SEERNet includes 5 digital learning platforms (DLPs) that connect researchers to millions of teachers and learners across US K-12 schools and post-secondary institutions. Each DLP has their own unique users and capabilities for conducting research – this site is a central resource for researchers looking to understand what each platform offers.
This guide aims to answer the following set of questions that researchers might ask when considering each platform:
This guide highlights the capabilities of each digital learning platform for external researchers interested in conducting experimental studies. The below table highlights the similarities and differences between the DLPs at varying stages of the research process – more detailed information on each platform, as well as other possibilities for non-experimental studies or alternate collaborations can be obtained from the platforms themselves. As network lead, SEERNet welcomes questions and feedback from interested partners – stay tuned for more content and future opportunities!
These five digital learning platforms (DLPs) have the power of scale: ASSISTment’s E-TRIALS, MATHia/UpGrade, Canvas + Terracotta, Kinetic by OpenStax, and ASU Learning @ Scale each have at least 100,000 users. Read on for an overview of each DLP:
We have 6,000 problems with more than one support for each one and they have been randomized to over 300 students each. Educational data scientists might ask questions about the features of these supports (i.e., are hints that are shorter correlated with better student learning). Learning scientists will ask questions that contrast specific features of interventions they are interested in exploring.
UpGrade is an open-source A/B testing platform that facilitates randomized experiments on digital learning experiences. Currently it allows experiments to contrast the type/sequence/timing of secondary math content within MATHia, but by the end of the grant period it will enable connections to other software applications.
Terracotta is an open-source research platform that facilitates randomized experiments on learning activities within Canvas. It allows researchers to evaluate the content, context, timing, and mode of learning activities. The learning activities manipulated in Terracotta have the potential to be designed and implemented differentially at the class-level.
Kinetic will enable research on a wide spectrum of postsecondary learner outcomes related to behavior, performance, and psychosocial constructs. Depending upon the version of Kinetic and the exact outcomes of interest, researchers might design their studies using pre- and post-intervention assessments, collection of self-report or external data from students, and/or longitudinal analyses over multiple time scales using student records in OpenStax products. The alpha version will allow researchers to administer any measure that can be delivered via Qualtrics, while the beta version will allow linking of these researcher-administered measures to existing learner outcomes in OpenStax materials.
The L@S data warehouse will allow researchers to conduct several types of exploratory analysis and future designs may allow experimental interventions.
|Kinetic OpenStax||Learning at Scale (ASU)|
|K-12 math students using OER math curriculum||6-12 math students using Mathia, teachers using MATHia||6-16 students using Canvas||Post-secondary students preferably using OpenStax textbooks||Post-secondary online ASU students|
|Effectiveness of student supports for math learning||Improvements to student learning based on alternative presentations of material. Also motivational and related improvements due to design, messaging, etc.||Students’ behavior in learning activities, and the effects of learning activities on student performance (or any outcome score in Canvas, or manually added by the teacher).|
Learner characteristics and their influence on behavior, performance, and psychosocial constructs. 3 key guiding questions: who is the learner ? (individual differences), what are they learning? (contextual information), how are they learning? (context – learning strategies)
|ASU L@S affords a wide range of questions regarding learning in credit-bearing courses that utilize long-term and short-term student performance data and various student demographics.|
|Pre-register on OSF.io|
Verify feasibility of intervention with Carnegie Learning design team and interested district, including completing pre-registration form
|No formal vetting process. Teachers are in control.||Pre-register on OSF.io recommended||ASU Provost’s Office|
|Normal educational practice covered by existing ASSISTments IRB, external researchers get an IRB to receive data.||Researchers use own IRB (if needed)||Researchers use own IRB protocol||Researchers submit to Rice IRB||ASU IRB, researchers use own IRB|
No recruitment necessary – all users eligible. The timing of the study will depend on when the teachers assign the problems as determined by the curriculum order/time of year
|Carnegie Learning will assist researchers in recruiting school(s)/district(s) using Mathia’s customer base, and will assist with data-sharing agreements with these districts.||Teachers (at institutions where Terracotta has been integrated) recruit students to participate in study. In the event that the researcher is not a teacher, the researcher recruits teachers to participate.||Students opt-in, incentivized, institutional partnerships||Recruitment depends on existing data or implementing interventions/surveys.|
|Randomization||Student-level random assignment||Individual or group random assignment (class, teacher, school, district)|
Student-level random assignment AND student/assignment-level randomization (within-subject crossovers)
|Student-level random assignment||Affords randomization at individual or group level depending on research question.|
|Intervention||Set of student supports for one or more problems||Alternate unit of instruction/activity in Mathia. Messaging, hints, presentation and design features.||Assignments||Open-ended based on capabilities of Qualtrics||Affords randomization at individual or group level depending on research question.|
Prior achievement/ demographic data
Class/group membership, school/class-level contextual data, prior ASSISTments achievement
|Class/group membership, prior Mathia achievement||Existing data within Canvas course site (gradebook, activity, assignments), and any student-level data added by the Teacher||Learner characteristics collected by Kinetic across studies||Data warehouse will contain demographic, achievement, course activity data|
|Performance on Similar-but-Not-the-Same (SNS) problems||Mathia process measures, performance, and survey measures||Canvas gradebook, activity, assignments, data added by teacher||Researcher-administered measures in Qualtrics In future versions, connections to institutional data (course grades, etc)||Course activity, grades, persistence/graduation|
|Analysis||Data export, posted to OSF.io||Data export||Data export, possible analysis tools||Secure data enclave allows researchers to run analysis with full dataset without access to PII||Data warehouse|