Machine learning (ML) and deep learning (DL) provide significant opportunities to enhance our understanding of idea generation in asynchronous online discussions (AODs). Drawing on the interaction analysis model (IAM) as our theoretical framework, we built one baseline ML and three DL systems to automate message classification when assessing collaborative knowledge construction depth in academic AODs. The viability of these systems was demonstrated via four offerings of a traditional online course. We achieved 79% as the highest overall accuracy score across all phases of the IAM. To the best of our knowledge, this study is the first to classify AOD messages across all IAM phases. We contribute to the theory by updating the IAM to better explain how to promote deeper interactions in AODs. Additionally, we provide a methodological blueprint for future research where classifying text is crucial.

In the context of large annotation-based literature discussions, this research examines the affordances of recommender systems on users’ disorientation. Drawing insights from literature on group cognition, knowledge building, and recommender systems, we developed three recommender systems and tested these systems on 136 users. Results indicate that the recommender system with constrained Pearson correlation coefficient similarity metric reduced users’ disorientation and afforded them the opportunity to become better aware of interesting and relevant information based on their needs and preferences without heavy costs in terms of time and effort. With respect to other software conditions, results indicate that users suffered from higher levels of disorientation. These findings counter the claim that annotations reduce disorientation. Theoretical and practical implications are also discussed.

Within conversational media, how others perceive contributions affects his or her interactions with those contributions. This research explores a novel addition to conversational software, one that provides real-time assessment of quality across user contributions. An analysis of 2,157 online conversations examined attributes of quality, including lexical complexity and prompt-specific vocabulary. These factors helped to inform the redesign of an existing asynchronous online discussion board (AOD). More specifically, a real-time quality analyzer was constructed, which provides users with a visual breakdown of their post in relation to the overall group discussion thread. An experiment across two populations was performed and results found that the system increased overall levels of quality in conversations, while also increasing quality interactions across the system. The results were supplemented with survey data and a social network analysis (SNA), which discovered higher levels of system satisfaction and group cohesion.

Service-learning continues to play an increasing role in higher education as instructors look to incorporate high impact practices that challenge students through active and experiential learning. Yet limitations in learning management systems (LMS) can be barriers to service-learning project success. In this paper, we present an experience report on the design and implementation of an interdisciplinary service-learning course for computer science. We also present on the design and implementation of specialized social networking software as a mechanism to support service-learning across interdisciplinary computer science courses. More specifically, this research introduces customized social software, consisting of blogging, wiki and discussion software as tools for facilitating the specialized needs of these courses. These needs range from the ability for project management and milestone tracking, which are supported through wiki technology and messaging, self-reflection, which is supported through blogging and information exchange and knowledge sharing, which are supported through online discussion boards, social bookmarking and file-sharing. Results were largely positive, with a majority of students indicating that the course learning environment supported learning, collaboration and course community.

This paper reports on software designed to visualize levels of quality within online conversational media. Prior to construction, data mining was performed on 2,157 online conversations and examined for attributes of quality. This initial dataset was analyzed for lexical complexity and prompt-specific vocabulary usage and helped guide the redesign of an existing asynchronous online discussion board (AOD). The new design incorporates a real-time quality analyzer and provides users with a visual breakdown of their post in relation to the overall group discussion thread. Results found that the proposed system produced higher levels of overall quality in discussion posts and increased interactions with higher quality discussion posts. Survey results and a social network analysis (SNA) indicate that the proposed system produced higher levels of system satisfaction and group cohesion when compared against control software.