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With the increased popularity of electronic textbooks, there is a growing interest in developing a new generation of “intelligent textbooks,” which have the ability to guide readers according to their learning goals and current knowledge. Intelligent textbooks extend regular textbooks by integrating machine-manipulable knowledge, and the most popular type of integrated knowledge is a list of relevant concepts mentioned in the textbooks. With these concepts, multiple intelligent operations, such as content linking, content recommendation, or student modeling, can be performed. However, existing automatic keyphrase extraction methods, even supervised ones, cannot deliver sufficient accuracy to be practically useful in this task. Manual annotation by experts has been demonstrated to be a preferred approach for producing high-quality labeled data for training supervised models. However, most researchers in the education domain still consider the concept annotation process as an ad-hoc activity rather than a carefully executed task, which can result in low-quality annotated data. Using the annotation of concepts for the Introduction to Information Retrieval textbook as a case study, this paper presents a knowledge engineering method to obtain reliable concept annotations. As demonstrated by the data we collected, the inter-annotator agreement gradually increased along with our procedure, and the concept annotations we produced led to better results in document linking and student modeling tasks. The contributions of our work include a validated knowledge engineering procedure, a codebook for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as a gold standard in further intelligent textbook research. 

Developing online courses is a complex and time-consuming process that involves organizing a course into a sequence of topics and allocating the appropriate learning content within each topic. This task is especially difficult in complex domains like programming, due to the incremental nature of programming knowledge, where new topics extensively build upon domain concepts that were introduced in earlier lessons. In this paper, we propose a course-adaptive content-based recommender system that assists course authors and instructors in selecting the most relevant learning material for each course topic. The recommender system adapts to the deep prerequisite structure of the course as envisioned by a specific instructor, while unobtrusively deducing that structure from problem-solving examples that the instructor uses to present course concepts. We assessed the quality of recommendations and examined several aspects of the recommendation process by using three datasets collected from two different courses.While the presented recommender system was built for the domain of introductory programming, our course-adaptive recommendation approach could be used in a variety of other domains.