International journal on e-learning 

About: International journal on e-learning is an academic journal. The journal publishes majorly in the area(s): Educational technology & Distance education. Over the lifetime, 552 publications have been published receiving 10089 citations.

Perspectives on Blended Learning in Higher Education

Abstract: This article explores the benefits and challenges of blended learning in higher education from the perspective of students, faculty, and administration that have had direct experience with this form of course delivery. Students indicate that a blended learning model provides them with greater time flexibility and improved learning outcomes but that initially they encounter issues around time management, taking greater responsibility for their own learning, and using sophisticated technologies. Faculty suggest that blended courses create enhanced opportunities for teacher-student interaction, increased student engagement in learning, added flexibility in the teaching and learning environment, and opportunities for continuous improvement. They state that the challenges faced in developing such a course include a lack of time, support and resources for course redesign, acquiring new teaching and technology skills, plus the risks associated with delivering a course in a blended format. From an administrative perspective, blended learning presents the opportunity to enhance an institution’s reputation, expand access to an institution’s educational offerings, and reduce operating costs. The challenges consist of aligning blended learning with institutional goals and priorities, resistance to organizational change and lack of organizational structure and experience with collaboration and partnerships.

Key Factors for Determining Student Satisfaction in Online Courses

Abstract: H istorically, retention of distance learners has been problematic with dropout rates disproportionably high compared to traditional course settings (Richards & Ridley, 1997; Wetzel, Radtke, & Stern, 1994). Dropout rates of 30 to 50% have been common (Moore & Kearsley, 1996). Students may experience feelings of isolation in distance courses compared to prior faceto-face educational experiences (Shaw & Polovina, 1999). If the distance courses feature limited contact with instructors and fellow students, the result of this isolation can be unfinished courses or degrees (Keegan, 1990). Student satisfaction in traditional learning environments has been overlooked in the past (Astin, 1993; DeBourgh, 1999; Navarro & Shoemaker, 2000). Student satisfaction has also not been given the proper attention in distance learning environments (Biner, Dean, & Mellinger, 1994). Richards and Ridley (1997) suggested further research is necessary to study factors affecting student enrollment and satisfaction. Prior studies in classroom-based courses have shown there is a high correlation between student satisfaction and retention (Astin, 1993; Edwards & Waters, 1982). This high correlation has also been found in studies in which distance learners were the target population (Bailey, Bauman, & Lata, 1998). The purpose of this study was to identify factors influencing student satisfaction in online courses, and to create and validate an instrument to measure student satisfaction in online courses.

The Measurement of Social Presence in an Online Learning Environment

Abstract: Presentation of a tool to measure social presence and privacy in a Computer Mediated Communication (CMC).

Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment

Abstract: In this article, we proposed an evolving e-learning system which can adapt itself both to the learners and to the open Web, and we pointed out the differences of making recommendations in e-learning and other domains. We propose two pedagogy features in recommendation: learner interest and background knowledge. A description of a paper's value, similarity, and ordering are presented using formal definitions. We also study two pedagogy-oriented recommendation techniques: content-based and hybrid recommendations. We argue that while it is feasible to apply both of these techniques in our domain, a hybrid collaborative filtering technique is more efficient to make \"just-in-time\" recommendations. In order to assess and compare these two techniques, we carried out an experiment using artificial learners. Experiment results are encouraging, showing that hybrid collaborative filtering, which can lower the computational costs, will not compromise the overall performance of the recommendation system. In addition, as more and more learners participate in the learning process, both learner and paper models can better be enhanced and updated, which is especially desirable for webbased learning systems. We have tested the recommendation mechanisms with real learners, and the results are very encouraging.