Academic journal article Educational Technology & Society

Toward a Semantic Forum for Active Collaborative Learning

Academic journal article Educational Technology & Society

Toward a Semantic Forum for Active Collaborative Learning

Article excerpt

Introduction

Proponents of collaborative learning claim that learners in cooperative teams achieve higher levels of performance and retain information longer than learners who work individually (Webb, 1995). Asynchronous discussion forum plays an important role in supporting collaborative learning, allowing learners to communicate at large to ask questions, articulate their thoughts, explain and justify their opinions, share ideas and resources, with collaborative contribution to the knowledge building within online discussion forum. The accumulated discussion transcripts contain a great deal of deposited human knowledge. So, almost all e-learning platforms or environment provide discussion forums, expecting to support flexible collaborative learning and maximize resource sharing and reuse.

However, there exist some challenges and difficulties that hinder online discussion forums being as an effective e- learning facility. Firstly, the messages being delivered are arbitrarily structured and the content is dynamically generated through multiple learners' inputs, interactions, and annotations, thus learners have to spend a lot of time to go through nonlinear messages for locating useful messages. Second, messages are incoherent and disordered without semantic association. So, learners cannot search for the specific topic by retrieving certain discussion transcripts in separation. Last but not least, learners usually feel isolated and are easily tended to become disengaged and inactive when they are outside of the social context of the classroom (Amy et al., 2002). The literature in education research suggests that learners who are actively engaged in learning will be more likely to achieve success (Bonwell, 1996)(Richards, 1995)(Rubin et al., 1998). But investigators have come to recognize that asking learners to interact and discuss on forums does not necessarily lead to high-quality discourse. So, how to motivate learners to be actively engaged in collaborative learning via scaffolding intelligent facilities in forums becomes an important issue.

Therefore, by incorporating domain ontology and text mining technologies, this paper proposes a semantic forum that focuses on the semantic organization of discussion transcripts with semantic associations. By analyzing the content of messages as well as interaction between learners, an approach is also presented to discover special interest groups (SIGs) on the same or similar themes for topic-centric social context measurement. Meanwhile, a semantic search with relational navigation support is also provided to guide the learners through well-structured and coherent messages relevant to their learning demands. With such facilities, online discussion forum not only supplies communication space, but also serves as full-fledged learning environment with evolved structured knowledge repository to better support highly active and peer-to-peer in-depth collaborative learning.

Related Works

Analysis of the relationship among participants in discussion forums draws a lot of attention from researchers (Hewitt et al., 2007). Social Network Analysis (SNA) is an established method to derive person-person relations in the form of sociograms from "traces" of communication in a networked community (Wassermann et al., 1994). It is widely used to study the way people participated and interacted with each other in discussion boards (Laat et al., 2005), which provides information about the activities of such a community and the way they learn collaboratively. Based on writing and responding to messages, researchers have concentrated particularly on the constructed social networks and properties such as the small-world property and power-law degree distributions. This method is simply based on the information flow between learners but ignores the content of messages, and is therefore unable to discover interest groups to support theme-centered in-depth communication and learning. …

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