Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Learning Efficiency and Effectiveness <s> In a cluster of many servers containing heterogeneous multimedia learning material and serving users with different backgrounds (e.g. language, interests, previous knowledge, hardware and connectivity) it may be difficult for the learners to find a piece of material which fit their needs. This is the case of the COLDEX project. Recommender systems have been used to help people sift through all the available information to find that most valuable to them. We propose a recommender system, which suggest multimedia learning material based on the learner’s background preferences as well as the available hardware and software that he/she has. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Learning Efficiency and Effectiveness <s> This paper introduces a ubiquitous computing environment in order to support the learners while doing tasks; this environment is called PERKAM (PERsonalized Knowledge Awareness Map). PERKAM allows the learners to share knowledge, interact, collaborate, and exchange individual experiences. It utilizes the RFID ubiquities technology to detect the learner’s environmental objects and location, then recommends the best matched educational materials and peer helpers in accordance with the detected objects and the current location. This environment provides the learner with Knowledge Awareness Map, which visualizes the space of the environmental objects that surround the learner, the educational materials space, and the peer helpers’ space. PERKAM system was implemented and an experiment was done in order to evaluate the system performance and the learner’s satisfaction. This paper illustrates the design, the implementation and the evaluation of this environment, and it focuses on Knowledge Awareness Map, which is personalized according to the learner’s current need and location. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Learning Efficiency and Effectiveness <s> This paper presents an augmented paper-based reading system which incorporated digital materials and the reading strategy of scaffolded questioning into printed materials to enhance studentsi¦ English reading comprehension. QR codes were printed on paper prints to provide direct access to digital resources and scaffolded questions, smart phones were used to scan the QR codes and display digital learning materials. The results showed that the reading strategy of scaffolded questioning significantly improved studentsi¦ English reading comprehension, but direct access to digital materials using QR codes did not. Implications of the study shed light on the future development of the system. <s> BIB003
The effect on learning efficiency and/or learning effectiveness has been evaluated for five systems. Evaluation results of Rogers et al.  indicate that suggesting resources during outdoor learning activities helps children to reflect and has a positive effect on the learning outcome. The impact on learning effectiveness of PERKAM BIB002 was measured in an experiment that consisted of several phases, including a comparison of the knowledge learned with and without the system. Among others, results indicate that more learners were able to successfully complete the task within a fixed time interval when using the system. In a similar way, Zhao et al.  evaluated learning effectiveness in comparison with learners who use a text book only. Results indicate that users receive better test scores and that learning interest increased with more than 10 minutes. The used metric is somewhat unconventional, but at least some attempt is made to measure the effect on learning. Teng et al. BIB003 measured effectiveness in a comparative evaluation and conclude that there is no significant difference with students who did not use the recommender. Evaluation results of APOSDLE BIB001 indicate that the impact on learning effectiveness and efficiency is dependent on the domain in which the system is deployed. In highly specialized domains where knowledge is well documented, the approach turned out to have a bigger impact on learning than in domains with few available resources. Whereas conclusions are in some cases still preliminary, at least some efforts have been made to assess the impact of contextaware recommender systems on learning.