Scaling biology cloud labs : K-12 education deployment and next generation biotic processing unit system

Cloud based science labs enable students at scale (and as a future goal also professional and citizen scientists) to execute realistic experiments remotely, log elaborative activities, and access data for sophisticated analysis. Previously, our lab, Stanford Riedel-Kruse Lab, deployed an interactive Biology Cloud Lab (BCL) as part of a Massive Open Online Course (MOOC) through the Open edX platform. This BCL enabled the real-time experimentation with photo-tactic Euglena gracilis cells - a single celled organism commonly used in K-12 education. In this thesis I now report (1) how teachers can integrate such a BCL successfully into their on-site school curriculum, (2) analysis of student behavior using the BCL platform and (3) improvements made to this BCL through advancing the Biotic Processing Unit (BPU) architecture. In my first contribution, I report how three middle and high-school teachers integrated this BCL into their on-site school curriculum over the course of 2 years with over 200 students. The teachers were successful in adapting the MOOC material and BCL for their respective classroom needs while integrating with components of their conventional curricular activities. The three teachers pursued versatile and distinctly different learning objectives, i.e., to introduce and practice large-scale data analysis, to prepare students to computationally simulate living microorganisms, or to motivate students towards hands-on, on-site microscopy experiments. This thesis illustrates that BCL has the potential to advance the type and depth of classroom science inquiry activities, provide flexibility for teachers to design their curricula based on their own specific classroom needs, and scale to large student numbers. Based on the results I provide recommendations for future development and wider deployment of educational cloud labs. In my second contribution, I use course learning analytics to derive key insights on student behavior before and after going through BCL-based courses. The in-depth analysis can provide the required feedback on the level of alignment of course design results to overall goals of STEM education. BCL causes students to be more interested about science, raise their self-confidence to take on scientific inquiry in the future, and shows student preference towards live experimentation over simulations. Therefore, incorporating BCL potentially aligns courses well with the authentic inquiry-based scientific approach promoted by national institutions, such as the National Research Council (NRC) and Next Generation Science Standards (NGSS). In my third contribution, I develop a new system architecture for Biotic Processing Unit (BPU), the domain-specific system which serves as the basic unit of BCL. Here, new hardware and software features are incorporated including an integrated circuit that improves controllability, sensors, and a feedback system. The first generation BPU introduced by Stanford Riedel-Kruse Lab was a proof-of-concept and yet to improve on major performance metrics, that as reported by end users is required to be met in order to achieve a higher level of Quality-of-Service (QoS). This dissertation focuses on these key metrics and introduces a more advanced system architecture that addresses the limitations of the previous version. Preliminary results show better performance through auto-configurable responsiveness and background density, and effective sensor integration to detect any initial false positive situation. In conclusion, this dissertation takes BCL to at-scale deployment in K-12 setting through effective teacher co-design process, uses student learning analytics to formulate meaningful feedback ensuring alignment with overall STEM education goals, and proposes a new BPU architecture to improve its performance.

Keywords: Electrical Engineering.
Advisor: Riedel-Kruse Hans
Hennessy John L. 
Olukotun Oyekunle Ayinde