Multimedia learning objects framework for e-learning

Abstract

With the growing interest in flexible and blended education, the usage of recorded lecture videos as a part of learning material, is becoming an everyday activity in most of the higher education institutions. Flexible education provides students with the opportunity to select how, when, and where to study compared to ordinary on-campus education. Additionally, lecture videos contain most of the instructional content. The content of these videos are in general non-scripted and unedited, and thus do not provide the required level of interactivity. Therefore, these videos fail to captivate students' attention for a long time, and thus their effective use remains a challenge. In this regard, Multimedia Learning Object (MLO) can play an important role in future flexible education. MLOs are media rich documents that have pedagogical values encapsulated with all needed features such as navigation structures and surrogates. We propose in this paper a framework for interactive MLOs in distance and blended education. The framework supports creation, storage, distribution, and evaluation of automatically extracted learning objects from digital media. The framework not only conforms to industry standards, but also provide novel methods and techniques to develop Video Learning Objects (VLOs).

Adaptive Learning Environments Based on Intelligent Manipulation for Video Learning Objects

“…The method detects slide transitions and extracts information from a presentation document such as (author name, title, date of creation) to obtain slide images, fill in the metadata of the learning object, and extract the table of contents from the presentation. Imran and Cheikh [13] proposed a framework for the development of multi-media learning objects. They presented this framework to integrate with LMSs (Learning Management System) for the creation, storage, distribution, and evaluation of automatically extracted learning objects from digital media.…”

Automatic Identification and Extraction of Adaptive Learning Objects From Traditional Courseware

“…The method detects slide transitions and extracts information from a presentation document (author name, title, date of creation) to obtain slide images, fill in the metadata of the learning object, and extract the table of contents from the presentation. The authors in [16] proposed a framework for the development of effective multi-media learning objects. They presented this framework to integrate with LMSs (Learning Management System) for the creation, storage, distribution, and evaluation of automatically extracted learning objects from digital media.…”

Integrating word embeddings and document topics with deep learning in a video classification framework

“…Massive amount of video lectures are uploaded on a daily basis that has created a need for e cient structuring and classification of educational resources into respective categories for easy search and retrieval. Imran and Cheikh (2012) first proposed the multimedia learning object framework for video lectures that opened up the niche for utilizing both implicit and explicit metadata obtained from textual content and non-textual cues for content organizing, structuring, and classification of video learning objects. A number of classification approaches as a result with respect to lecture videos have evolved over the years, most of which make use of natural language processing (NLP) either directly on the accompanying audio transcript as in (Imran et al, 2012b), or extracted automatically from the lecture images employing OCR as in (Imran et al, 2012a) or via speech-to-text such as in (Dessì et al, 2018).…”