This project explores scalable, low-cost platforms that enables multiple users to run independently or in collaboration, in real time or not, real biological experiments via interactive web applications. The aim of the project is to create a tools that are aligned with recent curricular frameworks (Next Generation Science Standards) that integrate scientific practices along with content and bring inquiry-based science approaches in massively scaled online learning.

The first prototype is designed for spatio-temporal stimulation experiments with the physarum polycephalum. The second platform allows for real-time interactive experiments on phototaxis of Euglena. All hardware is assembled from consumer electronics, and all software is open source.

Background

The life sciences will be the “physics” of the 21st century, bringing groundbreaking discoveries and innovations to humanity. This new reality requires new approaches for K-12 and higher education, especially ones that enable “deep inquiry” practices, as recognized by the Next Generation Science Standards. However, within the recently intensified large-scale movement towards online education, deep inquiry seems to be absent from the most prominent initiatives such as MOOCs or video-based lecture libraries such as Khan Academy. The lack of effective integration of real lab experiences is still an unsolved problem in general, but especially in the life sciences. Virtual experimentation face limitations for realism and flexibility of exploration, as biological and biochemical systems are far more complex and less well understood than classical mechanical systems, thus more difficult to simulate.

Remote experimentation has been is been increasingly tested and employed in educational settings over the past 15 years. However, most of the initiatives have focused on physics and engineering education, but not at all on the life sciences. We believe that Biology could be particularly suited for remote laboratories for science inquiry, as (1) Biology is particularly complex with dynamics less well understood than man-made electronics and classical physics, and the biological imagery can provide lots of visually exciting experiences and motivational incentives – hence we expect a higher potential for discovery and exploration on the student side; (2) Cellular and micro-ecological phenomena happen on small spatial scales, while advancements, automation, and cost-reduction in high-throughput life-science equipment (robots, automated microscopes, microfluidics etc.) will increasingly enable to run massive numbers of biological experiments in parallel at very low cost per user (we expect eventually below 1$/user/month); (3) Biology laboratories are expensive with respect to resources, personell, and time commitment, whereas remote labs would not only be easier for the teacher to setup with additional flexibility in tailoring experiments, but also potentially give access to very expensive or even unsafe equipment.

Finally, there is general ongoing debate about the relative advantages (and limitations) of hands-on environments versus virtual simulation platforms for experimentation for various educational aspects. Remote labs might provide novel insights by breaking up this dichotomy. Remote labs might enable students to focus better on data acquisition and conceptual understanding due to the automatization of content-irrelevant experimentation techniques. Additionally, remote labs might leverage deeper motivation and excitement than virtual simulation platforms because they are "real". These are issues that remain to be explored with this project.

Goal

The goal of the project is are three-folds: First, the exploration of optimal design principles for remote biology experimentation platforms for educational purposes aligned with the NGSS; second, the development of Learning Analytics that assess learning processes through applied scientific practices, as well as enable teachers to track and monitor student progress; third, the integration of the platforms into online learning system to enable inquiry-based science approaches.

The system will be employed science courses either in-person or online, both in K-12 schools as well as in higher education institutions, and extend several weeks to a semester of integration.

Process

The first prototype is designed for spatio-temporal stimulation experiments with the physarum polycephalum.

The hardware consists of a robotic pipetting device with a gantry system, which was built using LEGO Mindstorm, mounted atop a regular flatbed scanner. It is designed such that it involves readily accessible components, and can be reproduced by anyone with reasonable LEGO Mindstorm experience.

The user interface enables the remote user to execute experiments and assess previously recorded experimental data. Logged on, the user can determine where stimuli are to be deposited onto a plate by clicking on any location within the plate. More complex instructions such as delivering the stimulus at a future time point are also possible.

The second platform is an interactive system to investigate phototactic behaviors of Euglena Gracilis in realtime.

The Euglena live in a microfluidic chip in a microscope that is located in Riedel-Kruse Lab. Four LEDs that can be controlled remotely are attached in the plane on the four edges of the microfluidic chip to provide input stimulus, and a webcam mounted to the microscope provides live images. The user can interact with that system through the live lab interface that has three main components, a software joystick to control in realtime the light directions, a live video stream widget to observe Euglena movement and a secondary live video stream widget to observe the entire hardware in action. Experiments are stored as a set of image and light data that can be accessed and further processed through other web applications.

Both system track all user interactions such that all the experiments are reproducible post-hoc. This allows us to employ data mining techniques to extract patterns of interactions, both on short and long time scales, compare experiments, and find regularities across students.