Briefly Inq-ITS (inqits.com) is a platform for science inquiry. Instead of taking multiple choice tests on rote science knowledge for assessment, students conduct inquiry with interactive, virtual labs in the Inq-ITS system. While kids “show what they know”, educators get real-time, actionable reports and alerts on a teacher dashboard called Inq-Blotter. All grading on students’ competencies on science practices are done automatically using patented artificial intelligence algorithms. Reports and alerts enable teachers to pinpoint which students need help most, and on what practices (skills). Inq-Blotter responds to a current challenge for teachers and students, namely the need to hone Next Generation Science Standards’ inquiry practices (NGSS Lead States, 2013).

In this project, we conducted a number of studies with Inq-ITS and Inq-Blotter to evaluate how it was being used and whether it supported teachers’ assessment and instruction of science practices. In Study 1, we examined the teacher discourse that is generated when teachers respond to alerts. Thirty recordings of discourse from middle school teachers were collected as teachers responded to alerts, which were fired in real time in response to students’ difficulties with science inquiry practices in Inq-ITS. We examined whether teacher help based on Inq-Blotter improved students’ performance on students’ next opportunity to engage in the practice on which they were helped. Analyses showed that 80% of students improved on their next opportunity to engage in an inquiry practice after receiving help from their teacher. In Study 2 we examined whether help from teachers, triggered by alerts, improves students’ competencies on the next task for the inquiry practice on which they were helped. Our exploratory study suggested that for the practice of data analysis, students who were helped by a teacher who used Full Blotter (with alerts) demonstrated better performance by their third lab activity compared to students whose teachers used Minimal Blotter (no alerts). In studies 3a, we investigated the fine-grained discourse analysis to investigate differences in the patterns in the teachers’ feedback following the alerts. Overall, the findings of the present study reveal that the majority of students improved after receiving help, but the pattern of discursive feedback provided by each teacher differed significantly. In Study 3b, we examined whether there were particular patterns of support as identified using epistemic network analyses that promoted inquiry learning across multiple activities. We identified differences in the patterns of support related to student improvement; while lower level scaffolds may benefit students’ short-term inquiry performance, higher level inquiry combined with content help were found to be more robust. These findings have important implications for the design of alerts for teachers to, in turn, support their students. Overall, our findings show the benefits of real-time actionable alerts in terms of guiding teacher scaffolding and supporting student inquiry performance.

Intellectual Merit: The proposed project, deeply informed by learning sciences theory and undergirded by sophisticated technologies, combines techniques to measure students’ inquiry practices at scale to study how teachers interact with their students when provided real-time, alerts. In doing so, it can realize teachers’ needs for formative assessments for science practices as well as students’ needs’ for learning inquiry. It also provides principles that can guide the design and development of alerting tools for teachers by advancing the state-of-the-art using a technologically-based approach to close the formative assessment loop for an ill-defined domain, namely, science inquiry. This work contributes to the well-acknowledged need to better support teachers’ data use in classrooms, as outlined in the U.S. Department of Education’s National Education Technology Plan 2010, as well as support students in STEM learning, as outlined in the Every Student Succeeds Act. 

Broader Impact: By researching use of a real-time classroom alerting tool, Inq-Blotter, we provide new opportunities for teachers to utilize fine-grained assessment data so that it can be used in real time with students as they conduct inquiry in Inq-ITS (Inquiry Intelligent Tutoring System; Gobert et al., 2012, 2013). By conducting our research with diverse populations in a domain where many American students are falling behind (science), and where there is great need for the United States’ economy, the proposed benefits of the project are amplified.

Last Modified: 02/11/2020
Modified by: Janice D Gobert