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Civic engagement in the United States has increased since the election of President Trump. This increase is evident online as people are using Twitter to assert their digital citizenship by voicing their opinions regarding President Donald J. Trump and demonstrating solidarity with various civic movements. President Trump's election has caused many people to recognize how policies impact their daily lives and shed previous understandings as described by Freire (2005) as concientization. This study employed a Content Analysis to classify Tweets from #DisruptJ20 posted during inauguration week according to concientization and Support or Opposition of President Trump. A Sentiment Analysis revealed that supporters of President Trump were much more negative than those who oppose President Trump. Results of the Logistic Regression found that variables related to network structure (Friends, Followers, and Likes) were more likely to predict Retweets than concientization. Results of Hierarchical Linear Modeling indicate the average level of concientization was positively related to being Retweeted. Implications include recognizing that digital citizens value content that illustrates how matters of state are impacting their lives. As concientization increases in America, the more likely it is for people with opposing viewpoints to understand one another and work for mutually beneficial social change.

Gunawardena et al.'s (1997) Interaction Analysis Model (IAM) is one of the most frequently employed frameworks to guide the qualitative analysis of social construction of knowledge online. However, qualitative analysis is time consuming, and precludes immediate feedback to revise online courses while being delivered. To expedite analysis with a large dataset, this study explores how two neural network architectures--a feed-forward network (Doc2Vec) and a large language model transformer (BERT)--could automatically predict phases of knowledge construction using IAM. The methods interrogated the extent to which the artificial neural networks' predictions of IAM Phases approximated a human coder's qualitative analysis. Key results indicate an accuracy of 21.55% for Doc2Vec phases I-V, 43% for fine-tuning a pre-trained large language model (LLM), and 52.79% for prompt-engineering an LLM. Future studies for improving accuracy should consider either training the models with larger datasets or focusing on the design of prompts to improve classification accuracy. Grounded on social constructivism and IAM, this study has implications for designing and supporting online collaborative learning where the goal is social construction of knowledge. Moreover, it has teaching implications for guiding the design of AI tools that provide beneficial feedback for both students and course designers. [ABSTRACT FROM AUTHOR]

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and social network analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ("big data") in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social network analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and social network analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms. [ABSTRACT FROM AUTHOR]