Works (4)
Updated: July 5th, 2023 15:38
2021 article
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor (September, 10.1007/s40593-020-00213-3, 2020)
Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2021, March). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 31, pp. 154–155.
link.springer.com (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: December 21, 2020
2021 article
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor
Cody, C., Maniktala, M., Lytle, N., Chi, M., & Barnes, T. (2021, May 21). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.
author keywords: Tutoring system; Hints; Assistance; Data-driven methods
TL;DR:
The influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course is investigated to suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: June 10, 2021
2020 journal article
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 30(4), 637–667.
author keywords: Intelligent tutoring system; Help avoidance; User experience; Unsolicited hints; Aptitude-treatment interaction; Logic proofs; Productive persistence; Clustering; problem solving
TL;DR:
Encouraging evidence is provided that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance is provided.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 9, 2020
2015 article
Decision Tree Learning for Fraud Detection in Consumer Energy Consumption
2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), pp. 1175–1179.
author keywords: fraud detection; decision trees; smart meter data
TL;DR:
This research reports on a novel application of decision tree learning technique to profile normal energy consumption behavior allowing for the detection of potentially fraudulent activity.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Source: Web Of Science
Added: August 6, 2018
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