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Continuous Learning and Automated Scoring in Science

Congratulations...New Grant Awarded to...

Marcia C. Linn, James D. Slotta, Lydia Liu, Jon Breitbart, Libby Gerard, Doug Kirkpatrick, 
Jacquie Madhok, Camillia Matuk, Kelly Ryoo, Hiroki Terashima

Entitled: CLASS: Continuous Learning and Automated Scoring in Science

The Continuous Learning and Automated Scoring in Science project (CLASS) is a full research and development proposal that addresses the first DR K-12 program challenge: How can enhanced assessments of student knowledge and skills advance STEM learning? CLASS will investigate how automated scoring of inquiry assessments can increase success for diverse students, improve teachers’ instructional practices, and inform administrators’ decisions about professional development, inquiry instruction, and assessment.

CLASS harnesses the power of cyberlearning by implementing automated scoring of inquiry activities in a middle school curriculum using the Web-based Inquiry Science Environment (WISE). Whereas classroom tests, district benchmarks, and statewide assessments provide only infrequent, summative snapshots of performance, CLASS will continuously assess student understanding during inquiry activities and use the scores to provide guidance. Results will be synthesized in design principles to help new designers improve inquiry activities.

CLASS will work with 5 middle schools serving over 4000 students in 3 diverse school districts. Comparison studies will investigate effective design of instruction, automated assessment, adaptive guidance, and professional development to improve student learning outcomes.

Intellectual merit. CLASS responds to the national need for validated science inquiry assessments that efficiently and effectively measure inquiry learning, provide inputs for adaptive student guidance, and help teachers and administrators contribute to student success. Current science assessments emphasize recall of factual information over development of conceptual understanding and inhibit use of inquiry learning. CLASS will take advantage of new technologies to develop and test automated assessment activities to capture students’ abilities to integrate their ideas and form coherent scientific arguments. CLASS will develop automated scoring for short essays, science narratives, MySystem (a form of concept map), graphing problems, and virtual experiments.

In general, students benefit from detailed guidance rather than from general suggestions and from scaffolded inquiry rather than from direct instruction. CLASS will study how best to combine information from automated scores and teacher inputs to provide adaptive guidance by comparing alternative forms of guidance. CLASS will study how these scores can help teachers monitor student progress. CLASS will form a community of principals to explore how best to provide evidence from CLASS scores that can inform administrative decision-making.

Broader impacts. CLASS will research how best to adapt instruction so all students can benefit from inquiry science. By broadening indicators of progress, CLASS will increase opportunities for students of diverse abilities to succeed, as well as support efficient, evidence-based decision-making by teachers and administrators. In successful inquiry instruction, teachers must be sensitive to what kind of feedback to provide, to whom, and when – requiring that they keep track of multiple students, each working at different paces, and each with individual needs. CLASS will use automated scoring of five assessment activities to give teachers a nuanced image of their student capabilities, including trajectories for each student. To support teachers and administrators to effectively interpret and apply CLASS data to instructional decisions, CLASS will develop online tools such as interactive data reports. By integrating assessment and online guidance into inquiry learning, CLASS will personalize instruction so diverse students can succeed on high stakes tests without sacrificing the opportunity to engage in meaningful science learning.

Note: New Post Doc will be advertised T.B.A.
Joint EARLI SIG 20 & SIG 26 meeting: August 22-24,...

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