M E Schreiner
Biographic Data
| ID | 6212705 |
|---|---|
| NAME | M E Schreiner |
| GIVEN NAMES | M E |
| FAMILY NAME | Schreiner |
| SIGNATURE | SCHREINER M E |
| AFFILIATIONS | University of Colorado |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 13 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1995 |
| LATEST PUBLICATION YEAR | 1998 |
| H-INDEX | 2 |
Learning from text: Matching readers and texts by latent semantic analysis
This study examines the hypothesis that the ability of a reader to learn from text depends on the match between the background knowledge of the reader and the difficulty of the text information. Latent Semantic Analysis (LSA), a statistical technique that represents the content of a document as a vector in high‐dimensional semantic space based on a large text corpus, is used to predict how much readers will learn from texts based on the estimated…
Using latent semantic analysis to assess knowledge: Some technical considerations
In another article (Wolfe et al., 1998/this issue) we showed how Latent Semantic Analysis (LSA) can be used to assess student knowledge—how essays can be graded by LSA and how LSA can match students with appropriate instructional texts. We did this by comparing an essay written by a student with one or more target instructional texts in terms of the cosine between the vector representation of the student's essay and the instructional text in ques…
Assisting Text Processing: What Do We Need to Consider
42 undergraduates were presented one of two 25-sentence versions of a fictitious town. One version (route) described the town as a driver might encounter it, while the other (survey) received a geographic description. Sentences were printed one to a card and read aloud. Feedback included (1) limited access to a map, (2) limited opportunity to review previously read sentences, or (3) presentation of entire text after processing. Memorial represent…
Learning from text: Matching readers and texts by latent semantic analysis
This study examines the hypothesis that the ability of a reader to learn from text depends on the match between the background knowledge of the reader and the difficulty of the text information. Latent Semantic Analysis (LSA), a statistical technique that represents the content of a document as a vector in high‐dimensional semantic space based on a large text corpus, is used to predict how much readers will learn from texts based on the estimated…
Using latent semantic analysis to assess knowledge: Some technical considerations
In another article (Wolfe et al., 1998/this issue) we showed how Latent Semantic Analysis (LSA) can be used to assess student knowledge—how essays can be graded by LSA and how LSA can match students with appropriate instructional texts. We did this by comparing an essay written by a student with one or more target instructional texts in terms of the cosine between the vector representation of the student's essay and the instructional text in ques…
Assisting Text Processing: What Do We Need to Consider
42 undergraduates were presented one of two 25-sentence versions of a fictitious town. One version (route) described the town as a driver might encounter it, while the other (survey) received a geographic description. Sentences were printed one to a card and read aloud. Feedback included (1) limited access to a map, (2) limited opportunity to review previously read sentences, or (3) presentation of entire text after processing. Memorial represent…
Learning from text: Matching readers and texts by latent semantic analysis
This study examines the hypothesis that the ability of a reader to learn from text depends on the match between the background knowledge of the reader and the difficulty of the text information. Latent Semantic Analysis (LSA), a statistical technique that represents the content of a document as a vector in high‐dimensional semantic space based on a large text corpus, is used to predict how much readers will learn from texts based on the estimated…
Using latent semantic analysis to assess knowledge: Some technical considerations
In another article (Wolfe et al., 1998/this issue) we showed how Latent Semantic Analysis (LSA) can be used to assess student knowledge—how essays can be graded by LSA and how LSA can match students with appropriate instructional texts. We did this by comparing an essay written by a student with one or more target instructional texts in terms of the cosine between the vector representation of the student's essay and the instructional text in ques…
Computer Science (3 works) · Natural language processing (3 works) · Artificial Intelligence (2 works) · Latent semantic analysis (2 works) · Psychology (2 works) · Representation (politics (2 works) · Text Readability and Simplification (2 works) · Topic Modeling (2 works) · Advanced Text Analysis Techniques (1 works) · Cognitive psychology (1 works)