Principal Investigator: Janice Gobert
CoPrincipal Investigator(s): Michael Sao Pedro
Organization: Rutgers University New Brunswick
Abstract:
The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects design and build new kinds of learning technologies in order to explore their viability, to understand the challenges to using them effectively, and to study their potential for fostering learning. This EXP project addresses the need for real-time diagnostic tools for teachers that can assess students’ needs, i.e. provide formative assessment, in order to improve science instruction. The project will extend, pilot, implement, and study Inq-Blotter, a scalable, web-based alerting system that enables teachers’ formative assessment of middle school students’ Physical Science scientific practices, aligned to the newly released national framework Next Generation Science Standards. The Inq-Blotter alerting system will be used in conjunction with Inq-ITS (Inquiry Intelligent Tutoring System), in which students “show what they know” by conducting inquiry with simulations. Students form questions, design and conduct experiments, interpret data, warrant their claims with data, and communicate their findings. As students work, they are assessed in real-time by the algorithms of Inq-ITS. To complete the formative assessment loop, the Inq-Blotter alerting tool sends real time alerts to teachers’ laptops and smartphones on students’ inquiry skills so that the teachers know who needs the most help and on which skills. Discourse between teachers and students will be analyzed to better understand how this alerting system can support teachers’ real-time instruction of inquiry and how it can foster students to learn inquiry practices in real-time and transfer them to subsequent activities, thereby contributing to practical knowledge about how science inquiry is taught and learned.
Unique to Inq-ITS is its ability to automatically assess inquiry using algorithms based on knowledge-engineering and data mining, making reliable alerting possible. By adding logging and timestamping to Inq-Blotter of every interaction a teacher has with its interface, the PIs will introduce the ability to capture and analyze teacher-student interactions on an extremely fine-grained scale, in turn allowing for maximum leverage of the algorithmic assessment capability of Inq-ITS. This combination of measuring students’ inquiry practices in real-time at scale with a technological tool that facilitates real-time, targeted instruction could revolutionize how teachers interact with students during inquiry-based science instruction. The project will advance the state of the art of using a technology-based approach to close the formative assessment loop for the ill-defined domain of science inquiry. This research will also evince the broader principles surrounding this technological genre so as to guide the design of human-computer interfaces of other alerting tools for teachers and inform how learning-analytics techniques can best be utilized in such tools.
