Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 1 |
Descriptor
Intelligent Tutoring Systems | 2 |
Natural Language Processing | 2 |
Questionnaires | 2 |
Student Attitudes | 2 |
Teaching Methods | 2 |
Advanced Placement | 1 |
Biology | 1 |
Classification | 1 |
Computer Science Education | 1 |
Control Groups | 1 |
Data Analysis | 1 |
More ▼ |
Author
Baraniuk, Richard G. | 1 |
Grimaldi, Philip J. | 1 |
Lan, Andrew S. | 1 |
Lane, H. Chad | 1 |
Michalenko, Joshua J. | 1 |
VanLehn, Kurt | 1 |
Waters, Andrew E. | 1 |
Publication Type
Reports - Research | 2 |
Journal Articles | 1 |
Speeches/Meeting Papers | 1 |
Education Level
High Schools | 1 |
Higher Education | 1 |
Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G. – International Educational Data Mining Society, 2017
An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…
Descriptors: Data Analysis, Misconceptions, Student Attitudes, Feedback (Response)
Lane, H. Chad; VanLehn, Kurt – Computer Science Education, 2005
For beginning programmers, inadequate problem solving and planning skills are among the most salient of their weaknesses. In this paper, we test the efficacy of natural language tutoring to teach and scaffold acquisition of these skills. We describe ProPL (Pro-PELL), a dialogue-based intelligent tutoring system that elicits goal decompositions and…
Descriptors: Control Groups, Intelligent Tutoring Systems, Programming, Natural Language Processing