Name
Abstract | ||
|---|---|---|
Comprehension assessment is an essential tool in classroom learning. However, the judgment often relies on experience of an instructor who makes observation of students' behavior during the lessons. We argue that students should report their own comprehension explicitly in a classroom. With students' comprehension made available at the slide level, we apply a machine learning technique to classify presentation slides according to comprehension levels. Our experimental result suggests that presentation-based features are as predictive as bag-of-words feature vector which is proved successful in text classification tasks. Our analysis on presentation-based features reveals possible causes of poor lecture comprehension. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TLT.2011.22 | TLT |
Keywords | Field | DocType |
support vector machines,possible cause,comprehension assessment,own comprehension,presentation-based feature,essential tool,poor lecture comprehension,estimate slide comprehension,comprehension level,classroom learning,bag-of-words feature vector,feature vector,support vector machine,feature extraction,materials,machine learning,data analysis,kernel,comprehension,educational technology,prediction,psychology,learning artificial intelligence,accuracy,bag of words,questionnaires,svm | Educational technology,Kernel (linear algebra),Feature vector,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Natural language processing,Comprehension,Machine learning | Journal |
Volume | Issue | ISSN |
5 | 1 | 1939-1382 |
Citations | PageRank | References |
3 | 0.37 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nimit Pattanasri | 1 | 21 | 4.24 |
| Masayuki Mukunoki | 2 | 199 | 21.86 |
| Michihiko Minoh | 3 | 349 | 58.69 |