Lecture Videos for E-Learning: Current Research and Challenges

Liu, T.; Kender, John R.

doi:10.1109/mmse.2004.48

Cited by 22 publications

(4 citation statements)

References 25 publications

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“…E-learning in multimedia offers various advantages, such as increased engagement, interactive content, and personalized learning experiences [11]. However, it also comes with several challenges.…”

Section: Challengesmentioning

confidence: 99%

Scope and Challenges of Multimedia in Education Sector

Monika¹,

Bala²,

Sunita³

2023

IJFMR

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Section: Challengesmentioning

confidence: 99%

Scope and Challenges of Multimedia in Education Sector

Monika¹,

Bala²,

Sunita³

2023

IJFMR

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“…However, many researchers have argued that e-learning cannot replace traditional learning altogether. In a similar argument, Liu & Kender (2004) mentioned that, based on a recent survey carried out at University of South Carolina, students consider traditional blackboard presentation as "essential" and "indispensable. "…”

Section: Non-computer Based Interactionmentioning

confidence: 99%

Rationale, Design and Implementation of a Computer Vision-Based Interactive E-Learning System

Jin

2007

International Journal of Distance Education Technologies

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This article presents a schematic application of computer vision technologies to e-learning that is synchronous, peer-to-peer-based, and supports an instructor's interaction with non-computer teaching equipments. The article first discusses the importance of these focused e-learning areas, where the properties include accurate bidirectional interaction and low cost hardware; system portability and versatile vision technology are emphasized. In the subsequent sections, we present some results aiming to achieve these goals. In particular, we highlight the most recent advancements in the interactive PTZ camera control from both the instructor and remote student. We also illustrated how these results have successfully addressed the challenges.

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“…Finding such index points will help create efficient VLOs [1] by removing portions of inactive scenes from a video with no pedagogical value. However, creating an index for a lecture video is a challenging task [2]. One has to know what kind of indexes are good to navigate through a video.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Visual Cues in Non-Scripted Lecture Videos for Multi-modal Action Recognition

Imran

Moreno

Cheikh

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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The usage of non-scripted lecture videos as a part of learning material is becoming an everyday activity in most of higher education institutions due to the growing interest in flexible and blended education. Generally these videos are delivered as part of Learning Objects (LO) through various Learning Management Systems (LMS). Currently creating these video learning objects (VLO) is a cumbersome process. Because it requires thorough analyses of the lecture content for meta-data extraction and the extraction of the structural information for indexing and retrieval purposes. Current e-learning systems and libraries (such as libSCORM) lack the functionally for exploiting semantic content for automatic segmentation. Without the additional meta-data and structural information lecture videos thus do not provide the required level of interactivity required for flexible education. As a result, they fail to captivate students' attention for long time and thus their effective use remains a challenge.Exploiting visual actions present in non-scripted lecture videos can be useful for automatically segmenting and extracting the structure of these videos. Such visual cues help identify possible key frames, index points, key events and relevant meta-data useful for e-learning systems, video surrogates and video skims. We therefore, propose a multimodel action classification system for four predefined actions performed by instructor in lecture videos. These actions are writing, erasing, speaking and being idle. The proposed approach is based on human shape and motion analysis using motion history images (MHI) at different temporal resolutions allowing robust action classification. Additionally, it augments the visual features classification based on audio analysis which is shown to improve the overall action classification performance. The initial experimental results using recorded lecture videos gave an overall classification accuracy of 89.06%. We evaluated the performance of our approch to template matching using correlation and similitude and found nearly 30% improvement over it. These are very encouraging results that prove the validity of the approach and its potential in extracting structural information from instructional videos.

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Lecture Videos for E-Learning: Current Research and Challenges

Cited by 22 publications

References 25 publications

Scope and Challenges of Multimedia in Education Sector

Scope and Challenges of Multimedia in Education Sector

Rationale, Design and Implementation of a Computer Vision-Based Interactive E-Learning System

Exploiting Visual Cues in Non-Scripted Lecture Videos for Multi-modal Action Recognition

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