Artificial Intelligent in Education
Abstract
:1. Introduction
2. Artificial Intelligence in Education (AIED)
2.1. The Objectives of the AIED
2.2. The Strategy of the AIED
2.3. Example of AIED Tools: Intelligent Tutoring Systems
2.3.1. The Domain Model
2.3.2. The Educational Model
2.3.3. The Model of the Learner
3. Educational Data Mining (EDM)
3.1. Attempts to Define EDM
3.2. The Main Approaches in EDM
3.3. Examples of Applications of EDM
4. Learning Analytics: Towards Decision Support Tools in a Learning Context
4.1. Definition of Learning Analytics
4.2. Objectives of LA
4.3. Research Methodology in Leanring Analytics
4.4. Types of Data Used in Learning Analytics
4.5. LA and EDM: Similarities and Differences
- Prediction methods,
- Discovery of the structure,
- Mining relationship,
- Distillation of data for human judgment,
- Discovery with models, and
- Tools for conducting EDM/LA methods.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category of Method | Goals of Method | Key Applications |
---|---|---|
Prediction | Develop a model which can infer a single aspect of the data (predicted variable) from some combination of other aspectsof the data (predictor variables) | Detecting student behaviors (e.g., gaming the system, off-task behavior, slipping); developing domain models; predicting and understanding student educational outcomes. |
Clustering | Find data points that naturally group together, slipping the full data set into a set of categories. | Discovery of new student behavior patterns; investigating similarities and differences between schools |
Relationship mining | Discover relationships between variables | Discovery of curricular associations in course sequences; Discovering which pedagogical strategies lead to more effective/robust learning |
Discovery with models | A model of a phenomenon developed with prediction, clustering or knowledge engineering, is used as a component in further prediction or relationship mining | Discovery of relationships between student behaviors, and student characteristics or contextual variables; Analysis of research questions across wide variety of contexts. |
Distillation of data for Human judgment | Data is distilled to enable a human to quickly identify or classify features of the data. | Human Identification of patterns in student learning, behavior, or collaboration; Labeling data for use in later development of prediction model |
Data Generated by LMS | Data Generated by Instructor |
---|---|
Number of times resource accessed | Grades on discussion forum |
Date and time of access | Grades on Assignment |
Number of discussion posts generated | Grades on tests |
Number of dissussion posts read | Final Grades |
Types of resource accessed | Number (and type) of questions asked in a discussion forum. |
Number of Emails sent to instructor |
LA | EDM | |
---|---|---|
Discovery | Leveraging human judgment is key; automated discovery is a tool to accomplish this goal. | Automated discovery is key; leveraging human judgment is a tool to accomplish this goal |
Reduction & holism | Stronger emphasis on understanding systems as wholes, in their full complexity. | Stronger emphasis on reducing to components and analyzing individual components between them |
Origins | LA has stronger origins in semantic web, “intelligent curriculum”, outcome prediction, and systemic interventions. | LED has strong origins in educational software and student modeling, with a significant community in predicting course outcomes. |
Adaptation and personalization | Greater focus on informing and empowering instructors and learners | Greater focus on automated adaption (e.g., by te computer with no human in the loop) |
Techniques and methods | Social network analysis, sentiment analysis, influence analytics, discourse analysis, learner success prediction, concept analysis, sensemaking models | Classification, clustering, Bayesian modeling, relationship mining, visualization |
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Hamal, O.; El Faddouli, N.-E.; Harouni, M.H.A.; Lu, J. Artificial Intelligent in Education. Sustainability 2022, 14, 2862. https://doi.org/10.3390/su14052862
Hamal O, El Faddouli N-E, Harouni MHA, Lu J. Artificial Intelligent in Education. Sustainability. 2022; 14(5):2862. https://doi.org/10.3390/su14052862
Chicago/Turabian StyleHamal, Oussama, Nour-Eddine El Faddouli, Moulay Hachem Alaoui Harouni, and Joan Lu. 2022. "Artificial Intelligent in Education" Sustainability 14, no. 5: 2862. https://doi.org/10.3390/su14052862
APA StyleHamal, O., El Faddouli, N.-E., Harouni, M. H. A., & Lu, J. (2022). Artificial Intelligent in Education. Sustainability, 14(5), 2862. https://doi.org/10.3390/su14052862