Opening up! How to take full advantage of Open Educational Resources (OER) for Management Education

Terrasse, C., Marinova, B., Greller, W., Bitter-Rijpkema, M., & Schwertel, U. (2012). Opening up! How to take full advantage of Open Educational Resources (OER) for Management Education. In B. Rienties, P. Daly, S. Reeb-Gruber, K. Reid, & P. Van den Bossche, Proceedings of the 19th EDINEB Conference The Role of Business Education in a Chaotic World (pp. 145-149). Haarlem: FEBA ERD Press.General awareness and availability of Open educational resources (OER) have increased in recent years. Some institutions have already started incorporating OER in their strategies. However, compared to other educational fields, OER in Management and Business education have shown rather slow growth. This article discusses the incentives and barriers for the use of OER in Management and Business education. It reviews some useful accomplishments in the field, presents recommendations from various stakeholders and introduces the OpenScout initiative aimed at gathering OER and related tools for Management education

A novel approach towards skill-based search and services of Open Educational Resources

Ha, K.-H., Niemann, K., Schwertel, U., Holtkamp, P., Pirkkalainen, H., Börner, D. et al (2011). A novel approach towards skill-based search and services of Open Educational Resources. In E. Garcia-Barriocanal, A. Öztürk, & M. C. Okur (Eds.), Metadata and Semantics Research: 5th International Conference MTSR 2011 (pp. 312-323), Izmir, Turkey, October 12-14, 2011. Springer.Open educational resources (OER) have a high potential to address the growing need for training materials in management education and training. Today, a high number of OER in management are already available in a large number of repositories. However, users face barriers as they have to search repository by repository with different interfaces to retrieve the appropriate learning content. In addition, the use of search criteria related to skills, such as learning objectives and skill-levels is not generally supported. The European co-funded project OpenScout addresses these barriers by intelligently connecting leading European OER repositories and providing federated, skillbased search and retrieval web services. On top of this content federation the project supports users with easy-to-apply tools that will accelerate the (re-) use of open content

D1.1 Analysis Report on Federated Infrastructure and Application Profile

Kawese, R., Fisichella, M., Deng, F., Friedrich, M., Niemann, K., Börner, D., Holtkamp, P., Hun-Ha, K., Maxwell, K., Parodi, E., Pawlowski, J., Pirkkalainen, H., Rodrigo, C., & Schwertel, U. (2010). D1.1 Analysis Report on Federated Infrastructure and Application Profile. OpenScout project deliverable.The present deliverable aims to report on functionalities of the first step of the described process. In other words, the deliverable describes how the consortium will gather the learning objects metadata, centralize the access to existing learning resources and form a suitable application profile which will contribute to a proper and suitable modeling, retrieval and presentation of the required information (regarding the learning objects) to the interested users. The described approach is the foundation for the federated, skill-based search and learning object retrieval. The deliverable focuses on reporting the analysis of the available repositories and the best infrastructure that can support OpenScout’s initiative. The deliverable explains the motivations behind the chosen infrastructure based on the study of available information and previous research and literature.The work on this publication has been sponsored by the OpenScout (Skill based scouting of open user-generated and community-improved content for management education and training) Targeted Project that is funded by the European Commission’s 7th Framework Programme. Contract ECP-2008-EDU-42801

Learning Pathway Recommendation based on a Pedagogical Ontology and its Implementation in Moodle

When learners may select among different alternatives, or are guided to do so by an adaptive learning environment (ALE), it is generally meaningful to discuss the concept of different learning pathways. Pedagogically, these learning pathways may either be defined macroscopically, e.g. in terms of desired learning outcomes or competencies, or microscopically in terms of a didactical model for individual knowledge objects. In this contribution we consider such learning pathways from a pedagogical point of view and then establish a mathematical model for their traversal by a learner and for the analysis of his behavior. This model is implemented in a novel ALE provided by the EU FP7 project INTUITEL, introduced in its Moodle version as concrete example

Attempto controlled english

Abstract. Attempto Controlled English (ACE) – a subset of English that can be unambiguously translated into first-order logic – is a knowledge representation language. To support automatic reasoning in ACE we have developed the Attempto Reasoner RACE (Reasoning in ACE). RACE proves that one ACE text is the logical consequence of another one, and gives a justification for the proof in ACE. Variations of the basic proof procedure permit query answering and consistency checking. Reasoning in RACE is supported by auxiliary first-order axioms and by evaluable functions. The current implementation of RACE is based on the model generator Satchmo. 1 Reasoning in Natural Language Knowledge representation requires a language suited to the problem domain investigated. Traditional candidates for knowledge representation languages are natural language and formal languages. Discussing the pros and cons of natural language versus formal languages one could easily overlook that natural language is not on a par with these other languages, but plays an important and privileged role. First, it is the prototypical means of human communication, and also offers itself as a user-friendly means to interact with computers. Second, it serves as the meta-language for all other languages, informal or formal ones. Third, natural language effectively serves as its own meta-language, thus supporting representation, explanation, argumentation, and analysis all in one and the same notation. Fourth, natural language needs no extra learning effort, and – provided we exercise some care to avoid vagueness and ambiguity – is easy to use and to understand. Some researchers go as far as to consider natural language “the ultimate knowledge representation language ” [18]. Arguably, natural language also has a great potential for semantic web applications. This potential will be explored by the EU Network of Excellence “Reasoning on the Web with Rules and Semantics (REWERSE)”. Likewise, we use natural language to perform common sense reasoning that involves logical inference operations like deduction, abduction, and induction. Knowing that Every company that buys a machine gets a discount. Hardware Corporation is a company and buys a machine