Cluster Analysis in Online Learning Communities: A Text Mining Approach

This paper presents a theory-informed blueprint for mining unstructured text data using mixed- and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants’ selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth

A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one’s health. One such area is a move towards healthier lifestyle choices such as one’s diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-based health management, offering individuals more food options, not only based on one’s preferred tastes but also on one’s dietary needs and restrictions. This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain

How Design Science Research Helps Improve Learning Efficiency in Online Conversations

In this design science research paper, we report on our constructing and evaluating an attention-guidance system that we integrated into a computer-supported collaborative learning system. Drawing on social constructivist literature, our proposed design focuses on attracting, retaining, and, if necessary, reacquiring users’ attention on task-relevant information in online collaborative literature processing. The investigation involved an experiment across two sections of students in a human-computer interaction course. Results show that the new design allowed users to consistently reflect and evaluate the content of a text as they capitalized on one another’s reasoning to resolve misconceptions. Moreover, we found that the new system increased users’ perceptions of learning. However, the difference in knowledge gain scores was marginally significant and represented a medium effect size. Interestingly, we found that the attention-guidance system supported more efficient learning. Finally, we discovered that task-oriented reading of text, revisions of incomplete or incorrect ideas, and perceptions of learning mediated the relationship between software system and learning efficiency. We discuss the theoretical and practical implications

Employee Rewards And The Likelihood Of A Successful Initial Public Offering

In this paper, we survey employees about human resources practices regarding employee stock ownership plans (ESOPs) and profit sharing plans of firms that have registered for an IPO offering.  We find that firms that had ESOPs in place prior to the registration of an IPO have a greater likelihood of eventually launching an IPO than those registered firms who do not.  Our results broaden the existing finance literature of IPO analysis as we survey registered companies prior to their attempted IPO launch to determine whether their employee-based compensation structure impacts the likelihood of a successful IPO launch

Determining Link Relevancy in Tweets Related to Multiple Myeloma Using Natural Language Processing

Social media platforms continue to play a leading role in the evolution of how people share and consume information. Information is no longer limited to updates from a user’s immediate social network but have expanded to an abstract network of feeds from across the global internet. Within the health domain, users rely on social media as a means for researching symptoms of illnesses and the myriad of therapies posted by others with similar implications. Whereas in the past, a single user may have received information from a limited number of local sources, now a user can subscribe to information feeds from around the globe and receive real-time updates on information important to their health. Yet how do users know that the information they are receiving is relevant or not? In this age of fake news and widespread disinformation the global domain of medical knowledge can be tough to navigate. Both legitimate and illegitimate practitioners leverage social media to spread information outside of their immediate network in order to reach, sway, and enlist a larger audience. In this research, we develop a system for determining the relevancy of linked webpages using a combination of web mining through Twitter hashtags and natural language processing (NLP)

Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

Research on computer-supported collaborative learning (CSCL) often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students’ messages from voluminous discussions. We employ learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed

Classifying Vaccine Misinformation in Online Social Media Videos using Natural Language Processing and Machine Learning

The spread of information through online social media videos is one of the most popular ways to share and obtain information, while at the same time the spread of misinformation across these same social spaces has become a significant concern affecting human well-being. Being able to detect this misinformation before it spreads is becoming more and more desirable for many social media platforms. This research focuses on exploring the accuracy of detecting misinformation across two social media platforms, YouTube and BitChute. This involves the classification of video data into two types: genuine information or misinformation. More specifically, this research generates additional metadata embedded within online videos related to the COVID-19 vaccination. Using natural language processing (NLP) we extract medical subject headings (MeSH) terms from video transcripts and classify videos using four machine learning techniques including naïve Bayes, random forest, support vector machine, and logistic regression. Implementation of each classifier is presented, and the accuracy of each technique is compared and discussed

Development of a Reading Material Recommender System Based On Design Science Research Approach

Using design science research (DSR), we outline the construction and evaluation of a recommender system incorporated into an existing computer-supported collaborative learning environment. Drawing from Clark’s communication theory and a user-centered design methodology, the proposed design aims to prevent users from having to develop their own conversational overload coping strategies detrimental to learning within large discussions. Two experiments were carried out to investigate the merits of three collaborative filtering recommender systems. Findings from the first experiment show that the constrained Pearson Correlation Coefficient (PCC) similarity metric produced the most accurate recommendations. Consistently, users reported that constrained PCC based recommendations served best to their needs, which prompted users to read more posts. Results from the second experiment strikingly suggest that constrained PCC based recommendations simplified users’ navigation in large discussions by acting as implicit indicators of common ground, freeing users from having to develop their own coping strategies

Towards a Sentiment Analyzing Discussion-board

In this paper we present the design and construction of a sentiment analyzing discussion board, which was used to support learning and interaction within an existing online social networking (OSN) system. More specifically, this research introduces an innovative extension to learning management software (LMS) that combines real-time sentiment analysis with the goal of fostering student engagement and course community. In this study we perform data mining to extract sentiment on over 6,000 historical discussion board posts. This initial data was analyzed for sentiment and interaction patterns and used for guiding the redesign of an existing asynchronous online discussion board (AOD). The redesign incorporates a sentiment analyzer, which allows users to analyze the sentiment of their individual contributions prior to submission. Preliminary results found that the proposed system produced more favorable outcomes when compared to existing AOD software