Auto-req: Automatic detection of pre-requisite dependencies between academic videos (2023)

Abstract: Online learning platforms offer a wealth of educational material, but as the amount of content on these platforms grows, students may struggle to determine the most efficient order in which to cover the material to achieve a particular learning objective. In this paper, we propose a feature-based method for identifying pre-requisite dependencies between academic videos. Our approach involves using a transcript engine with a language model to transcribe domain-specific terms and then extracting novel similarity-based features to determine pre-requisite dependencies between video transcripts. This approach succeeds due to the development of a novel corpus of K-12 academic text, which was created using a proposed feature-based document parser. We evaluate our method on hand-annotated datasets for transcript extraction, video pre-requisites determination, and textbook parsing, which we have released. Our method for pre-requisite edge determination shows significant improvement (+4.7%-10.24% F1-score) compared to existing methods.

## introduction in many online learning platforms, academic videos that cover specific concepts are included in the curriculum. these videos cover certain "academic concepts," which are key ideas that are conveyed in the learning material. these fine-grained concepts aid students in understanding the learning content more effectively and achieving their core learning objectives. the prerequisite dependencies between these concepts, which pertain to the order in which they should be covered, are crucial for both educators and learners. they assist educators in curriculum planning and creating better learning pathways for students. with the increasing reliance on online learning platforms, there is a vast amount of academic content that requires proper organization into dependency graphs to aid in indexing for smart search capabilities and providing defined learning paths for students. research has shown that organizing content in this manner has significant benefits for learning, even in offline settings. a meta-analysis of 55 studies involving over 5,000 participants found that students who use concept maps for their daily studies were able to learn more in the same amount of time @xcite . although learning content is organized in textbooks and moocs, the creation of dependency graphs for academic videos serves to extend this organization, enabling us to identify only the relevant and required content for a specific learning objective based on prerequisite relationships. such a system allows us to recommend personalized learning pathways to users, fostering efficient and effective coverage of specific academic concepts. this tailored approach enhances students' educational experiences and promotes better understanding of the subject matter. moreover, it saves time for the student by ensuring that all required concepts or skills are covered before viewing content related to the desired academic concept. in this study, we propose a two-stage methodology for identifying prerequisite relationships among academic videos. the process begins with transcribing videos utilizing a speech-to-text model, combined with a language model specifically trained on a k-12 domain corpus. subsequently, we extract innovative similarity-based features from these transcripts to determine the prerequisite connections. the features employed in our study have been meticulously designed with the guidance of expert educators in the respective domain. these features utilize several similarity-based factors between two videos to identify pre-requisite dependencies. these factors include similarities between titles, content, and taxonomy. we also use keyphrase extraction algorithms to identify the topics covered in the transcripts and then compare the similarity between them. our work introduces the use of extracted keyphrase-based similarity for this task, contributing a novel approach to this research domain. once the features are extracted we use models such as lgbm @xcite , random forrest @xcite , and extratrees @xcite to predict prerequisite dependencies. our approach for identifying prerequisite relationships among educational videos demonstrates superior performance compared to existing benchmarks. to evaluate our pipeline, we used a hand-labeled dataset of k-12 academic videos with annotated pre-requisite edges. we introduced a novel featurebased pdf document parser that extracts a k-12 text corpus which ensures correct transcription of domain-specific terminologies and extraction of accurate semantic similarity-based features that take into account the contextual meaning of such terms. this tool extracts a hierarchical and well-organized corpus of k-12 academic text from core curriculum textbooks, strengthening the resilience and effectiveness of both pipeline stages when addressing technical vocabulary. the primary contributions of our research can be enumerated as follows: the datasets are released at https://bit.ly/ 412wkqp and a demo for the generated prerequisite edges can be found at https://bit. ly/3vrzmyl . ## current work our end-to-end pipeline to identify prerequisite dependencies between academic videos is novel. however, the sub-problems, such as transcript extraction, prerequisite edge detection, and parsing textbook pdfs have been well-studied in the literature. ## methodology in this section, we present a comprehensive explanation of the two-stage pipeline used for identifying prerequisite edges between academic videos as shown in figure 1 . the pipeline comprises a transcript extraction stage, followed by a feature extraction and classification stage for prerequisite edge detection. additionally, the pipeline requires a corpus of academic text obtained from academic textbooks. to fulfill this requirement, we have developed our own academic textbook parser. ## dataset 4.1 transcript dataset to showcase the efficacy of our proposed wav2vec2 speech model combined with the language model trained on our k-12 corpus, we assembled a dataset comprising five random academic videos in the science and math domains from youtube. we provide ground truth subtitles for these videos, alongside subtitles extracted by our algorithm and other benchmarks for comparison. ## conclusion in this paper, we present a pipeline for detecting prerequisite dependencies among academic videos using novel similarity-based features. our approach outperforms existing methods, even surpassing prerequisite detection in domains like wikipedia. we introduce hand-labeled datasets to discover prerequisite relations across diverse subjects, fostering future research in this area. future work will explore additional features and methods, extending our approach to a broader range of educational content such as podcasts, slides, and lecture notes. we also aim to integrate collaborative filtering and recommender systems for personalized learning paths, enhancing students' educational experience and learning outcomes.