Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging

Video is frequently used as a learning medium in a variety of educational settings, including large online courses as well as informal learning scenarios. To foster learner engagement around instructional videos, our learning scenario facilitates interactive note taking and commenting similar to popular social video-sharing platforms. This approach has recently been enriched by introducing nudging mechanisms, which raises questions about ensuing learning effects. To better understand the nature of these effects, we take a closer look at the content of the comments. Our study is based on an ex post analysis of a larger data set from a recent study. As a first step of analysis, video comments are clustered based on a feature set that captures the temporal and semantic alignment of comments with the videos. Based on the ensuing typology of comments, learners are characterized through the types of comments that they have contributed. The results will allow for a better targeting of nudges to improve video-based learning

Indicators for enhancing learners’ engagement in massive open online courses: A systematic review

Massive open online courses (MOOCs) have paved a new learning path for the 21st-century world. The potential to reach a massive geographically dispersed audience is one of the major advantages of MOOCs. Moreover, they can be offered on a self-paced and self-regulated basis and have become an integral part of lifelong learning, especially in workplaces. However, one persistent problem is the lack of learners’ engagement. A harmonisation of studies providing a holistic view into aggregating indicators for enhancing learners’ engagement in MOOCs is lacking. The coronavirus pandemic has accelerated MOOC adoption, and learners’ engagement in MOOCs has become even more essential for the success of this educational innovation. We examine the existing literature to derive indicators important for enhancing learners’ engagement in MOOC learning environments. Using a systematic approach, 83 empirical studies were examined, and 10 indicators were identified as important considerations for enhancing learners’ engagement while designing MOOCs—from initiatives for individual learners to platform and instructional design perspectives. We also present a table describing these indicators and offer a structured discussion on each one. We believe the results provide guidelines for MOOC designers and instructors, educational policymakers, higher education institutions, and MOOC engagement researchers.Peer reviewe

Priming and Mining the Civil Engineering Mindset: How Personal Values and Perfectionism Shape Societal Engagement and Consideration in Design

There are many ongoing calls for the integration of public welfare needs and concerns into engineering curricula and practice; for example, promoting social consciousness, human-centred design, and other socially-related frameworks. However, some engineering students still seem to devalue or resist these initiatives. This project attempts to overcome this problem by exploring a new methodology to facilitate such integrations, whilst bypassing the possible resistance. In the first intervention, this project explores to facilitate such notions via exploiting the psychology-informed approach of priming. Results of the first intervention showed that the priming initially intended to raise empathy (and by extension, social consciousness) scores unexpectedly resulted in significantly decreasing them. This initiated the second and third interventions, which explored how different key facets of the mindset (i.e., personal values and perfectionism, respectively) contribute to decision-making, particularly in contexts of human-centred designing and socially relevant initiatives, in civil engineering design. Such research on exploring the engineering mindset was to also inform the under-explored research literature on the subjective nature of sustainable decision-making in engineering. .. The second and third interventions therefore serve to fill the gap on addressing the subjective nature of sustainable decision-making in engineering, by researching to understand how the different facets of the mindset (i.e., personal values and perfectionism, respectively) dictate decision-making and facilitate (or hinder) social engagement and consideration in human-centric designing and socially considerate contexts. The influence of priming on such decision-making processes and social considerations were also observed in light of the different facets of the mindset. Results show that the majority of civil engineering undergraduates hold dominant Higher Order Values rooted in Self Transcendence (60.87%), and were categorised as perfectionists (74.48%). Findings indicate that those with Higher Order Value rooted in Self Transcendence were significantly less likely to produce what I term Communal Designs (i.e., designs that inform the metaphysical as well as the physical needs of the end-user), compared to those with dominant values rooted in the Higher Order Value of Openness to Change. Students were also found to transition in value towards the Higher Order Value of Conservation with time (i.e., with transition from year 1 to year 3 in a civil engineering programme), and thus transition away from their likelihood of producing Communal Designs by extension. Similarly, those categorised as perfectionists were significantly less likely to produce Communal Designs compared to those categorised as non-perfectionists. Perfectionists were later found to be associated with the Higher Order Value of Conservation when resumed back to the literature for sense-making of the present findings. Underlying common motives of Self-Protection and Anxiety-Avoidance were thus deduced to be hindering ‘truthful’ (i.e., intrinsically driven) engagement with human-centric initiatives, and production of what I termed Communal Designs. An intention-behaviour gap was found prominent in civil engineering undergraduates perhaps intending to, but then failing to produce Communal Designs. Further, the reversed influence of the priming was then discussed to be relative to the underlying motives of self-protection and anxiety-avoidance of the civil engineering undergraduates. Findings of the present project thus serve as a foundation for future mitigative studies or interventions promoting socially considerate initiatives or practices in civil engineering designs

The Philosophy of Online Manipulation

Are we being manipulated online? If so, is being manipulated by online technologies and algorithmic systems notably different from human forms of manipulation? And what is under threat exactly when people are manipulated online? This volume provides philosophical and conceptual depth to debates in digital ethics about online manipulation. The contributions explore the ramifications of our increasingly consequential interactions with online technologies such as online recommender systems, social media, user friendly design, microtargeting, default settings, gamification, and real time profiling. The authors in this volume address four broad and interconnected themes: What is the conceptual nature of online manipulation? And how, methodologically, should the concept be defined? Does online manipulation threaten autonomy, freedom, and meaning in life and if so, how? What are the epistemic, affective, and political harms and risks associated with online manipulation? What are legal and regulatory perspectives on online manipulation? This volume brings these various considerations together to offer philosophically robust answers to critical questions concerning our online interactions with one another and with autonomous systems. The Philosophy of Online Manipulation will be of interest to researchers and advanced students working in moral philosophy, digital ethics, philosophy of technology, and the ethics of manipulation

Interactional Slingshots: Providing Support Structure to User Interactions in Hybrid Intelligence Systems

The proliferation of artificial intelligence (AI) systems has enabled us to engage more deeply and powerfully with our digital and physical environments, from chatbots to autonomous vehicles to robotic assistive technology. Unfortunately, these state-of-the-art systems often fail in contexts that require human understanding, are never-before-seen, or complex. In such cases, though the AI-only approaches cannot solve the full task, their ability to solve a piece of the task can be combined with human effort to become more robust to handling complexity and uncertainty. A hybrid intelligence system—one that combines human and machine skill sets—can make intelligent systems more operable in real-world settings. In this dissertation, we propose the idea of using interactional slingshots as a means of providing support structure to user interactions in hybrid intelligence systems. Much like how gravitational slingshots provide boosts to spacecraft en route to their final destinations, so do interactional slingshots provide boosts to user interactions en route to solving tasks. Several challenges arise: What does this support structure look like? How much freedom does the user have in their interactions? How is user expertise paired with that of the machine’s? To do this as a tractable socio-technical problem, we explore this idea in the context of data annotation problems, especially in those domains where AI methods fail to solve the overall task. Getting annotated (labeled) data is crucial for successful AI methods, and becomes especially more difficult in domains where AI fails, since problems in such domains require human understanding to fully solve, but also present challenges related to annotator expertise, annotation freedom, and context curation from the data. To explore data annotation problems in this space, we develop techniques and workflows whose interactional slingshot support structure harnesses the user’s interaction with data. First, we explore providing support in the form of nudging non-expert users’ interactions as they annotate text data for the task of creating conversational memory. Second, we add support structure in the form of assisting non-expert users during the annotation process itself for the task of grounding natural language references to objects in 3D point clouds. Finally, we supply support in the form of guiding expert and non-expert users both before and during their annotations for the task of conversational disentanglement across multiple domains. We demonstrate that building hybrid intelligence systems with each of these interactional slingshot support mechanisms—nudging, assisting, and guiding a user’s interaction with data—improves annotation outcomes, such as annotation speed, accuracy, effort level, even when annotators’ expertise and skill levels vary. Thesis Statement: By providing support structure that nudges, assists, and guides user interactions, it is possible to create hybrid intelligence systems that enable more efficient (faster and/or more accurate) data annotation.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163138/1/sairohit_1.pd