A low text representation of the content of text can reveal rhetorical structure or orchestration (or their absence). Cmap representation can have a valuable place in the writing center toolkit.
This document outlines a presentation about using low-level reading texts to teach transition skills to English language learners. It discusses two frameworks - Critical Thinking and Learning Strategies - that can be used to incorporate transition skills into beginning ESL classes. Specific subskills are provided for each framework, along with sample activities and ways teachers can check if they already incorporate or could try certain skills in their instruction. The goal is to help teachers adapt existing lessons to include more opportunities for students to develop transition skills.
This document provides an introduction and overview of a course on data structures and algorithms. It discusses the importance and fundamental nature of the topics covered in the course. The course will focus on commonly used data structures like lists, trees, and graphs, as well as related algorithms for tasks like searching, sorting, and graph operations. It will also cover reasoning about the correctness and efficiency of algorithms. The document provides recommendations for textbooks to reference and related courses that provide useful background knowledge.
AUTOMATED SHORT ANSWER GRADER USING FRIENDSHIP GRAPHScsandit
The paper proposes a method to assess short answer written by student using friendship matrix,
representation of friendship graph. The Short Answer is a type of answer which is based on
facts. These answers are quite different from long answers and Multiple Choice Question
(MCQ) type answers. The friendship graph is a graph which is based on friendship condition
i.e. the nodes have only one common neighbor. Friendship matrix is the matrix form of the
friendship graph. The student answer is stored in a friendship matrix and the teacher answer is
stored in another friendship matrix and both the matrices are compared. Based on the number
of errors encountered from student answer an error marks is calculated and that number is
subtracted from full marks to get student grade.
Interactive Analysis of Word Vector Embeddingsgleicher
Word vector embeddings present challenges for interactive analysis due to their high-dimensional nature and complex relationships between words. The authors conducted a task analysis of common uses of word embeddings which revealed 7 linguistic tasks. They designed 3 visualizations - Buddy Plots, Concept Axis Plots, and Co-occurrence Matrices - to support the tasks of understanding word similarities, co-occurrences, and semantic directions within concept axes. An online system implements the visualizations to enable interactive exploration of word vector embeddings.
This document outlines the eligibility requirements and admission process for an MPhil/PhD program in Computer Science at an unnamed institution. Applicants must have a bachelor's and master's degree in computer science or a related field with minimum marks. The admission process involves a written entrance test covering mathematics and computer science topics, followed by an interview. The test is 3 hours long and has 70 multiple choice questions testing undergraduate and post-graduate level knowledge. Candidates who score a minimum of 50% on both the test and interview will be eligible for admission.
The document describes a method for learning design patterns from hierarchical labeled data using grammar induction and Bayesian modeling. The method begins with an initial grammar derived directly from the exemplars, then uses Markov chain Monte Carlo optimization to explore more general grammars formed by merging and splitting nonterminal symbols. The optimal grammar balances descriptive power over the exemplars against representation complexity, distilling general patterns from the data in a principled way. The method is demonstrated on geometric models and web pages.
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
A Deep Learning Model to Predict Congressional Roll Call Votes from Legislati...mlaij
This document describes a deep learning model called the Predict Text Classification Network (PTCN) that was developed to predict the outcome (pass/fail) of congressional roll call votes based solely on the text of legislation. The PTCN uses a hybrid convolutional and long short-term memory neural network architecture to analyze legislative texts and predict whether a vote will pass or fail. The model was tested on legislative texts from 2000-2019 and achieved an average prediction accuracy of 67.32% using 10-fold cross-validation, suggesting it can recognize patterns in language that correlate with congressional voting behaviors.
This document outlines a presentation about using low-level reading texts to teach transition skills to English language learners. It discusses two frameworks - Critical Thinking and Learning Strategies - that can be used to incorporate transition skills into beginning ESL classes. Specific subskills are provided for each framework, along with sample activities and ways teachers can check if they already incorporate or could try certain skills in their instruction. The goal is to help teachers adapt existing lessons to include more opportunities for students to develop transition skills.
This document provides an introduction and overview of a course on data structures and algorithms. It discusses the importance and fundamental nature of the topics covered in the course. The course will focus on commonly used data structures like lists, trees, and graphs, as well as related algorithms for tasks like searching, sorting, and graph operations. It will also cover reasoning about the correctness and efficiency of algorithms. The document provides recommendations for textbooks to reference and related courses that provide useful background knowledge.
AUTOMATED SHORT ANSWER GRADER USING FRIENDSHIP GRAPHScsandit
The paper proposes a method to assess short answer written by student using friendship matrix,
representation of friendship graph. The Short Answer is a type of answer which is based on
facts. These answers are quite different from long answers and Multiple Choice Question
(MCQ) type answers. The friendship graph is a graph which is based on friendship condition
i.e. the nodes have only one common neighbor. Friendship matrix is the matrix form of the
friendship graph. The student answer is stored in a friendship matrix and the teacher answer is
stored in another friendship matrix and both the matrices are compared. Based on the number
of errors encountered from student answer an error marks is calculated and that number is
subtracted from full marks to get student grade.
Interactive Analysis of Word Vector Embeddingsgleicher
Word vector embeddings present challenges for interactive analysis due to their high-dimensional nature and complex relationships between words. The authors conducted a task analysis of common uses of word embeddings which revealed 7 linguistic tasks. They designed 3 visualizations - Buddy Plots, Concept Axis Plots, and Co-occurrence Matrices - to support the tasks of understanding word similarities, co-occurrences, and semantic directions within concept axes. An online system implements the visualizations to enable interactive exploration of word vector embeddings.
This document outlines the eligibility requirements and admission process for an MPhil/PhD program in Computer Science at an unnamed institution. Applicants must have a bachelor's and master's degree in computer science or a related field with minimum marks. The admission process involves a written entrance test covering mathematics and computer science topics, followed by an interview. The test is 3 hours long and has 70 multiple choice questions testing undergraduate and post-graduate level knowledge. Candidates who score a minimum of 50% on both the test and interview will be eligible for admission.
The document describes a method for learning design patterns from hierarchical labeled data using grammar induction and Bayesian modeling. The method begins with an initial grammar derived directly from the exemplars, then uses Markov chain Monte Carlo optimization to explore more general grammars formed by merging and splitting nonterminal symbols. The optimal grammar balances descriptive power over the exemplars against representation complexity, distilling general patterns from the data in a principled way. The method is demonstrated on geometric models and web pages.
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
A Deep Learning Model to Predict Congressional Roll Call Votes from Legislati...mlaij
This document describes a deep learning model called the Predict Text Classification Network (PTCN) that was developed to predict the outcome (pass/fail) of congressional roll call votes based solely on the text of legislation. The PTCN uses a hybrid convolutional and long short-term memory neural network architecture to analyze legislative texts and predict whether a vote will pass or fail. The model was tested on legislative texts from 2000-2019 and achieved an average prediction accuracy of 67.32% using 10-fold cross-validation, suggesting it can recognize patterns in language that correlate with congressional voting behaviors.
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
The document describes a case study using concept maps (Cmaps) to help EAP students improve their academic writing skills. The students mapped the introduction section of a research paper under constraints. They then critiqued their maps and created a consensus map. Based only on the consensus map, the students rewrote the introduction section. The students found that cycling between mapping and text analysis helped them better understand the paper's structure and argument. The case study suggests Cmaps are useful instructional tools, especially for identifying rhetorical structure in difficult texts.
Cmap Tools as an essential for teaching academic writingLawrie Hunter
IT tools are great, but they must take their place among other tools, some of them not recognized as technology, e.g. the paragraph is technology - didn't you knowtice?
Academic writing process: Cmaps as an essential tool (JALTCALL 2013, Matsumoto)Lawrie Hunter
The document describes a case study of using concept mapping (Cmaps) with English for Academic Purposes (EAP) students to improve their academic writing. It discusses how the students cycled between mapping texts and analyzing texts using Cmaps and text analysis tools. By mapping introductions to research papers and critiquing and revising the maps, the students were able to produce improved summaries. The case study suggests Cmaps are an effective tool for identifying rhetorical structure and aiding in academic writing.
Workshop
[Delivered at joint 8th International Conference on ESP in Asia and 3rd International Symposium on Innovative Teaching and Research in ESP, UEC, Tokyo. August 21, 2016]
In presentations, particularly during conference presentation Q&A, sci-tech EAP learners often prove unable to distil the underlying intentions of their research design or to identify the argument(s) surrounding their claim and the generalizability of their results.
These EAP learners usually have little training in rhetorical orchestration, especially since their research papers are built on the IMRAD structure, a rather poor metaphor for argument. As a result, these learners find spontaneous oral explanation and argument summarization difficult.
This workshop introduces the operation of a structured, low-text approach which has produced consistent, rapid development of the foundation target skills (argument analysis, argument construction) in classroom application (masters and PhD level). The key tool in this approach is the cross-platform freeware CmapTools, now widely adopted in science education. CmapTools automatically generates Novakian maps (maps in which each link is articulated by a relation phrase). Learners find these maps easy to evaluate in terms of correctness of relations and shockingly accessible in terms of structure of information.
This workshop begins with an overview of current styles of concept visualization (and their attendant syntax and information structures) so as to give participants a broad practical overview of mapping practice today. Participants will then be introduced to the use of CmapTools, and will take part in guided model task performance.
The workshop activities will be low-tech (post-its and marker pens) to maximize accessibility.
However, participants who would like to 'lean in' on this skill set are encouraged to download Cmap Tools to their laptops (Mac, Win or Linux) or iPads, familiarize themselves with the basic functions of the software (takes about 15 minutes), and show up equipped for bigger-curve learning.
This document discusses different graphical representations that can be used in tutoring systems, including conceptual graphs, ontologies, and concept maps. It examines how each representation has been utilized in tutoring systems, including representing the domain or learning material, diagnosing student errors, and assessing student knowledge through visual concept mapping exercises. Concept maps, ontologies, and conceptual graphs each have their own characteristics that make them suitable for different uses in tutoring systems.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring
systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms,
for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses
what is way of the utilization of every formalism and the offering of the potential outcomes to assist the
student in education systems.
GRAPHICAL REPRESENTATION IN TUTORING SYSTEMSijcsit
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
A Semantic Scoring Rubric For Concept Maps Design And ReliabilityLiz Adams
The document describes the development of a semantic scoring rubric for concept maps created by teachers in Panama. It discusses the need to develop objective measurement tools to assess how well concept maps were helping teachers foster meaningful learning. The rubric was designed to classify maps based on increasing levels of semantic complexity and quality of content. It includes six criteria: concept relevance and completeness, correct propositional structure, presence of misconceptions, dynamic propositions, number and quality of cross-links, and presence of cycles. Testing showed classifying maps by semantic levels was challenging due to uneven progression across criteria for different learners. So a point-based rubric was developed instead of levels, and score ranges correspond to overall content quality.
Knowledge maps for e-learning. Jae Hwa Lee, Aviv Segev
Maps such as concept maps and knowledge maps are often used as learning materials. These maps havenodes and links, nodes as key concepts and links as relationships between key concepts. From a map, theuser can recognize the important concepts and the relationships between them. To build concept orknowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the costof map creation is high. In this study, an attempt was made to automatically build a domain knowledgemap for e-learning using text mining techniques. From a set of documents about a specific topic,keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based onranking pairs of keywords according to the number of appearances in a sentence and the number ofwords in a sentence. The experiments analyzed the number of relations required to identify theimportant ideas in the text. In addition, the experiments compared K-map learning to document learningand found that K-map identifies the more important ideas
Concept mapping involves creating a visual representation of concepts and their relationships. Concept maps can take different forms like spider, hierarchy, or flowchart diagrams. They are used for assessment, group thinking, planning, and research. Key aspects of concept maps include concepts, relationships between concepts shown through lines and propositions. Strategies for using concept maps in the classroom include fill-in-the-blank, partial or pre-selected term mapping, and using them as advanced organizers. Concept maps can be assessed based on accurate identification of concepts and relationships between them.
Semantic organization tools allow students to analyze and organize concepts by creating visual representations that show relationships between ideas. There are two main types: semantic networks/concept maps where students draw visual maps linking related concepts; and databases which are electronic systems that can be used for data storage, retrieval, and linking information. Examples of semantic organization tools discussed are Inspiration software for creating concept maps, and using spreadsheets like Excel to generate concept maps from organized data. Steps for constructing concept maps involve selecting key concepts, ranking them by abstraction, clustering related ideas, and arranging them in a diagram.
See the trees: Concept mapping for text analysisLawrie Hunter
Presented at Writing Centers Association of Japan, March 2013.
This presentation reports several cases of fruitful application of Cmap Tools freeware, wherein EAP learners of academic writing discovered intellectual leverage in mapping. In each case the learners drew constrained maps of the content of a text (academic or popular genre), critiqued their maps, arrived at consensus on an accurate mapping, and then set out to write a new version of the text based only on the content of the map. The subsequent work was rich, which points to valuable applications in writing center work.
The document discusses different types of mapping including concept mapping, argument mapping, information structure mapping, syntactic mapping, and association mapping. It provides details on Novakian concept mapping using Cmap Tools and Hunter's information structure mapping using PowerPoint. The document also discusses matching different mapping styles to instructional purposes and considering constraints like architectural, rhetorical, and relational constraints when deciding on a mapping approach.
The document discusses several interactive and constructive models of reading:
- Rumelhart's model emphasizes how bottom-up and top-down processes interact through parallel application of sensory and non-sensory information.
- Stanovich's interactive-compensatory model proposes readers use bottom-up and top-down processes simultaneously and flexibly depending on purpose and knowledge, allowing compensation across levels.
- Anderson and Pearson's schema-theoretic view focuses on the role of schemas in anchoring new information through interaction of old and new knowledge.
- Pearson and Tierney's reading/writing model views reading as an act of composing meaning through negotiation between reader and writer.
A Web-Based Training System For Business Letter WritingJeff Nelson
This document introduces a web-based intelligent training system to assist workers in drafting business letters. The system is based on constructivism theory and self-directed learning. It uses a case-based reasoning approach to suggest rhetorical business letters from a case library that users can adapt to their specific contexts. The system aims to help users effectively transfer knowledge and acquire skills for business letter writing without direct instruction.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
I love designing learning experiences. I have worked with children through adults, in academic and corporate environments. Please contact me with any questions.
Development and Evaluation of Concept Maps as Viable Educational Technology t...paperpublications3
Abstract: This study had developed and evaluated concept maps as viable educational technology to facilitate learning and assessment. The development process concluded upon establishing validity and reliability. These maps were classified into two: concept maps to facilitate learning; and, fill-in-the-maps to facilitate assessment. A one group pre-test-posttest pre-experimental design was employed. Fill-in-the-maps were utilized for unit pre-tests and posttests. Complete concept maps were used to facilitate learning. For midterm examination, students were given composition as basis for constructing concept map. For final examination, students were provided concept maps to write their own composition. Rubrics were used to assess students’ outputs. z-test for correlated means showed significant increases of Mean Percentage Score (MPS) from pre-test to posttest. The overall posttest result was correlated with those of objective, fill-in-the-map, map construction and composition writing. Significant correlations were observed. Results accentuated that concept maps can be developed and evaluated to facilitate learning and assessment.
This document provides an overview of 8 lectures on human-computer interaction and interaction design. Lecture 1 introduces the scope and challenges of the field. Lecture 2 discusses visual representation, including text, maps, graphs, and schematic drawings. Lecture 3 covers text and gesture interaction as well as the evolution of interaction hardware. The remaining lectures cover topics like inference-based approaches, augmented reality, usability of programming languages, user-centered design research methods, and usability evaluation methods.
This document provides an overview of the lectures for a course on Human Computer Interaction and Interaction Design. The 8 lectures cover topics such as visual representation, text and gesture interaction, inference-based approaches, augmented reality, usability of programming languages, and usability evaluation methods. The first lecture introduces HCI and discusses how understanding users' needs and potential solutions can help create intuitive and powerful software, while noting that usability is not guaranteed. Recommended reading materials are also listed that cover theoretical approaches, practical design methods, and research methods in HCI.
Overview of CPC writing support for G-cube doctoral students 23.01.12Lawrie Hunter
This document describes editing and writing services offered by Lawrie Hunter to G-cube PhD students. It outlines 5 main services: 1) Editing papers and providing coded feedback, 2) Mentoring to improve conformity and readability, 3) Support with presentation skills and materials, 4) Assistance with complexity management using mapping tools, 5) Guidance on academic writing styles. Students can meet with Hunter for an initial assessment and discussion of needs. Examples are provided of Hunter's edited comments and mapping diagrams to illustrate the types of support available.
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Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
The document describes a case study using concept maps (Cmaps) to help EAP students improve their academic writing skills. The students mapped the introduction section of a research paper under constraints. They then critiqued their maps and created a consensus map. Based only on the consensus map, the students rewrote the introduction section. The students found that cycling between mapping and text analysis helped them better understand the paper's structure and argument. The case study suggests Cmaps are useful instructional tools, especially for identifying rhetorical structure in difficult texts.
Cmap Tools as an essential for teaching academic writingLawrie Hunter
IT tools are great, but they must take their place among other tools, some of them not recognized as technology, e.g. the paragraph is technology - didn't you knowtice?
Academic writing process: Cmaps as an essential tool (JALTCALL 2013, Matsumoto)Lawrie Hunter
The document describes a case study of using concept mapping (Cmaps) with English for Academic Purposes (EAP) students to improve their academic writing. It discusses how the students cycled between mapping texts and analyzing texts using Cmaps and text analysis tools. By mapping introductions to research papers and critiquing and revising the maps, the students were able to produce improved summaries. The case study suggests Cmaps are an effective tool for identifying rhetorical structure and aiding in academic writing.
Workshop
[Delivered at joint 8th International Conference on ESP in Asia and 3rd International Symposium on Innovative Teaching and Research in ESP, UEC, Tokyo. August 21, 2016]
In presentations, particularly during conference presentation Q&A, sci-tech EAP learners often prove unable to distil the underlying intentions of their research design or to identify the argument(s) surrounding their claim and the generalizability of their results.
These EAP learners usually have little training in rhetorical orchestration, especially since their research papers are built on the IMRAD structure, a rather poor metaphor for argument. As a result, these learners find spontaneous oral explanation and argument summarization difficult.
This workshop introduces the operation of a structured, low-text approach which has produced consistent, rapid development of the foundation target skills (argument analysis, argument construction) in classroom application (masters and PhD level). The key tool in this approach is the cross-platform freeware CmapTools, now widely adopted in science education. CmapTools automatically generates Novakian maps (maps in which each link is articulated by a relation phrase). Learners find these maps easy to evaluate in terms of correctness of relations and shockingly accessible in terms of structure of information.
This workshop begins with an overview of current styles of concept visualization (and their attendant syntax and information structures) so as to give participants a broad practical overview of mapping practice today. Participants will then be introduced to the use of CmapTools, and will take part in guided model task performance.
The workshop activities will be low-tech (post-its and marker pens) to maximize accessibility.
However, participants who would like to 'lean in' on this skill set are encouraged to download Cmap Tools to their laptops (Mac, Win or Linux) or iPads, familiarize themselves with the basic functions of the software (takes about 15 minutes), and show up equipped for bigger-curve learning.
This document discusses different graphical representations that can be used in tutoring systems, including conceptual graphs, ontologies, and concept maps. It examines how each representation has been utilized in tutoring systems, including representing the domain or learning material, diagnosing student errors, and assessing student knowledge through visual concept mapping exercises. Concept maps, ontologies, and conceptual graphs each have their own characteristics that make them suitable for different uses in tutoring systems.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring
systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms,
for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses
what is way of the utilization of every formalism and the offering of the potential outcomes to assist the
student in education systems.
GRAPHICAL REPRESENTATION IN TUTORING SYSTEMSijcsit
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
A Semantic Scoring Rubric For Concept Maps Design And ReliabilityLiz Adams
The document describes the development of a semantic scoring rubric for concept maps created by teachers in Panama. It discusses the need to develop objective measurement tools to assess how well concept maps were helping teachers foster meaningful learning. The rubric was designed to classify maps based on increasing levels of semantic complexity and quality of content. It includes six criteria: concept relevance and completeness, correct propositional structure, presence of misconceptions, dynamic propositions, number and quality of cross-links, and presence of cycles. Testing showed classifying maps by semantic levels was challenging due to uneven progression across criteria for different learners. So a point-based rubric was developed instead of levels, and score ranges correspond to overall content quality.
Knowledge maps for e-learning. Jae Hwa Lee, Aviv Segev
Maps such as concept maps and knowledge maps are often used as learning materials. These maps havenodes and links, nodes as key concepts and links as relationships between key concepts. From a map, theuser can recognize the important concepts and the relationships between them. To build concept orknowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the costof map creation is high. In this study, an attempt was made to automatically build a domain knowledgemap for e-learning using text mining techniques. From a set of documents about a specific topic,keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based onranking pairs of keywords according to the number of appearances in a sentence and the number ofwords in a sentence. The experiments analyzed the number of relations required to identify theimportant ideas in the text. In addition, the experiments compared K-map learning to document learningand found that K-map identifies the more important ideas
Concept mapping involves creating a visual representation of concepts and their relationships. Concept maps can take different forms like spider, hierarchy, or flowchart diagrams. They are used for assessment, group thinking, planning, and research. Key aspects of concept maps include concepts, relationships between concepts shown through lines and propositions. Strategies for using concept maps in the classroom include fill-in-the-blank, partial or pre-selected term mapping, and using them as advanced organizers. Concept maps can be assessed based on accurate identification of concepts and relationships between them.
Semantic organization tools allow students to analyze and organize concepts by creating visual representations that show relationships between ideas. There are two main types: semantic networks/concept maps where students draw visual maps linking related concepts; and databases which are electronic systems that can be used for data storage, retrieval, and linking information. Examples of semantic organization tools discussed are Inspiration software for creating concept maps, and using spreadsheets like Excel to generate concept maps from organized data. Steps for constructing concept maps involve selecting key concepts, ranking them by abstraction, clustering related ideas, and arranging them in a diagram.
See the trees: Concept mapping for text analysisLawrie Hunter
Presented at Writing Centers Association of Japan, March 2013.
This presentation reports several cases of fruitful application of Cmap Tools freeware, wherein EAP learners of academic writing discovered intellectual leverage in mapping. In each case the learners drew constrained maps of the content of a text (academic or popular genre), critiqued their maps, arrived at consensus on an accurate mapping, and then set out to write a new version of the text based only on the content of the map. The subsequent work was rich, which points to valuable applications in writing center work.
The document discusses different types of mapping including concept mapping, argument mapping, information structure mapping, syntactic mapping, and association mapping. It provides details on Novakian concept mapping using Cmap Tools and Hunter's information structure mapping using PowerPoint. The document also discusses matching different mapping styles to instructional purposes and considering constraints like architectural, rhetorical, and relational constraints when deciding on a mapping approach.
The document discusses several interactive and constructive models of reading:
- Rumelhart's model emphasizes how bottom-up and top-down processes interact through parallel application of sensory and non-sensory information.
- Stanovich's interactive-compensatory model proposes readers use bottom-up and top-down processes simultaneously and flexibly depending on purpose and knowledge, allowing compensation across levels.
- Anderson and Pearson's schema-theoretic view focuses on the role of schemas in anchoring new information through interaction of old and new knowledge.
- Pearson and Tierney's reading/writing model views reading as an act of composing meaning through negotiation between reader and writer.
A Web-Based Training System For Business Letter WritingJeff Nelson
This document introduces a web-based intelligent training system to assist workers in drafting business letters. The system is based on constructivism theory and self-directed learning. It uses a case-based reasoning approach to suggest rhetorical business letters from a case library that users can adapt to their specific contexts. The system aims to help users effectively transfer knowledge and acquire skills for business letter writing without direct instruction.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
I love designing learning experiences. I have worked with children through adults, in academic and corporate environments. Please contact me with any questions.
Development and Evaluation of Concept Maps as Viable Educational Technology t...paperpublications3
Abstract: This study had developed and evaluated concept maps as viable educational technology to facilitate learning and assessment. The development process concluded upon establishing validity and reliability. These maps were classified into two: concept maps to facilitate learning; and, fill-in-the-maps to facilitate assessment. A one group pre-test-posttest pre-experimental design was employed. Fill-in-the-maps were utilized for unit pre-tests and posttests. Complete concept maps were used to facilitate learning. For midterm examination, students were given composition as basis for constructing concept map. For final examination, students were provided concept maps to write their own composition. Rubrics were used to assess students’ outputs. z-test for correlated means showed significant increases of Mean Percentage Score (MPS) from pre-test to posttest. The overall posttest result was correlated with those of objective, fill-in-the-map, map construction and composition writing. Significant correlations were observed. Results accentuated that concept maps can be developed and evaluated to facilitate learning and assessment.
This document provides an overview of 8 lectures on human-computer interaction and interaction design. Lecture 1 introduces the scope and challenges of the field. Lecture 2 discusses visual representation, including text, maps, graphs, and schematic drawings. Lecture 3 covers text and gesture interaction as well as the evolution of interaction hardware. The remaining lectures cover topics like inference-based approaches, augmented reality, usability of programming languages, user-centered design research methods, and usability evaluation methods.
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1. Writing Research Across Borders III
Concept mapping and
text analysis
-a composite tool
for EAP writers and mentors
Lawrie Hunter
Kochi University of Technology
2. Problem: argument block
Engineering EAP PhD students have
trouble articulating their arguments.
Factors
1.The structure of an engineering RP is
not the structure of an argument.
2. Continuous text does not reveal
rhetorical structure.
3. Teaching argument leads poorly to
abstraction skill.
3. Intervention 2:
Use Cmap Tools
-force articulation of
relations between
core content elements
(The link relations are only
communication moves verbs
from argument discourse.)
4. Study 1: interventions 1 and 2
Map
source
text
Critique
the map
Make a
consensus
map
Write a
summary
from map
Results
Lack of rhetorical awareness led to the
inclusion of background information in
summaries.
Absence of visual metaphors led to
narrative order confusion.
6. Intervention 4:
Institute visual metaphors
abstract
overarching
more important
less important
concrete
subordinate
more salient
rhetorical
flow
argument
direction
less salient
passage through time
cause-effect
7. Study 2:
Instruction
in text
analysis
interventions 3 and 4
Map
source
text
Write a
summary
from map
Results
Core content dominant in summaries;
sentence order much better.
Some learners extended the process to a
second text analysis for further abstraction.
8. Reflection
Teachers of writing:
-built-in bias against
low-text work?
Outreach: just ask me!
Whatever
Lawrie Hunter
you need.
I love to help.
Kochi University of Technology
http://www.core.kochi-tech.ac.jp/hunter
http://lawriehunter.com
9. Concept mapping and text analysis: A composite tool for EAP writers and mentors
This presentation demonstrates how a low text representation of the content of text can reveal
rhetorical structure or orchestration (or their absence). It also argues that Cmap representation can
have a valuable place in the writing center toolkit.
Cmap representation has gained a wide usership, particularly in science education, thanks to the
popularity of the freeware Cmap Tools. Cmap Tools forces the user to label each link between two
nodes with a phrase specifying the relation between those nodes. As well, applying several visual
metaphors (e.g., up is abstract, down is concrete; up is overarching, down is subordinate) can make
the representation even more compressed. This presents an altogether more powerful representation
than mind maps.
This presentation reports several cases of fruitful application of Cmap Tools, wherein EAP learners of
academic writing discovered intellectual leverage in mapping. In each case the learners drew
constrained maps of the content of a text (academic or popular genre), critiqued their maps, arrived at
consensus on an accurate mapping, and then set out to write a new version of the text based only on
the content of the map.
In each case the subsequent work was rich regarding writing strategy and proactive use of tools. The
learners developed their own approaches, cycling between moves analysis and concept mapping as
they worked to unpack text that they had initially identified as 'good models'. In each case too the end
product was significantly closer to the target form.
The observations made here point to numerous applications in EAP writing, and suggest that the Cmap
deserves a place amongst the essential tools for EAP instruction and writing center work, being of
particular use in the analysis of source texts where the identification of rhetorical orchestration is
difficult; where argument is often masked by other rhetorical devices; and in situations where one's
thinking about an approach to a problem is complex and difficult to encode directly in extended text.
10. Concept mapping and text analysis: A composite tool for EAP writers and mentors
This presentation demonstrates how a low text representation of the content of text can reveal
rhetorical structure or orchestration (or their absence). It also argues that Cmap representation can
have a valuable place in the writing center toolkit.
Cmap representation has gained a wide usership, particularly in science education, thanks to the
popularity of the freeware Cmap Tools. Cmap Tools forces the user to label each link between two
nodes with a phrase specifying the relation between those nodes. As well, applying several visual
metaphors (e.g., up is abstract, down is concrete; up is overarching, down is subordinate) can make
the representation even more compressed. This presents an altogether more powerful representation
than mind maps.
This presentation reports several cases of fruitful application of Cmap Tools, wherein EAP learners of
academic writing discovered intellectual leverage in mapping. In each case the learners drew
constrained maps of the content of a text (academic or popular genre), critiqued their maps, arrived at
consensus on an accurate mapping, and then set out to write a new version of the text based only on
the content of the map.
In each case the subsequent work was rich regarding writing strategy and proactive use of tools. The
learners developed their own approaches, cycling between moves analysis and concept mapping as
they worked to unpack text that they had initially identified as 'good models'. In each case too the end
product was significantly closer to the target form.
The observations made here point to numerous applications in EAP writing, and suggest that the Cmap
deserves a place amongst the essential tools for EAP instruction and writing center work, being of
particular use in the analysis of source texts where the identification of rhetorical orchestration is
difficult; where argument is often masked by other rhetorical devices; and in situations where one's
thinking about an approach to a problem is complex and difficult to encode directly in extended text.