The FAU Video Lecture Browser system

Riedhammer, Korbinian; Gropp, Martin; Nöth, Elmar

doi:10.1109/slt.2012.6424256

Cited by 4 publications

(2 citation statements)

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“…By clicking each key phrase, learners can access corresponding video vignettes for detailed discussion [113]. Without transcribing speech to text beforehand, a method matching spoken search queries to lecture speech directly on audio is also proposed to improve video navigation [115].…”

Section: Human Language Technologymentioning

confidence: 99%

Educational Content Linking for Enhancing Learning Need Remediation in MOOCs

Li¹

2020

Preprint

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Since its introduction in 2011, there have been over 4,000 MOOCs (Massive Open Online Courses) on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to transcend time and space, democratize knowledge dissemination, and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience and outcome.To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improve learning outcome and experience.To support our conjecture, we present end-to-end studies to investigate our framework around two research questions. We first ask, can manually generated linking improve learning? For investigating this question, we choose two STEM courses, statistics and programming language, and demonstrate how the annotation of linking among course materials can be done with collaboration between course staff and online workers. With the annotation, we implement an interface that can present learning materials and visualize the linking among them simultaneously. We observe that, in a large-scale user study, this interface enables users to search for desired course materials more efficiently, and retain more concepts more readily. This result supports the notion that manual linking can indeed improve learning outcomes. Second, we ask, can learning content be generated with machine learning methods? For this question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still

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Section: Human Language Technologymentioning

confidence: 99%

Educational Content Linking for Enhancing Learning Need Remediation in MOOCs

Li¹

2020

Preprint

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“…The MIT Lecture Browser [19] automatically segmented, transcribed and indexed course lectures and offered efficient ways to retrieve the audio and video segments of the lectures. The FAU Video Lecture Browser displayed automatically extracted key terms for access of video lectures [301].…”

Section: A Motivation and Backgroundmentioning

confidence: 99%

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Lee

Glass

Lee

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.Index Terms-Spoken content retrieval, spoken term detection, query by example, semantic retrieval, joint optimization, pseudo-relevance feedback, graph-based random walk, unsupervised acoustic pattern discovery, query expansion, interactive retrieval, summarization, key term extraction.

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