Schindler et al., 2020 - Google Patents
Identifying student strategies through eye tracking and unsupervised learning: The case of quantity recognitionSchindler 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 …
- 238000004458 analytical method 0 abstract description 11
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 | |
Cobb | Ontological innovation and the role of theory in design experiments | |
Jarrell et al. | Success, failure and emotions: Examining the relationship between performance feedback and emotions in diagnostic reasoning | |
Özgenel | A antecedent of teacher performance occupational commitment | |
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 | |
Ray et al. | Design and implementation of technology enabled affective learning using fusion of bio-physical and facial expression | |
JP2009297501A (en) | Cerebral function analysis support apparatus and program | |
Kingir et al. | Exploring Relations among Pre-Service Science Teachers' Motivational Beliefs, Learning Strategies and Constructivist Learning Environment Perceptions through Unsupervised Data Mining. | |
Stephen | Congruent functioning: The continuing resonance of Rogers’ theory | |
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 | |
Vasquez | Philosophical bases of research methods: An integrated narrative review |