Nothing Special   »   [go: up one dir, main page]

Schindler et al., 2020 - Google Patents

Identifying student strategies through eye tracking and unsupervised learning: The case of quantity recognition

Schindler et al., 2020

View PDF
Document ID
16033806848634937372
Author
Schindler M
Schaffernicht E
Lilienthal A
Publication year
Publication venue
44th Conference of the International Group for the Psychology of Mathematics Education, Khon Kaen University, Thailand (Virtual Meeting), July 21-22, 2020

External Links

Snippet

Identifying student strategies is an important endeavor in mathematics education research. Eye tracking (ET) has proven to be valuable for this purpose: Eg, analysis of ET videos allows for identification of student strategies, particularly in quantity recognition activities …
Continue reading at www.diva-portal.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
    • G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/28—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Graesser et al. Advancing the science of collaborative problem solving
Mcleod Qualitative vs quantitative research methods & data analysis
Jansen et al. Validation of the self-regulated online learning questionnaire
Soiferman Compare and Contrast Inductive and Deductive Research Approaches.
Jarrell et al. Success, failure and emotions: Examining the relationship between performance feedback and emotions in diagnostic reasoning
Chan et al. Multimodal learning analytics in a laboratory classroom
Pierce Examining the relationship between collective teacher efficacy and the emotional intelligence of elementary school principals
Schindler et al. Identifying student strategies through eye tracking and unsupervised learning: The case of quantity recognition
Maya et al. The Predictive Power of University Students' Self-Leadership Strategies on Their Self-Efficacy.
Roth et al. The emergence of 3d geometry from children's (teacher-guided) classification tasks
Zhen et al. Prediction of academic performance of students in online live classroom interactions—an analysis using natural language processing and deep learning methods
Dağyar et al. Gifted and talented youth leadership, perfectionism, and lifelong learning: Leadership, perfectionism, and lifelong learning
Liu et al. Learning human-compatible representations for case-based decision support
JP2009297501A (en) Cerebral function analysis support apparatus and program
Yoo et al. Toward a common validation methodology for segmentation and registration algorithms
Kingir et al. Exploring Relations among Pre-Service Science Teachers' Motivational Beliefs, Learning Strategies and Constructivist Learning Environment Perceptions through Unsupervised Data Mining.
Kuzu et al. The Subjects That the Pre-Service Classroom Teachers Perceive as Difficult in Elementary Mathematics Curriculum.
Boels et al. Automated gaze-based identification of students’ strategies in histogram tasks through an interpretable mathematical model and a machine learning algorithm
Chen Toward an Understanding for Assessing 21st-Century Skills: Based on Literature and National Assessment Practice.
Adebiyi et al. Affective e-learning approaches, technology and implementation model: a systematic review.
Şendurur et al. Development of metacognitive skills inventory for internet search (MSIIS): Exploratory and confirmatory factor analyses
Lee et al. Development of the CAT–FER: A computerized adaptive test of facial emotion recognition for adults with schizophrenia
Thom et al. Beyond Words/Signs
Soiferman Compare and contrast inductive and deductive
Guarino et al. Looking patterns during analogical reasoning: generalizable or task-specific?