The paper presents a method for recommending augmentations against conceptual gaps in textbooks. Question Answer (QA) pairs from community question-answering (cQA) forums are noted to offer precise and comprehensive illustrations of concepts. Our proposed method retrieves QA pairs for a target concept to suggest two types of augmentations: basic and supplementary. Basic augmentations are suggested for the concepts on which a textbook lacks fundamental references. We identified such deficiencies by employing a supervised machine learning-based approach trained on 12 features concerning the textbook's discourse. Supplementary augmentations aiming for additional references are suggested for all the concepts. Retrieved QA pairs were filtered to ensure their comprehensiveness for the target students. The proposed augmentation system was deployed using a web-based interface. We collected 28 Indian textbooks and manually curated them to create gold standards for assessing our proposed system. Analyzing expert opinions and adopting an equivalent pretest-posttest setup for the students, the quality of these augmentations was quantified. We evaluated the usability of the interface from students' responses. Both system and human-based evaluations indicated that the suggested augmentations addressed the concept-specific deficiency and provided additional materials to stimulate learning interest. The learning interface was easy-to-use and showcased these augmentations effectively.