Abstract
With the introduction of Web 2.0, e-learning paradigm has made several attempts to ease the existing way of online learning. These attempts range from offering a unified learning platform to furnishing learning materials of different modalities. However, these works eventually correspond to a particular subject domain, learning aspect, or educational objective in general. Another alley of this e-learning paradigm relies upon the freely available information on the Internet to enhance existing learning materials with relevant learning supports. To summarize, the existing research studies integrated some of the learning aspects while enhancing the learning process in a particular learning mode. They lacked a design to accommodate different aspects of learning and different modes of learning materials together. In drive to support integrated and multimodal learning aspects, the present work offers a system to augment learning materials of two modes: textual and video-based. We consider school-level textbooks as textual learning materials, whereas video lectures from college and university levels constitute the target video-based materials. Our system diagnoses these learning materials for conceptual deficiencies and remediates these gaps with augmentations considered relevant to these target concepts.
Our textbook augmentation system recommends augmentations against con- ceptual deficiencies present in the textbooks. We have explored the possible conceptual gaps from existing literature and the textbooks to suggest three types of deficiencies (out-of-focus, sequentiality, and lack of relevant components). The present work employs a supervised machine learning-based approach trained on various features concerning the textbook’s discourse to identify these deficiencies automatically. Presence of a deficient concept asks for a comprehensive illustration of a set of concepts that are not adequately discussed in the concerned textbook.
We address such deficiencies by retrieving relevant augmentations against keyword- based queries formed with the deficient concepts, their contexts, and deficiency patterns. Retrieved augmentations are furtherfiltered to ensure that they remain understandable to the target students. On the other hand, concepts from video lectures are considered deficient if they are introduced as prerequisites to other course-relevant concepts. Being off-topic (out-of-scope in the current context), such concepts are not defined and discussed with necessary detail in the video lectures. The current work identifies such concepts and remediates these deficien- cies by suggesting relevant augmentations. We model the concepts in a concept similarity network and compare the inter-concept relations in a semantic space to identify the off-topic concepts. To encode the inter-concept relations accurately, we explored the available similarity measures and analyzed the community structures through several community detection algorithms. Once the queries are generated,
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Abstract and augmentations are archived, we suggest a retrieval model for recommending relevant augmentations for each off-topic concept. Retrieved augmentations are further reranked to ensure that the top-ranked augmentation offers the most basic understanding of the target off-topic concept.
For a given concept, relevant augmentations are recommended using retrieval approaches. Question Answer (QA) pairs from community question-answering (CQA) forums are noted to offer an illustration that defines a target concept explicitly with necessary learning supports and introduces related concepts or techniques for further readings. Consequently, we augmented the textbooks with QA pairs relevant to a target concept. Due to the structural differences of the QA pairs from usual textual discourses, we proposed a two-stage retrieval approach to retrieve the textbook augmentations in the form of QA pairs. The proposed approach retrieves similar questions for a given query using a deep learning-based approach in the first phase. The second phase reranks the initially retrieved questions based on inter-question similarities. The suggested deep learning-based approach is trained using several surface features of texts, and the associated weights are pre-trained using a deep generative model for better initialization.
The proposed retrieval model outperforms standard baseline question retrieval approaches. The proposed reranking approach performs inference over a similarity graph constructed with the initially retrieved questions and reranks the questions based on their similarity with other relevant questions. The suggested reranking approach significantly improves the precision of the retrieval task. On the other hand, the video lectures are augmented to offer a basic conceptual understanding.
Accordingly, short video lecture segments are considered a suitable augmentation candidate. Such augmentations are fetched using a basic retrieval approach boosted with objective-specific reranking strategies modeled over the factors which determine whether the retrieved segments offer a basic understanding or not.
The proposed textbook and video lecture augmentation systems have been deployed in a web-based user interface to cater the augmented learning materials to the students. The effectiveness of an online learning process depends on accessibility of the suggested learning materials and usability of the concerned learning platform.
Accordingly, we have developed an easy-to-use web-based platform such that the overall learning experience seems integrated.
We have suggested a thorough assessment plan combining both system-based and human-based evaluation tasks for each and every module that constitutes our proposed augmentation systems. System-based evaluation tasks are carried out by comparing a module’s predictions with the gold standard data. In contrast, human- based evaluation tasks gather opinions from experts and target users to imply the quality of the predicted augmentations and usability of the developed learning platform. Our augmentation systems are developed over two corpora. One contains 28 Indian textbooks published by the Indian National Council of Educational Research and Training (NCERT). The other is formed with 68 video lecture courses published by the National Programme on Technology Enhanced Learning (NPTEL). These corpora have been manually curated to create good quality gold standards by employing experienced subject matter experts. Our suggested system-based evaluation tasks have assessed the modules responsible for identifying
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Abstract
deficiencies and recommending augmentations for both textbooks and video lectures.
Performances of these modules are established by comparing them with several baseline methods. The quality of the recommended augmentations is judged using expert opinions. At the same time, their significance in remediating the conceptual gaps is quantified by comparing the students’ performance in an equivalent pretest- posttest setup. Both the system and human-based evaluations indicate that the suggested augmentations addressed the concept-specific deficiencies and provided additional materials to stimulate interest among the students. Even so, the target users found that the interface was easy-to-use and showcased these augmentations effectively.
Keywords: Augmenting learning materials, Textbook augmentation, Video lec- ture augmentation, MOOCs, Community question answering, Question retrieval, Structural reranking, Concept extraction, Deficiency diagnosis, Query generation, Off-topic concept identification, Concept similarity, Community detection.
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