Hämäläinen, 2006 - Google Patents
Descriptive and predictive modelling techniques for educational technologyHämäläinen, 2006
View PDF- Document ID
- 2070036193842945660
- Author
- Hämäläinen W
- Publication year
- Publication venue
- Licentiate thesis, Department of Computer Science, University of Joensuu
External Links
Snippet
Data-driven models are the basis of all adaptive systems. Adaption to the user requires that the models are driven from real user data. However, in educational technology real data is seldom used, and all general-purpose learning environments are predefined by the system …
- 238000000034 method 0 title abstract description 63
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F17/30 and subgroups
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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 |
---|---|---|
Han et al. | Data mining: concepts and techniques | |
Roiger | Data mining: a tutorial-based primer | |
Kantardzic | Data mining: concepts, models, methods, and algorithms | |
Pearson | Exploratory data analysis using R | |
Middleton | Capturing knowledge of user preferences with recommender systems | |
Braylan et al. | Modeling and aggregation of complex annotations via annotation distances | |
Aleven et al. | Domain modeling for AIED systems with connections to modeling student knowledge: A review | |
Malandri et al. | Taxoref: Embeddings evaluation for ai-driven taxonomy refinement | |
Ertel | Machine learning and data mining | |
Braylan et al. | A general model for aggregating annotations across simple, complex, and multi-object annotation tasks | |
US12124352B1 (en) | Apparatus and method generating a path using classified distractions | |
Ramezani | Modern statistical modeling in machine learning and big data analytics: Statistical models for continuous and categorical variables | |
Hämäläinen | Descriptive and predictive modelling techniques for educational technology | |
Liu | Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems | |
Kamoshida et al. | Automated clustering and knowledge acquisition support for beginners | |
Chaturvedi | Task-based example miner for intelligent tutoring systems | |
Guoliang | Knowledge Discovery with Bayesian Networks | |
Azcona | Artificial intelligence in computer science and mathematics education | |
Schouten | Exceptional Model Mining for Hierarchical Data | |
Kloptchenko | Text mining based on the prototype matching method | |
Fiacco | Functional Components as a Paradigm for Neural Model Explainability | |
Kurbalija | Time series analysis and prediction using case based reasoning technology | |
Shou et al. | Difficulty‐Based Knowledge Point Clustering Algorithm Using Students’ Multi‐Interactive Behaviors in Online Learning | |
Moraes | Unsupervised Concept Extraction in an Introduction to Programming Course | |
Shi | Knowledge Graph Extension by Entity Type Recognition |