.png){kind=icon}
.png){kind=icon}
Return
Data mining and education
.png){kind=icon}
PRE
AI
DOI：10.1002/wcs.1350.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. WIREs Cogn Sci 2015, 6:333-353. doi: 10.1002/wcs.1350 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article.
Keywords:
INTELLIGENT TUTORING SYSTEMS
LEARNING-FACTORS ANALYSIS
COGNITIVE TASK-ANALYSIS
MODELS
DIALOGUE
EMOTION
SUPPORT
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
W
IF:
3.8
Papers:
613
Citations:
2.8K
View more organizations