What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Carolyn Penstein Rosé focuses on Artificial intelligence, Natural language processing, Collaborative learning, Machine learning and Multimedia.
She works on Artificial intelligence which deals in particular with Parsing.
She has researched Natural language processing in several fields, including Modality, Speech recognition, Learning gain and German.
Her Collaborative learning study combines topics from a wide range of disciplines, such as Cooperative learning, Educational technology, Coding and Human–computer interaction.
Her Naive Bayes classifier and Supervised learning study in the realm of Machine learning connects with subjects such as Rose and Frame.
The concepts of her Multimedia study are interwoven with issues in Dialog system, Computer literacy, Online course, Construct and Exploit.
Her most cited work include:
- When Are Tutorial Dialogues More Effective Than Reading (315 citations)
- CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites (304 citations)
- Intelligent tutoring systems with conversational dialogue (281 citations)
What are the main themes of her work throughout her whole career to date?
Carolyn Penstein Rosé mostly deals with Artificial intelligence, Natural language processing, Collaborative learning, Knowledge management and Context.
Her Artificial intelligence research incorporates elements of Machine learning, Task and Identification.
Her research integrates issues of Speech recognition and Conversation in her study of Natural language processing.
In her work, Multimedia is strongly intertwined with Human–computer interaction, which is a subfield of Collaborative learning.
Her research investigates the connection between Knowledge management and topics such as Data science that intersect with problems in Process.
Her Mathematics education research is mostly focused on the topic TUTOR.
She most often published in these fields:
- Artificial intelligence (30.31%)
- Natural language processing (20.47%)
- Collaborative learning (15.80%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (30.31%)
- Natural language processing (20.47%)
- Task (12.69%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Artificial intelligence, Natural language processing, Task, Conversation and Data science.
Her Artificial intelligence research is multidisciplinary, incorporating elements of Event, Machine learning, Process and Textual entailment.
Carolyn Penstein Rosé combines subjects such as Semantic information, Semantics, Coreference and Benchmark with her study of Natural language processing.
Her Task research includes themes of Domain, Context, Software development, Interpretation and Pipeline.
Her studies deal with areas such as Computational linguistics and User experience design as well as Data science.
Carolyn Penstein Rosé interconnects Collaborative learning and Computer-supported collaborative learning in the investigation of issues within Instructional design.
Between 2018 and 2021, her most popular works were:
- Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design (36 citations)
- EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference (20 citations)
- Exploring Numeracy in Word Embeddings. (16 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
Artificial intelligence, Task, Natural language processing, Learning analytics and Benchmark are her primary areas of study.
Her Artificial intelligence study incorporates themes from Machine learning, Meaning and Textual entailment.
Her Machine learning research incorporates themes from Event trigger, Learning sciences, Process and Identification.
The study incorporates disciplines such as Domain, Adversarial system, Academic writing and Conditional random field in addition to Task.
Her Natural language processing study combines topics in areas such as Embedding, Event, Semantic memory and Coreference.
Her research in Learning analytics intersects with topics in Structured prediction, Rhetorical question and Human–computer interaction.
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