This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. \emphIn the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model'') and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries.

Here we describe the use of learning analytics (LA) for investigating inquiry-based science instruction. We define several variables that quantify curriculum usage and leverage tools from process mining to examine inquiry-based pedagogical processes. These are initial steps toward measuring and modeling fidelity of implementation of a science curriculum. We use data from one school district's use of an online science curriculum (N=1,021 teachers and nearly 330,000 page views).

In this paper, we discuss a scalable approach for integrating learning analytics into an online K-12 science curriculum. A description of the curriculum and the underlying pedagogical framework is followed by a discussion of the challenges to be tackled as part of this integration. We also include examples of data visualization based on real student and teacher data. With more than one million students and fifty thousand teachers using the curriculum, a massive and rich dataset is continuously updated. This repository depicts teacher and students usage of an inquiry-based science program, and offers exciting opportunities to leverage research to improve both teaching and learning. The growing dataset, with more than a hundred million items of activity in six months, also poses technical challenges such as data storage, complex aggregation and analysis with broader implications for pedagogy, big data, and learning.