Ali Shariq Imran

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Lecture videos contain most of the instructional content. These videos contents are in general non-scripted and unedited and thus do not provide the required level of interactivity from such material. Therefore, these videos fail to captivate students' attention for long and thus their effective use remains a challenge. In this regard, Media Learning Object(More)
—In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The(More)
—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(More)
In this paper we study image distortions and impairments that affect the perceived quality of blackboard lectures images. We also propose a novel reference free image quality evaluation metric that correlates well with the perceived image quality. The perceived quality of images of blackboard lecture contents is mostly affected by the presence of noise,(More)