Optimizing Quality for Collaborative Video Viewing

The increasing popularity of distance learning and online courses has highlighted the lack of collaborative tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources used by the students. We present an architecture and adaptation model called AI2TV (Adaptive Internet Interactive Team Video), a system that allows geographically dispersed participants, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2TV upholds the invariant that each participant will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any of the participants and the results of those actions are seen by all the members. These features allow group members to review a lecture video in tandem to facilitate the learning process. We employ an autonomic (feedback loop) controller that monitors clients' video status and adjusts the quality of the video according to the resources of each client. We show in experimental trials that our system can successfully synchronize video for distributed clients while, at the same time, optimizing the video quality given actual (fluctuating) bandwidth by adaptively adjusting the quality level for each participant

Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant

Progressor: Social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students. © 2013 Taylor and Francis Group, LLC

Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

Intelligent and adaptive tutoring for active learning and training environments

Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used

Transforming a linear module into an adaptive one : tackling the challenge

Every learner is fundamentally different. However, few courses are delivered in a way that is tailored to the specific needs of each student. Delivery systems for adaptive educational hypermedia have been extensively researched and found promising. Still, authoring of adaptive courses remains a challenge. In prior research, we have built an adaptive hypermedia authoring system, MOT3.0. The main focus was on enhancing the type of functionality that allows the non-technical author, to efficiently and effectively use such a tool. Here we show how teachers can start from existing course material and transform it into an adaptive course, catering for various learners. We also show how this apparent simplicity still allows for building of flexible and complex adaptation, and describe an evaluation with course authors

Motivational Social Visualizations for Personalized E-Learning

A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor +  in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them

Improving instructional effectiveness with computer‐mediated communication

This study explores the use of asynchronous Computer‐Mediated Communication (CMC) in the delivery of instructional content, and points up the interaction among learners, as well as between learners and instructors. The instructional content in the project described was available to learners online as Microsoft Word documents, with email being used for communicating within the student group. Many students, as well as some of the instructors, felt uncomfortable with the flexibility and openness that a CMC environment allowed. However, once familiar with this process of instruction and interaction, learners were able to work consistently at their own pace, and understand that instructors are interested in every individual learner's opinion and in the collective views of the group. It was evident that a CMC‐based instructional delivery system, when carefully planned, has the potential to facilitate that outcome, and to improve instructional effectiveness