The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.

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This article investigates a scenario of reuse in which existing learning resources serve as preliminary products for the creation of new learning resources. Authors should be able to reuse learning resources and also parts of them at different levels of granularity in a modular way. The requirements of multigranularity reuse are analyzed and compared to existing solutions. A concept for modular, multigranularity reuse is presented in this article. It is also shown how this kind of reuse can be achieved in practise.

Crowdsourcing platforms support the assignment of jobs while relying on the workers' search capabilities. Recommenders can support the workers' decisions to improve quality and outcome for both worker and requester. A precedent study showed, that many workers expect to get tasks recommended, which are similar to previously finished ones. In order to create genuine task recommendation, similarities between tasks have to be identified and analyzed. Therefore, this work provides an empirical study about how workers perceive task similarities. The perceived task similarities may vary between workers with different cultural background and may depend e.g. on the complexity, required action or the requester of the task.