Materials Similar to Machine learning predicts responses to conceptual tasks using eye movements

home » Detail Page » Similar Materials

Materials Similar to Machine learning predicts responses to conceptual tasks using eye movements

38%: How do Multimedia Hints Affect Students’ Eye Movements in Conceptual Physics Problems?
36%: Investigative Science Learning Environment: Using the processes of science and cognitive strategies to learn physics
36%: Using machine learning to understand physics graduate school admissions
35%: How Physics Teachers Model Student Thinking and Plan Instructional Responses When Using Learning-Progression-Based Assessment Information
34%: Using conceptual metaphor and functional grammar to explore how language used in physics affects student learning
33%: Visualizing patterns in CSEM responses to assess student conceptual understanding
32%: Using Large-Scale Classroom Research to Study Student Conceptual Learning in Mechanics and to Develop New Approaches to Learning
32%: Using multimedia learning modules in a hybrid-online course in electricity and magnetism
31%: Comparing Student Learning with Multiple Research-Based Conceptual Surveys: CSEM and BEMA
31%: Finding meaningful search features for automated analysis of short responses to conceptual questions
31%: Using time-on-task measurements to understand student performance in a physics class: A four-year study
31%: Do perceptually salient elements in physics problems influence students' eye movements and answer choices?
31%: Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores
31%: Investigating the Relationship between Active Learning Task Characteristics and Student Success
31%: Multidimensional Item Response Theory and the Conceptual Survey of Electricity and Magnetism
31%: Characterizing active learning environments in physics: network analysis of Peer Instruction classroom using ERGMs
30%: Using Collaborative Group Exams to Investigate Students’ Ability to Learn
30%: Preliminary results for the development and deployment of Conceptual Learning Assessment Instruments Methodology Survey (CLAIMS)
30%: Using asynchronous communication to support virtual faculty learning communities