction, performing a quantitative analysis of several aspects of Learning Object Repositories and Referatories in order to answer questions on their size, growth, contributor base and popularity. The analysis is performed on data from live repositories and referatories. The result of the analysis provides confirmation to long held belief, but also point out singular characteristics not previously discussed. Some of the findings are that LORs grow linearly, contribution distribution follows a power law and popularity of objects follows a log- normal distribution. The paper finalizes giving answer to the raised questions as well as the implication that these answer have in LOR community. 1. Introduction A Learning Object Repository (LOR) is digital library containing primarily educational material. Their main purpose is to enable the sharing of the material for its reuse in educational environments. They are the backbone of the proposed ���Learning Object Economy��� (Campbell, 2003), where learning objects are created, shared, reused, remixed, enriched and shared again. Technically, they are a computational system containing some type of metadata database, a digital file storage and an indexation and searching user interface. A special and popular form of LOR is the Learning Object Referatory. The Referatory does not store digital documents, it only contains metadata and links to relevant educational material hosted elsewhere (generally in the Web). Most part of the research on Learning Object Repositories has been focused on technical design issues like their architecture (Hatala et al, 2002) or their interoperability (Simon et al, 2004). On the other hand, little is known about more operational aspects of LORs. Some questions for which the literature does not provide an answer yet are: What is the typical size of a LOR? How LORs grow? What is the productivity of the average contributor? How many objects are accessed and how many times? Is there a Long Tail effect (Anderson, 2006) in the popularity of learning objects? The productivity of a contributor is related to the popularity of its objects? All this questions are relevant not only to measure the progress of the ���Learning Object Economy���, but also to provide ground information in which decisions about the architecture, interoperability strategies and growing planning should be based. This type of questions, formally called metrics, has been decisive for the advancement of other fields of knowledge. For example, software metrics make easier to compare different strategies for project development (Sharble, 1996), Scientometrics lead to schemas to measure the progress and impact of scientific research (Garfield, 2006), Webometrics enable the creation of PageRank (Page, 1998), the most famous metric for ranking Web sites. In the same line, Learnometrics, a proposed field to study metrics for Learning Objects, could help the research community to compare different system implementations, measure the impact that contributors or learning objects have in the learning community and to develop smarter tools to lower the barriers for Learning Object creation and reuse. To our knowledge, the most prominent attempts to characterize LORs and measure their characteristics are made by McGreal in (McGreal, 2007). He provides a comprehensive survey of existing LORs and classifies them in various typologies. Unfortunately, his analysis is mostly qualitative and cannot be used to answer the questions mentioned before. Other relevant studies are (Neven, 2002) and (Sicilia, 2005) where different LORs are also qualitatively compared. To obtain new knowledge to answer these questions, this paper will quantitatively analyze and compare representative LORs. The structure of this work is the following: Section 2 presents an analysis of the size distribution of 25 Repositories and 14 Refereatories. Section 3 analyzes the growth rate of 3 Repositories and 3 Referatories. Section 4 presents the study of the contribution distribution