Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
Source
| Grantee Submission | 2 |
Author
| Allison Liu | 1 |
| Andrew A. McReynolds | 1 |
| Ashish Gurung | 1 |
| Eglington, Luke G. | 1 |
| Kirk P. Vanacore | 1 |
| Neil T. Heffernan | 1 |
| Pavlik, Philip I., Jr. | 1 |
| Stacy T. Shaw | 1 |
Publication Type
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Kirk P. Vanacore; Ashish Gurung; Andrew A. McReynolds; Allison Liu; Stacy T. Shaw; Neil T. Heffernan – Grantee Submission, 2023
As evidence grows supporting the importance of non-cognitive factors in learning, computer-assisted learning platforms increasingly incorporate non-academic interventions to influence student learning and learning related-behaviors. Non-cognitive interventions often attempt to influence students' mindset, motivation, or metacognitive reflection to…
Descriptors: Intervention, Program Effectiveness, Student Behavior, Computer Assisted Instruction

Peer reviewed
Direct link
