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Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learningrelated constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established 740335R ERXXX10.

The use of recombinant gene technologies by the vaccine industry has revolutionized the way antigens are generated, and has provided safer, more effective means of protecting animals and humans against bacterial and viral pathogens. Viral and bacterial antigens for recombinant subunit vaccines have been produced in a variety of organisms. Transgenic plants are now recognized as legitimate sources for these proteins, especially in the developing area of oral vaccines, because antigens have been shown to be correctly processed in plants into forms that elicit immune responses when fed to animals or humans. Antigens expressed in maize (Zea mays) are particularly attractive since they can be deposited in the natural storage vessel, the corn seed, and can be conveniently delivered to any organism that consumes grain. We have previously demonstrated high level expression of the B-subunit of Escherichia coli heat-labile enterotoxin and the spike protein of swine transmissible gastroenteritis in corn, and have demonstrated that these antigens delivered in the seed elicit protective immune responses. Here we provide additional data to support the potency, efficacy, and stability of recombinant subunit vaccines delivered in maize seed.

Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model. We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012. This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models. Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.

In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover‐based terrain sensing, this paper proposes a self‐supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover–terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision‐based) terrain classifier. Once trained, the vision‐based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers—a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration—as well as a vision‐based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self‐supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four‐wheeled robot in an outdoor Mars‐analogue environment. © 2012 Wiley Periodicals, Inc.

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Effective collaboration in data science can leverage domain expertise from each team member and thus improve the quality and efficiency of the work. Computational notebooks give data scientists a convenient interactive solution for sharing and keeping track of the data exploration process through a combination of code, narrative text, visualizations, and other rich media. In this paper, we report how synchronous editing in computational notebooks changes the way data scientists work together compared to working on individual notebooks. We first conducted a formative survey with 195 data scientists to understand their past experience with collaboration in the context of data science. Next, we carried out an observational study of 24 data scientists working in pairs remotely to solve a typical data science predictive modeling problem, working on either notebooks supported by synchronous groupware or individual notebooks in a collaborative setting. The study showed that working on the synchronous notebooks improves collaboration by creating a shared context, encouraging more exploration, and reducing communication costs. However, the current synchronous editing features may lead to unbalanced participation and activity interference without strategic coordination. The synchronous notebooks may also amplify the tension between quick exploration and clear explanations. Building on these findings, we propose several design implications aimed at better supporting collaborative editing in computational notebooks, and thus improving efficiency in teamwork among data scientists.