Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 4 |
Descriptor
Error Patterns | 5 |
Graphs | 5 |
Data Analysis | 2 |
Elementary Secondary Education | 2 |
Feedback (Response) | 2 |
Mathematics Instruction | 2 |
Accuracy | 1 |
Achievement Gains | 1 |
Adaptive Testing | 1 |
Anatomy | 1 |
Arithmetic | 1 |
More ▼ |
Author
Conrad Borchers | 1 |
Hallagan, Jean E. | 1 |
Höppner, Frank | 1 |
Jahnke, Maximilian | 1 |
Kenneth R. Koedinger | 1 |
Meng Xia | 1 |
Paulo F. Carvalho | 1 |
Pelánek, Radek | 1 |
Pinyang Liu | 1 |
Rihák, Ji?rí | 1 |
Rule, Audrey C. | 1 |
More ▼ |
Publication Type
Speeches/Meeting Papers | 5 |
Reports - Evaluative | 2 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Education Level
Elementary Education | 1 |
Elementary Secondary Education | 1 |
Higher Education | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Secondary Education | 1 |
Audience
Researchers | 1 |
Location
Czech Republic | 1 |
Laws, Policies, & Programs
Assessments and Surveys
National Assessment of… | 1 |
What Works Clearinghouse Rating
Jahnke, Maximilian; Höppner, Frank – International Educational Data Mining Society, 2022
The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java…
Descriptors: Programming, Coding, Computer Science Education, Error Patterns
Conrad Borchers; Paulo F. Carvalho; Meng Xia; Pinyang Liu; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2023
In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Mathematics Education, Learning Processes
Pelánek, Radek; Rihák, Ji?rí – International Educational Data Mining Society, 2016
In online educational systems we can easily collect and analyze extensive data about student learning. Current practice, however, focuses only on some aspects of these data, particularly on correctness of students answers. When a student answers incorrectly, the submitted wrong answer can give us valuable information. We provide an overview of…
Descriptors: Foreign Countries, Online Systems, Geography, Anatomy
Rule, Audrey C.; Hallagan, Jean E.; Shaffer, Barbara – Online Submission, 2008
Teachers need to address global climate change with students in their classrooms as evidence for consequences from these environmental changes mounts. One way to approach global climate change is through examination of authentic data. Mathematics and science may be integrated by interpreting graphs from the professional literature. This study…
Descriptors: Preservice Teachers, Elementary Education, Graphs, Climate
Tatsuoka, Kikumi K. – 1982
This study introduced a probabilistic model utilizing item response theory (IRT) for dealing with a variety of misconceptions. The model can be used for evaluating the transition behavior of error types, advancement of learning stages, or the stability and persistence of particular misconceptions. Moreover, it apparently can be used for relating…
Descriptors: Adaptive Testing, Elementary Secondary Education, Error Patterns, Evaluation Methods