Peerceptiv provides a platform for instructors to turn traditional assessments into opportunities for collaborative learning experiences. Collaborative assessment empowers instructors in any size class with the ability to promote writing in the discipline, engage students, and improve critical thinking and other “soft skill” outcomes. With Peerceptiv, instructors can assign higher-order formative assessments (writing, lab reports, design projects, presentations, etc.), engage students on the giving side of the feedback loop, provide a framework for academic dialogue among students, and deliver actionable feedback to students on demand. Instructors may participate in the assessment any way they choose, but the research-validated grading algorithms are powerful enough to allow the assessment to flow from publication date through grade generation without any instructor involvement at all.
This process-based method creates comprehensive student learning data on the giving and receiving side of peer assessment.
Many institutions across North America and Europe are already seeing the benefits of collaborative assessment. The University of Iowa uses Peerceptiv to track improvement of writing, reading, and collaboration skills. Professors are especially excited about the way students hold each other accountable for the quality of their reviews by “reviewing the reviewer” during the back-evaluation process. A science instructor at Texas A&M eliminated all multiple choice tests in his 300-student class and replaced them with collaborative assessments that include a variety of written projects, a video debate, and other formative feedback assignments that actively engage students in the learning process. This level of assessment scalability is only possible if there is a solid foundation in data and analytics and users can monitor status and measure outcomes across courses.
At the assignment level, Peerceptiv provides numerous metrics that help instructors make data-driven instructional decisions. Instructors can see how students perform on each rubric category as an individual or as an entire class. Peerceptiv generates data that measures the accuracy and helpfulness of each student’s reviews, and peer ratings are weighted by reviewer accuracy to produce writing scores. The focus is on improving the student’s ability to provide constructive feedback and collaborate with others, skills that are extremely desirable in academia and the workplace.
A reliability rating measures the level of agreement among peers in each rating rubric, an indication to the professor that the students both understand the underlying concept and can recognize good performance in that dimension. Depending on the type of assignment, professors could mine data about their students’ ability to create effective argumentation, think critically, analyze problems creatively, communicate in appropriate voice, and any other measure that is important to the success of their students in the discipline.
It’s also vitally important that schools have data at the institutional level so that administrators can make well informed decisions in all courses across the curriculum. Peerceptiv now offers an Administrator Dashboard that allows single login access to all courses, along with summary level stats, assignment metrics, and a link to IMS Caliper learning data. Caliper offers anonymized data broken down into a number of learning profiles, including session, assessment, and grading. Caliper is of great value in high-level, dereferenced analysis, while Peerceptiv additionally offers an API link so that instructional technologists can query data specific to the users and events of greatest interest. Peerceptiv provides actionable metrics to improve learning outcomes over time.
Please join Peerceptiv in our Partner Day Webinar on May 2, 12:45 PM Mountain Time, where we’ll discuss and answer questions on collaborative assessment strategies and analytics, Caliper learning data, and how best to use data to scale learning. Click here to sign up.
Technical Lead, Peerceptiv
Customer Support, Peerceptiv