Domain-general and domain-specific computations in single word processing

Domain-specific technologies, which are used for analysis, representation, and production in real-world contexts, differ from basic technologies, such as word processing software and Internet search tools. They cannot be used effectively without adequate command of fundamental domain-specific content knowledge. They can be used to deepen students' understanding of content, but these technologies bring distinct classroom-integration challenges. This chapter presents a framework for supporting in-service teachers to integrate these technologies. The research team derived this framework from data collected during an extended TPACK-style (Technological Pedagogical Content Knowledge) workshop that engaged 13 life science community college instructors in integrating bioinformatics technologies into courses. This chapter presents a case study about the challenges community college teachers faced in implementing these tools—and the strategies they used to address them. Challenges included activity translation, problem definition, implementation, and assessment.

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A Framework for Supporting In-Service Teachers to Use Domain-Specific Technologies for Instruction

Teacher Education ◽

10.4018/978-1-5225-0164-0.ch014 ◽

2016 ◽

pp. 253-285

Author(s):

Louise Yarnall ◽

Judith Fusco

Keyword(s):

Community College ◽

Content Knowledge ◽

Word Processing ◽

Problem Definition ◽

College Instructors ◽

Specific Content ◽

Pedagogical Content ◽

Domain Specific ◽

Community College Instructors ◽

Science Community

Domain-specific technologies, which are used for analysis, representation, and production in real-world contexts, differ from basic technologies, such as word processing software and Internet search tools. They cannot be used effectively without adequate command of fundamental domain-specific content knowledge. They can be used to deepen students' understanding of content, but these technologies bring distinct classroom-integration challenges. This chapter presents a framework for supporting in-service teachers to integrate these technologies. The research team derived this framework from data collected during an extended TPACK-style (Technological Pedagogical Content Knowledge) workshop that engaged 13 life science community college instructors in integrating bioinformatics technologies into courses. This chapter presents a case study about the challenges community college teachers faced in implementing these tools—and the strategies they used to address them. Challenges included activity translation, problem definition, implementation, and assessment.

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Positron emission tomographic studies of the cortical anatomy of single-word processing

Nature ◽

10.1038/331585a0 ◽

1988 ◽

Vol 331 (6157) ◽

pp. 585-589 ◽

Cited By ~ 1655

Author(s):

S. E. Petersen ◽

P. T. Fox ◽

M. I. Posner ◽

M. Mintun ◽

M. E. Raichle

Keyword(s):

Word Processing ◽

Positron Emission Tomographic ◽

Single Word ◽

Positron Emission

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Mandarin and English Single Word Processing Studied with Functional Magnetic Resonance Imaging

Journal of Neuroscience ◽

10.1523/jneurosci.19-08-03050.1999 ◽

1999 ◽

Vol 19 (8) ◽

pp. 3050-3056 ◽

Cited By ~ 260

Author(s):

Michael W. L. Chee ◽

Edsel W. L. Tan ◽

Thorsten Thiel

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Word Processing ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Single Word

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A Framework for Supporting In-Service Teachers to Use Domain-Specific Technologies for Instruction

Educational Leadership and Administration ◽

10.4018/978-1-5225-1624-8.ch018 ◽

2016 ◽

pp. 324-356

Author(s):

Louise Yarnall ◽

Judith Fusco

Keyword(s):

Community College ◽

Content Knowledge ◽

Word Processing ◽

Problem Definition ◽

College Instructors ◽

Specific Content ◽

Pedagogical Content ◽

Domain Specific ◽

Community College Instructors ◽

Science Community

Domain-specific technologies, which are used for analysis, representation, and production in real-world contexts, differ from basic technologies, such as word processing software and Internet search tools. They cannot be used effectively without adequate command of fundamental domain-specific content knowledge. They can be used to deepen students' understanding of content, but these technologies bring distinct classroom-integration challenges. This chapter presents a framework for supporting in-service teachers to integrate these technologies. The research team derived this framework from data collected during an extended TPACK-style (Technological Pedagogical Content Knowledge) workshop that engaged 13 life science community college instructors in integrating bioinformatics technologies into courses. This chapter presents a case study about the challenges community college teachers faced in implementing these tools—and the strategies they used to address them. Challenges included activity translation, problem definition, implementation, and assessment.

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A Framework for Supporting In-Service Teachers to Use Domain-Specific Technologies for Instruction

Handbook of Research on Teacher Education in the Digital Age - Advances in Higher Education and Professional Development ◽

10.4018/978-1-4666-8403-4.ch023 ◽

2015 ◽

pp. 604-634

Author(s):

Louise Yarnall ◽

Judith Fusco

Keyword(s):

Community College ◽

Content Knowledge ◽

Word Processing ◽

Problem Definition ◽

College Instructors ◽

Specific Content ◽

Pedagogical Content ◽

Domain Specific ◽

Community College Instructors ◽

Science Community

Domain-specific technologies, which are used for analysis, representation, and production in real-world contexts, differ from basic technologies, such as word processing software and Internet search tools. They cannot be used effectively without adequate command of fundamental domain-specific content knowledge. They can be used to deepen students' understanding of content, but these technologies bring distinct classroom-integration challenges. This chapter presents a framework for supporting in-service teachers to integrate these technologies. The research team derived this framework from data collected during an extended TPACK-style (Technological Pedagogical Content Knowledge) workshop that engaged 13 life science community college instructors in integrating bioinformatics technologies into courses. This chapter presents a case study about the challenges community college teachers faced in implementing these tools—and the strategies they used to address them. Challenges included activity translation, problem definition, implementation, and assessment.

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Domain Specific Lexicon Generation through Sentiment Analysis

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v15i09.13109 ◽

2020 ◽

Vol 15 (09) ◽

pp. 190

Author(s):

Kamran Shaukat ◽

Ibrahim A Hameed ◽

Suhuai Luo ◽

Imran Javed ◽

Farhat Iqbal ◽

...

Keyword(s):

Sentiment Analysis ◽

Word Meaning ◽

Context Dependency ◽

Single Word ◽

Specific Topic ◽

Huge Amount ◽

Domain Specific ◽

The Core ◽

Special Area ◽

Research Contribution

Sentiment analysis (SA) is used to extract opinions from a huge amount of data and these opinions are comprised of multiple words. Some words have different semantic meanings in different fields and we call them domain specific (DS) words. A domain is defined as a special area in which a collection of queries about a specific topic are held when user do queries in the data regarding the domain appear. But Single word can be interpreted in many ways based on its context-dependency. Demonstrate each word under its domain is extremely important because their meanings differ from each other so much in different domains that a word meaning from A in one context can change into Z in another context or domain. The purpose of this research is to discover the correct sentiment in the message or comment and evaluate it either it is positive, negative or neutral. We collected tweets dataset from different domains and analyze it to extract words that have a different definition in those specific domains as if they are used in other fields of life they would be defined differently. We analyzed 52115 words for finding their DS meaning in seven different domains. Polarity had been given to words of the dataset according to their domains and based on this polarity they have been recognized as positive negative and neutral and evaluated as domain-specific words. The automatic way is used to extract the words of the domain as we integrated and afterward the comparison to identify that either this word differs from other words as far as domain is concerned. This research contribution is a prototype that processes your data and extracts their domain-specific words automatically. This research improved the knowledge about the context-dependency and found the core-specific meanings of words in multiple fields.

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