For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers Googling papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply Googling articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.<p>In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their cognitive goals.<p>It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. <p>The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. <p>Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. <p>Finding a good paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance.

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers Googling papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply Googling articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.<p>In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their cognitive goals.<p>It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. <p>The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. <p>Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. <p>Finding a good paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance.