What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Law
- Mathematics education
Danielle S. McNamara focuses on Cohesion, Reading comprehension, Comprehension, Coh-Metrix and Computational linguistics.
Her work carried out in the field of Cohesion brings together such families of science as Sentence, Second language writing, Verb and Writing quality.
Her Reading comprehension study integrates concerns from other disciplines, such as Cognitive psychology and Domain knowledge.
Her Comprehension research is multidisciplinary, incorporating perspectives in Mathematics education, Knowledge level, Inference, Free recall and Reciprocal teaching.
Her Coh-Metrix research incorporates elements of Concreteness, Coherence, Text linguistics, Syntax and Semantics.
Natural language processing and Artificial intelligence are the main topics of her Computational linguistics study.
Her most cited work include:
- Are Good Texts Always Better? Interactions of Text Coherence, Background Knowledge, and Levels of Understanding in Learning From Text (1014 citations)
- Are Good Texts Always Better? Interactions of Text Coherence, Background Knowledge, and Levels of Understanding in Learning From Text (1014 citations)
- Coh-Metrix: Analysis of text on cohesion and language (913 citations)
What are the main themes of her work throughout her whole career to date?
Danielle S. McNamara mainly investigates Artificial intelligence, Natural language processing, Comprehension, Reading comprehension and Mathematics education.
Her Artificial intelligence study combines topics in areas such as Lexical diversity, Machine learning and Readability.
Her study focuses on the intersection of Natural language processing and fields such as Cohesion with connections in the field of Writing quality.
Her research in Comprehension tackles topics such as Reading which are related to areas like Sentence.
Her work in Reading comprehension is not limited to one particular discipline; it also encompasses Knowledge level.
Danielle S. McNamara combines subjects such as Pedagogy and Intelligent tutoring system with her study of Mathematics education.
She most often published in these fields:
- Artificial intelligence (31.75%)
- Natural language processing (28.92%)
- Comprehension (25.04%)
What were the highlights of her more recent work (between 2017-2021)?
- Comprehension (25.04%)
- Reading comprehension (23.99%)
- Artificial intelligence (31.75%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Comprehension, Reading comprehension, Artificial intelligence, Mathematics education and Natural language processing.
Her research brings together the fields of Reading and Comprehension.
Her Reading comprehension research includes elements of Cognitive psychology, Active reading, Educational technology, Intelligent tutoring system and Summative assessment.
Danielle S. McNamara has included themes like Machine learning and Set in her Artificial intelligence study.
Her study in the field of Formative assessment, Writing instruction and TUTOR is also linked to topics like Network analysis.
Her research in Natural language processing intersects with topics in Cohesion, Mental representation and Source text.
Between 2017 and 2021, her most popular works were:
- Improving Methodological Standards in Behavioral Interventions for Cognitive Enhancement (60 citations)
- Cohesion network analysis of CSCL participation. (34 citations)
- Reading Comprehension and Metacognition: The Importance of Inferential Skills (12 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Law
- Machine learning
Her primary scientific interests are in Artificial intelligence, Reading comprehension, Natural language processing, Comprehension and Machine learning.
Her Artificial intelligence study integrates concerns from other disciplines, such as Health literacy and Reading.
Her Reading comprehension research integrates issues from Mathematics education, Educational technology, Cognitive psychology and Summative assessment.
The Cognitive psychology study combines topics in areas such as Knowledge level, Metacognition and Affect.
Her work on Computational linguistics as part of general Natural language processing study is frequently linked to Electronic mail and Gold standard, therefore connecting diverse disciplines of science.
Her Comprehension study frequently draws connections to other fields, such as Automatic summarization.
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