Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in teaching materials. This challenge causes a semantic gap. Learners lack sufficient knowledge about the domain they are trying to learn about, so are unable to assemble effective keywords that identify what they wish to learn. This problem presents an intent gap. The work presented in this thesis focuses on addressing the semantic and intent gaps that learners face during an e-Learning recommendation task. The semantic gap is addressed by introducing a method that automatically creates background knowledge in the form of a set of rich learning-focused concepts related to the selected learning domain. The knowledge of teaching experts contained in e-Books is used as a guide to identify important domain concepts. The concepts represent important topics that learners should be interested in. An approach is developed which leverages the concept vocabulary for representing learning materials and this influences retrieval during the recommendation of new learning materials. The effectiveness of our approach is evaluated on a dataset of Machine Learning and Data Mining papers, and our approach outperforms benchmark methods. The results confirm that incorporating background knowledge into the representation of learning materials provides a shared vocabulary for experts and learners, and this enables the recommendation of relevant materials. We address the intent gap by developing an approach which leverages the background knowledge to identify important learning concepts that are employed for refining learners' queries. This approach enables us to automatically identify concepts that are similar to queries, and take advantage of distinctive concept terms for refining learners' queries. Using the refined query allows the search to focus on documents that contain topics which are relevant to the learner. An e-Learning recommender system is developed to evaluate the success of our approach using a collection of learner queries and a dataset of Machine Learning and Data Mining learning materials. Users with different levels of expertise are employed for the evaluation. Results from experts, competent users and beginners all showed that using our method produced documents that were consistently more relevant to learners than when the standard method was used. The results show the benefits in using our knowledge driven approaches to help learners find relevant learning materials.

Books and printed materials have been used as a major learning content for thousands of years. Nowadays, Smartphone is considered as an important tool for mobile learning. This study designed a learning environment with paper and Smartphone which seamlessly integrates printed materials and digital materials. The idea is to augment the traditional paper-based materials with plenty of digital materials available on the Internet. Furthermore, because both book and Smartphone are with very good mobility, the designed system is also very suitable for mobile learning. Two special mechanisms were designed for supporting learning activities, and their effects on learning performance were evaluated. The first one is learning recommendation which is generated based on the learning portfolio. The second one is automated content connection which can reduce the loading of context switching between printed materials and digital materials so as learners can be more concentrated on learning tasks. A system was designed and implemented for conducting an experiment and data collection. The statistic analysis shows that learning recommendation has a significant positive effect on learning performance; however, the effect of automated content connection on learning performance is not significant. Besides, the questionnaire survey also shows that learners have positive attitude toward the acceptance of the learning system designed in this study. Based on the results, some implications and suggestions are provided for researchers and instructors.