The aggregation engine uses application programming interfaces (APIs) to find educational information and curricular resources from multiple types of external sources and internal sources including social media, internal drives, "what's trending" and learning trails.
The attribution engine uses Tacit Collection and Natural Language Processing, Machine Learning and Cognitive Computing to enable the collection, storage, tagging and SIS grade integration to build enough data to enable attribution of resources to student outcomes.
The integration service creates a series of content blocks by assembling learning objects and defined content placing the collections into "binders". The alignment service links the learning objects expectations and context blocks together to ready them for review and attribution. .
Learning is complex, and rarely follows a straight ahead path. By recording a learner’s “digital breadcrumbs,” Peeristics is able to record and visualize a learner’s journey through both curriculum and informal learning opportunities to plot student specific “learning trails”.
Learning content often exists in disparate places, making it difficult to find and use effectively. Peeristics searches sources from anywhere, including applications like Google Drive, Pinterest, OneNote, OneDrive and DropBox, as well as educational applications, connected repositories, personal learning environments, and learning management systems. Peeristics understands what is found where, but does not need to copy it into its environment.
Contextual metadata is added, which facilitates sharing and collaboration, and provides valuable insights into how resources are used in the classroom. Peeristics keeps data where its stored, without forcing data to be copied into the system, allowing teachers to get on with the job of teaching.
Integration adds context to content and connects curriculum resources and activities, providing new data points that can be used for critical decisions by ministries, school boards and teachers. By building context around teacher created material, and by tracking the use of external resources, educators can understand what materials work best for their students. As well, these new data points facilitate a wide-range of analysis for provincial or state jurisdictions to help better understand how to improve resource materials without having to resort to subjective anecdotal feedback.
The Peeristics innovative information architecture means that jurisdictions can attribute learning results to the use of specific teaching practices, resources and/or learning technologies. By generating more granular data than other learning platforms, Peeristics supports the personalization of curriculum, and can be used to develop diagnostics about the effectiveness of a curriculum or specific activities or resources.
Peeristics offers a variety of group and learning spaces where materials that have been aggregated and aligned with objectives can be engaged and worked on. The built-in virtual classroom has all the functions of a learning managements system, or Peeristics can gather data from an existing LMS. Collaboration and sharing are native functions of Peeristics, as is the provision of personal work spaces and dashboards.
Learning is complex, and rarely follows a straight ahead path. By recording a learner’s “digital breadcrumbs,” Peeristics is able to record and visualize a learner’s journey through both a curriculum and informal learning opportunities to plot a “learning trail,” and to attribute being exposed to specific resources and activities to student outcomes.