This article reports on a large-scale (n = 987), exploratory factor analysis study incorporating various concepts identified in the literature as critical success factors for online learning from the students’ perspective, and then determines their hierarchical significance. Seven factors--Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Online Social Comfort, Online Interactive Modality, and Social Presence--were identified as significant and reliable. Regression analysis indicates the minimal factors for enrollment in future classes—when students consider convenience and scheduling—were Basic Online Modality, Cognitive Presence, and Online Social Comfort. Students who accepted or embraced online courses on their own merits wanted a minimum of Basic Online Modality, Teaching Presence, Cognitive Presence, Online Social Comfort, and Social Presence. Students, who preferred face-to-face classes and demanded a comparable experience, valued Online Interactive Modality and Instructional Support more highly. Recommendations for online course design, policy, and future research are provided.

Abstract: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.

This paper proposes a cybersecurity control framework for blockchain ecosystems, drawing from risks identified in the practitioner and academic literature. The framework identifies thirteen risks for blockchain implementations, ten common to other information systems and three risks specific to blockchains: centralization of computing power, transaction malleability, and flawed or malicious smart contracts. It also proposes controls to mitigate the risks identified; some were identified in the literature and some are new. Controls that apply to all types of information systems are adapted to the different components of the blockchain ecosystem.

This paper proposes a research framework for studying the connections-realized and potential-between unstructured data (UD) and cybersecurity and internal controls. In the framework, cybersecurity and internal control goals determine the tasks to be conducted. The task influences the types of UD to be accessed and the types of analysis to be done, which in turn influences the outcomes that can be achieved. Patterns in UD are relevant for cybersecurity and internal control, but UD poses unique challenges for its analysis and management. This paper discusses some of these challenges including veracity, structuralizing, bias, and explainability.

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