Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self‐regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student‐centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self‐regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self‐regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3‐year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.

How is a child's successful participation in science learning shaped by their family's support? We focus on the critical time period of early adolescents, testing (i) whether the child's perception of family support is important for both choice preferences to participate in optional learning experiences and engagement during science learning, and (ii) whether the effects on choice preferences and engagement are mediated through effects on child interest and self‐efficacy in science. Structural equation modeling is applied to data from two different contexts, one examining engagement during a science and technology center visit and the other examining engagement and learning during classroom instruction. Models from both datasets suggest that early adolescents' perceived family support for learning is associated with their choices for and engagement in science learning, and that these effects are mediated by effects on child interest and self‐efficacy in science. Further, children's family physical resources (e.g., available learning spaces and materials) predicts their perceived family support, but is not separately connected to either interest or self‐efficacy. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 450–472, 2016

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Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WIKIEVENTS which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WIKIEVENTS datasets respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model's trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE. 1Prosecutors say he drove the truck to Geary Lake in Kansas, that 4,000 pounds of ammonium nitrate laced with nitromethane were loaded into the truck there, and that it was driven to Oklahoma City and detonated.Elliott testified that on April 15, McVeigh came into the body shop and reserved the truck, to be picked up at 4pm two days later.

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