Ppt on computer assisted language learning

Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess CPE/CSC 486: Human-Computer Interaction.

Mustillo] Electronic Performance Support ❖ set of seamless and intuitive support mechanisms  generate performance and learning through guidance, advice, and consistent access to information on demand ❖ examples of different types /managing electronic mail  novice users with moderate computer exposure 89 © Franz J. Kurfess Important Concepts and Terms ❖ assistant ❖ advisor ❖ coach ❖ documentation ❖ help ❖ hints ❖ minimal manual approach ❖ natural language assistance ❖ task-based interface ❖ reference card ❖/


Assistive Technology Tools & software Assessment Cindy Nankee CESA #3 WATI Consultant

to control/operate TV, VCR, DVD, CD player, etc. Software Completion of art activities Games on computer Other computer software Activities of Daily Living (ADLS) Non slip materials to hold things in place Universal cuff/strap /STAGES Stage One: Cause and Effect Stage Two: Language Readiness Stage Three: Emerging language Stage Four: Early Concepts Stage Five: Advanced Concepts Stage Six: Functional Learning Stage Seven: Written Expression Assistive Technology, Inc. Software Demo –Assessment software/STAGES /


Strategies for Teaching Students with Learning and Behavior Problems, 8e Vaughn and Bos ISBN: 0137034695 © 2012, 2009, 2006 Pearson Education, Inc. All.

principles of mathematics to mastery Establish realistic goals Monitor progress Provide evidence Utilize computer-assisted instruction Strategies for Teaching Students with Learning and Behavior Problems, 8e, Vaughn and Bos ISBN # 0137034695 © 2012,/Inc. All rights reserved. 11- 41 Math Concepts and Computation: Language of Math Computation Many students with learning and behavior problems have difficulty with the language of computation. However, understanding the vocabulary is important for success in the/


Yogesh Kumar Meena, Assistant Professor,CSE & IT Dept., HITM Agra 1 Data Mining: Concepts and Techniques — Unit 1 — — Introduction — Code :ECS-075 Title:

R. E. 1997. A decision- theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139. 9/7/2015 Yogesh Kumar Meena, Assistant Professor,CSE & IT Dept., HITM Agra 18 The 18 Identified /mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels/


Creating Successful Communication Opportunities! Division of Accountability Office of Exceptional Children Assistive Technology Services Special Thanks.

S, ATACP Assistive Technology Specialist Why Use AAC? Provides a purpose and intent for learning - Through play and active participation Self-concept, self-esteem, self-competence – improves ability to learn Means of expressive & improving receptive language Provides a means/.http://www.cast.org Closing the Gap http://www.closingthegap.com This web site spotlights resources in computer technology, special education and rehabilitation. The Resource Directory is a database of over 2000 hardware and software/


Computer Assisted Language Learning WEEK ONE Definition & History of CALL.

a certain level of technology and certain pedagogical theories. Behaviorist CALL In the 1960s and 1970s the first form of computer-assisted Language Learning featured repetitive language drills, the so-called drill-and- practice method. It was based on the behaviorist learning model and as such the computer was viewed as little more than a mechanical tutor that never grew tired. Behaviorist CALL was first designed and/


 2004, G.Tecuci, Learning Agents Center CS 7850 Fall 2004 Learning Agents Center and Computer Science Department George Mason University Gheorghe Tecuci.

even for simpler agents. Knowledge Base Problem Solving Engine Autonomous Language Understanding and Learning Agent Knowledge Data Learning Engine Text Text Understanding Engine Results  2004, G.Tecuci, Learning Agents Center Knowledge Acquisition for agent development Disciple approach to agent/ experience. Making this vision a reality would allow a normal computer user, who is not a trained knowledge engineer, to build by himself an intelligent assistant as easily as he now uses a word processor to write/


N97C0004 Betty Exploration of The Attitudes of Freshman Foreign Language Students Toward Using Computers A Turkish State University.

(3) Integrative CALL  Lee (2000): eight categories of contribution of net-work-based technology (1) experiential learning (1) experiential learning (2) motivation (2) motivation (3) amelioration of students achievement (3) amelioration of students achievement (4) supply/ the current scale generated positive responses toward using computers in instruction. (5) The result of the current study support the assumptions of Lee (2000)—computer assisted language instruction might lead to more positive attitudes. */


Frontiers in Research and Education in Computing: A View from the National Science Foundation Jeannette M. Wing Assistant Director Computer and Information.

languages, algorithms, metrics E.g., Science of Security –Privacy –Usability too New for FY10 43DACJeannette M. Wing Clickworkers Collaborative Filtering Collaborative Intelligence Collective Intelligence Computer Assisted Proof Crowdsourcing eSociety Genius in the Crowd Human-Based Computation/Education Implications for K-12 What is an effective way of learning (teaching) computational thinking by (to) K-12? - What concepts can students (educators) best learn (teach) when? What is our analogy to numbers in /


UNIVERSAL DEISGN UNIVERSAL DESIGN FOR LEARNING 1.

and Intuitive Use – design is easy to understand, regardless of user’s experience, knowledge, language skills, or current concentration level. Especially reflected in elements #5 (simple, clear, intuitive instructions/Computer simulations, virtual reality can also provide an engaging way to scaffold instruction. Computer simulations, virtual reality can also provide an engaging way to scaffold instruction. 40 References Assistive Technology, Universal Design,Universal Design for Learning: Improved Learning/


Prepared by : Genesis Z. Tayanes Ed-Eng 106 Technology in Language Education.

for Economic Cooperation and Development has identified four levels of courses with only components, namely  web-supplemented  web-dependent,  mixed mod and  fully online" Dr. Dilip Barad, Slide-Share CALL Computer Assisted Language Learning. Dept. of English, Bhavnagar University Bhavnagar. Gujarat – INDIA. Retrieved from www.dilipbarad.com last February 11, 2014. http://en.wikipedia.org/wiki/Computerassisted_ language_learning Dr. Dilip Barad, Slide-Share CALL/


ED 635 SPED – Graduate Introduction to Assistive Technology.

for Physical Disabilities bAssistive Technology for Learning Disabilities bAssistive Technology for the Vision impaired bAssistive Technology for Computer Access Types of Assistive Technology bPositioning bMobility bAugmentative and Alternate / compounding) in language. Assistive Technology Devices to Enhance Speech Communication Language Components Phonology The system of speech sounds of a language. Assistive Technology Devices to Enhance Speech Communication Language Components Pragmatics Linguistics/


English and Digital Literacies Unit 2.5: Summary of the 3 CALL traditions Bessie Mitsikopoulou School of Philosophy Faculty of English Language and Literature.

). SYSTEM: An International Journal of Educational Technology and Applied Linguistics. SYSTEM: An International Journal of Educational Technology and Applied Linguistics. Computer Assisted Language Learning. Computer Assisted Language Learning Computer Assisted Language Learning – Electronic Journal. Computer Assisted Language Learning – Electronic Journal CALICO Computers and Composition. Computers and Composition Journal of Technology and Teacher Education. Journal of Technology and Teacher Education E/


Measuring the Power of Learning.™ California Assessment of Student Performance and Progress (CAASPP) CAASPP Workshops January 28, 2016- General Overview.

learning, monitor progress, and adjust instruction throughout the year. Measuring the Power of Learning.™ 2015–16 CAASPP Pretest Administration Workshop 10 Student Participation: General The Smarter Balanced Summative Assessments consist of the following: –English Language Arts/Literacy (ELA) and mathematics tests –Includes computer/. Provide student login information on cards (i.e., login tickets) to assist test administrators with student login. –New: Login tickets can include any information as long/


Development of Web-based Groupware for Assisting Language Teaching Yuichiro Yoshinari Tokyo Denki University.

(for general purposes) School LAN and students ’ PCs Internet-connected home PCs Computer-Assisted Language Learning Environments Today CALL (Computer-Assisted Language Learning [Laboratory] Computer-Assisted Language Learning Environments Today Computer-equipped Classrooms (for general purposes) Computer-Assisted Language Learning Environments Today School LAN and students ’ PCs Computer-Assisted Language Learning Environments Today Internet-connected home PCs Web-based Training (WBT) WebCT TopClass/


WELCOME TO GROUP 3 1. ICT PRESENTATION ComputerAssisted Language Learning (CALL) Presented By: Syahrial Nahri Tiara Noviarini Ratnaningsih.

GROUP 3 1 ICT PRESENTATION ComputerAssisted Language Learning (CALL) Presented By: Syahrial Nahri Tiara Noviarini Ratnaningsih MAIN TOPIC: ComputerAssisted Language Learning (CALL) 1.CAL and CALL: What are they? 2.Advantages and Disadvantages of CALL 3.A typology of CALL Programs and Application 4.Web Based Learning 5.Example of Web Based Learning 6.Three main ways in which the Web can assist with teaching language 7.Advantages and Disadvantages/


L A N G L E Y R E S E A R C H C E N T E R Big Data Analytics and Machine Learning in Aerospace Manjula Ambur, Lin Chen, Charles Liles, Robert Milletich,

and Machine Learning Background Vision of Virtual Experts/Assistants Data Intensive Scientific Discovery Knowledge Analytics/Cognitive Computing Projects towards Virtual Assistants Aerospace Data Assistants and Algorithms Aerospace Knowledge Assistants and Software/ StateChannelized Attn Diverted AttnStartle /Surprise Machine Learning Languages and Libraries Neural Networks and Deep Learning Regression Ensemble Learning Time Series Motifs PythonTheano/Keras- Scikit-Learn, XGBoost - MATLAB- MATLAB Functions -/


Assistive Technology Roundtable Raising Awareness October 31, 2013 Beaver Valley Intermediate Unit.

with others? Do the student’s communication skills impact upon learning? Does the student require assistive devices to assist in the development and use of meaningful language used in direct instruction? What other considerations (e.g., / Alternate keyboard Pointing options / head mouse Scanning with switch Voice recognition software No/Low Tech High Tech Mid Tech Computer Access AT for Math Abacus / math line Alternatives for answering, explaining, giving examples Money calculator / coinulator Tactile /


Staff Development Resources is a Student with LEARNING ABILITIES who can SUCCEED at ACADEMIC STUDY A Student with a LEARNING DISABILITY.

language (listening, speaking and understanding) reading (decoding and comprehension) written language (spelling and written expression) mathematics (computation and problem solving) Learning disabilities may also cause difficulties with organisational skills, social perception and social interaction. Staff Development Resources Definition of Learning/). 2.Arranging access to lecture notes. 3.Discussing course workload and assist decision making about undertaking full time or part time study. 4.Negotiating/


Computational Science

Vision for Computational Science Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/~langley A Day in the Life of a Future Scientist Professor Jones comes into her office on Tuesday morning. Her first action is to check the status of an experiment she submitted the night before. Her computerized assistant reports the/


Non-Functional Requirements

to bring order into chaos, but ... Classification 1 [Roman, IEEE Computer 1985] 6 classes + 2-3 levels NFRs: subjective in both definitions/ [Molich and Nielsen90] Simple and natural dialogue; Speak the user’s language Minimize the user’s memory; Consistency; Feedback Clearly makred exits; Shortcuts /without requiring assistance (e.g., modifying exclusion date set) 95% / figure out how to use it? Easy-to-learn, Easy-to-learn(f), Easy-to-learn(f2), Easy-to-learn(x) How robust is the input? Robust, /


SCHOOLS K - 12 Dr. Susan W. Floyd Education Associate Speech-Language Disabilities, Assistive Technology Office of Exceptional Children South Carolina.

in the child’s language and communication mode;  Consider whether the child needs assistive technology devices and services. What is assistive technology and how is it used in schools?  Assistive Technology Service  Assistive Technology Device  any /. Examples  Students with learning disabilities may not be able to decode words in printed text.  Computer-based instruction can support learning: talking software, word prediction, positive feedback If your child has assistive technology needs…  Contact /


LMIIT ‘S TECHNOLOGICA COMPUTER EDUCATION

interface belonged to Microsofts own Multiplan spreadsheet). As most secretaries had learned how to use WordPerfect, companies were reluctant to switch to a /citation needed] The Hello world program, a common computer program employed for comparing programming languages, scripting languages, and markup languages is made of 9 lines of code in /ODBC client-server database. An additional solution, the SQL Server Migration Assistant for Access(SSMA), is also available for download from MICROSOFT ACCESS STEPS/


G-TEN Management and Administration

speaking the Koorie language impact on the learning of Koorie students? Presenter Marlene Atkinson, from the Moiduban tribe of the Bangerang people, is a qualified secondary teacher who has worked with schools and families to assist to develop / become familiar with using their new Macbook Notebook. Topics covered will include managing and using the OSX computer operating system and included programs, installing and deleting applications, managing network preferences and other options. Participants will/


Basic Development of the Individualized Education Program Annie Margaret Harris Office of Special Education Division of Technical Assistance 2011 - 2012.

in a one-to-one situation. She does not understand the relationship of the language in the problems and the computation required. She needs to learn to set-up and solve story problems. Although the results of the MCT2 / for general education personnel  Collaborate with special education personnel and/or related service providers  Co-teaching  Use an assistant in the general education setting 71 2011 - 2012 Mississippi Department of Education Office of Instructional Enhancement and Internal Programs Office/


SUNY Distance Mentored Undergraduate Research: Leveraging System Expertise to Enhance Learning Lori Bernard, Geneseo Jack Croxton, Director of OSCAR (Office.

Kathleen Gradel, Language, Learning and Leadership Department, SUNY Fredonia Sharon Raimondi, The Exceptional Education Department at UB/Buffalo State Chris Widdall, Childhood/Early Childhood Education Department, SUNY Cortland Karl Klein, Computer Studies Department,/ Professor, SUNYIT Jorge Novillo, Professor, SUNYIT Christopher Urban, Lecturer, SUNYIT Nick Merante, Instructional Support Assistant, SUNYIT Piloted Stanford developed ClassX, an open-source system that enables viewer selection any portion of /


Contents Introduction Teaching Philosophy Foreign language Standards Why we should study another language? Language study in the United States Final course.

What the learner can do with assistance today, they will be able to do on their own tomorrow. Motivational factor play an important but complex role in language learning and performance in a language classroom. Interactive environment that model /; and c) Technology is used to enhance the learning experience through information dissemination, communication, collaboration, and knowledge construction. The education theorist, Seymour Papert tells us that the computer is a tool. Papert says that schools can be/


© 1999 Franz Kurfess User Assistance 1 Course Overview  Introduction  Understanding Users and Their Tasks  Principles and Guidelines  Interacting with.

almost immediate competent performance, even by novice users  recommended for tasks that are very difficult to learn, complex to do, or critical  drawbacks  draw attention from the work itself  can be/and managing electronic mail  novice users with moderate computer exposure © 1999 Franz Kurfess User Assistance 77 Important Concepts and Terms  assistant  advisor  coach  documentation  help  hints  minimal manual approach  natural language assistance  task-based interface  reference card  /


Jon Sayles, IBM Software Group, Rational EcoSystems Team

assisting with this course: David Myers/IBM, Ka Yin Lam/IBM John Fenyar Wilbert Kho/IBM Steven Wilcenski/Sabre Systems Inc. Mike Wrzinski/Sentry Insurance Course Description Course Name: COBOL Foundation Training - with RDz Course Description: Learn the COBOL language, RDz and learn/ SECTION – Optional section, used to specify the development and run: Source-Computer – the compiling computer. Object-Computer – the run-time computer INPUT-OUTPUT SECTION – Used to define external files used by the program /


國立高雄第一科技大學 應用英語系副教授 陳其芬 國立高雄第一科技大學 94 年度提昇南區大專校院「大學新進教師教學知能研習會」 電腦科技輔助語言教學之應用 Using Computer Technology to Enhance Language Teaching.

Flexibility New paradigms in education and language teaching Constructivist approach Communicative language teaching II. How are computers used in language teaching contexts? Four Contexts and Three Roles Locus of Control Early CALL vs. Modern CALL (*CALL: computer assisted language learning) Contexts & Roles for computer assisted language teaching Contexts One-computer classroom Computer Network room Self-access learning center Distance learning Roles of the Computer  Tool  Tutor  Medium Locus of/


Resource centres and self-study: issues in computer assisted language learning (CALL) The 4th Education in a Changing Environment Conference 12th-14th.

study but as little as 24 hours of this time is face-to-face An emerging research tradition within CALL The field of Computer assisted language learning (CALL) “… learners learning language in any context with, through, and around computer technologies.” Egbert (2005:4). CBMs are “language specific as well as more generic Information Technology (IT) programmes”. Jarvis (2004:116) Traditionally CBMs can be divided according two functions (Taylor/


Introducing ESP courseware into the classroom Shu-Chiao Tsai 蔡叔翹 Dept. of Applied Foreign Languages National Kaohsiung University of Applied Sciences 高雄應用科技大學應用外語系.

with English texts and their English audio 3. Providing opportunities for comprehensible output → on-line evaluation system of the courseware provides learners with various language tests to practice English skills Chapelle’s suggestions for multimedia computer-assisted language learning 15 4. Providing opportunities for learners to notice their errors 5. Providing opportunities for learners to correct their linguistic output → instant self-checking function of the/


ALIGNING THE COMPUTER ENGINEERING DEPARTMENT WITH ABET EC 2K

, and test assembly language programs can be described/Learning Environment – Improvement Areas The following needs improvement (60% - 70%): Quality of instruction in Mathematics, Physics, and Chemistry Quality of Laboratories: Instruction provided by lab instructors, Experiments and lab manuals, Computing facilities and equipments Quality of supervision or advice: Summer training or COOP, Academic planning, Career planning (All students agree on this issue). Equity of treatment by: Teaching assistants/


INNOVATIVE TECHNOLOGY- BASED PEDAGOGIES FOR THE FOREIGN LANGUAGE CLASSROOM Center for Educational Resources in Culture, Language and Literacy (CERCLL)

-Seghayer, K. (2001). The effect of multimedia annotation modes on L2 vocabulary acquisition. Language Learning & Technology, 5 (1), 202-232. Ariew, R. & Ercetin, G. (2004) Exploring the potential of hypermedia annotations for second language reading. Computer Assisted Language Learning, 17 (2), 237-259. Coll, J. F. (2002). Richness of semantic encoding in a hypermedia-assisted instructional environment for ESP: effects on incidental vocabulary retention among learners with low/


12.0 Computer-Assisted Language Learning (CALL) References: 1.“An Overview of Spoken Language Technology for Education”, Speech Communications, 51, pp.832-844,

Detection and Unsupervised Discovery of Pronunciation Error Patterns for Computer-Assisted Language Learning”, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 23, No. 3, Mar 2015, pp. 564-579. Computer-Assisted Language Learning (CALL) Globalized World –every one needs to learn one or more languages in addition to the native language Language Learning –one-to-one tutoring most effective but with high cost Computers not as good as Human Tutors –software reproduced easily/


LRC 530 Technology in English Language Learning/Foreign Language Instruction José Álvarez Valencia, Travis Hawkley, Sonja Fordham, and Merica McNeil.

é Álvarez Valencia, Travis Hawkley, Sonja Fordham, and Merica McNeil Aims Review major perspectives of CALL (Computer Assisted Language Learning) as a way to locate current computer mediated applications to language teaching and learning. Show examples of two online communities for learning languages and their implications in terms of the language, learning, the roles of the computer, the teacher and the students. Before starting What differences do you notice in the following websites/


C-DAC,Kolkata 1 C-DAC All Rights Reserved ACTIVITIES of Centre for Development of Advanced Computing, (C-DAC), Kolkata

mode. Application Areas: On-line form processing. Text entry for S.M.S through Mobile, Note taking purposes, E-Learning s/w, Language Learning Tool, Forensic Document authentication / Verification Bangla On-line Handwriting Recognition System www.cdac.in C-DAC,Kolkata 33 C-DAC /EXPLORATORY IDEAS www.cdac.in C-DAC,Kolkata 53 C-DAC All Rights Reserved Creation of an Assisted Living Environment for Elderly People Using Ubiquitous Computing www.cdac.in C-DAC,Kolkata 54 C-DAC All Rights Reserved www.cdac.in C-DAC/


Worcester Public Schools Assistive Technology Specialist Catherine Salerno Karen Hernandez Kathleen Marple.

– portable battery-powered word processor with option of data transfer to desktop computer; simple, easy-to-use word processor; 4/ lines of text on/ Paul Visvader MA CCC-SLP © 2013 Assistive Technology Team, Boulder Valley School District, Boulder, CO Communication and Language Communication and language skills allow a person to…  Initiate/preview content/sequence of story/text (library, audiotape purchase or rental stores, Learning Ally, BookShare  hand-held talking dictionary/speller – e.g., Franklin /


Department of Computer and Information Science Ph.D. Graduate Program.

Assistants  Sports ❍ Division 1 football, basketball, baseball, track, volleyball ❍ “Tracktown, USA” (Historic Hayward Field) Ph.D. Graduate Student Recruiting, Beihang University, December 10, 20136 7 Department of Computer and Information Science (CIS) CIS Department Information  Tenure-track Faculty: 18  Graduate Students: 67 ❍ 29 Master’s students ❍ 38 Ph.D. students  CIS Lecture Series  Programming language/Jun Zhu, and Yang Xiang. “On Learning Cluster Coefficient from Private Networks.” In /


FAST BREAK: Career and College Readiness Through Accelerated Learning by Dr. Barry Stern (540) 751-0601

More ethnically diverse  More from single parent households, more half-siblings  More speak English as 2 nd language  More low-income qualifying for school lunch program  More experiencing/witnessing abuse (drug, physical, mental,/programs – Roger Penske + Gov. What is “Fast Break”?  Computer-assisted, intensive, accelerated learning / work preparation model that emphasizes reading, math, basic computer applications, employability and interpersonal skills. Fast Break provides immersion-type curriculum /


Universal Design Learning and Technology in the Elementary Classroom By Theresa Moore 735 Technology for Diverse Population Dr. Wissick – Summer I.

, language, and cognitive skills. Computers properly used with administrative support teacher training, correct software, right environment, attentive to time, needs, abilities, and developmentally appropriate activities, technology will have a positive impact on young children’s learning. References Attewell, P., Belkis, S. & Battle, J. (2003 Fall). Computers and young children: Social Benefit or Social Problem. Social Forces, 82 (1), 277-296. Behrman, M. (1998). Assistive technology/


CALL Computer Assisted Language Learning University of Stellenbosch.

and applications that have evolved over the years. Terminology The term Computer assisted language learning refers to educational measures taken to enhance language instruction which is not conducted by computer. Terminology The term Computer assisted language learning appears to imply that stand-alone language software [true self-study applications] are impossible, unlikely or undesirable. Terminology If language instruction by computer is only possible or desirable as an enhancement of other educational/


ELL English Language Learner Program By: Martha Sosa.

’t know to use throughout the day in various writing contexts. LA25. Use electronic spell checkers. ORAL LANGUAGE LA26. Drama LA27. Total Physical Response LA28. Language Preparation LA29. Storytelling ELL Strategies: Language Arts LA30. Cooperative learning LA31. Song/Music LA32. Peer tutor LA33. Adult tutor LA34. Realia LA35. Computer Assisted Instruction LA36. Audio Visuals, Tapes, Laser Disks LA37. Multisensory experiences. LA38. Bilingual dictionaries accessible to students/


Addressing the Needs of Students with Learning Disabilities in Online Instruction Everybody has learning differences. Its just that some differences obstruct.

language (e.g. listening, speaking, understanding); –reading (e.g. decoding, phonetic knowledge, word recognition, comprehension); –written language (e.g. spelling and written expression); –mathematics (e.g. computation, problem solving); –organizational skills; –social perception; –social interaction. Learning difficulties of students with learning disabilities range in severity. Learning/-modeling/demonstration and allow practice. Encourage using assistive technology. Set flexible time limits on performing/


Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning W. Hui,* P.J.-H.

Utah, Salt Lake City, Utah, USA ‡Information and Systems Management, School of Business and Management, Hong Kong University of Science and Technology, ClearWater Bay, Hong Kong, China §Language Center, School of Humanities, Hong Kong University of Science and Technology, ClearWater Bay, Hong Kong, China Journal of Computer Assisted Learning, Vol. 24, 245-259, 2007 Introduction Some researchers, including Zhang et al. (2004), suggest technology/


—————————————————————————————————————————— Design of Interactive Computational Media Jan.-Apr. 2003 ©1992-2003, Ronald M. Baecker Slide 12.1 The Design.

control of user User need not learn new language to deal with agent —————————————————————————————————————————— Design of Interactive Computational Media Jan.-Apr. 2003 ©1992-2003, Ronald M. Baecker Slide 12.28 Microsoft Intelligent Agents In early days, called “social interfaces” 1994-1997: Microsoft Bob –Anthropomorphic help, e.g., Rover the Dog –Usually viewed as silly and simplistic 1997-2002: Office Assistant aka “Clippy” –Again, represented by/


EUROCALL 2002: Jyväskylä 1. 2 Creating, Developing and Sustaining a Computer-Based Language Learning Environment John Gillespie & David Barr School of.

from CHASS techniciansSupport from CHASS technicians Students paid to digitise/assist in preparation of coursewareStudents paid to digitise/assist in preparation of courseware Robarts Library provides suite of web / 2002: Jyväskylä 24 VAT: Value Added Teaching Learning Environments are complexLearning Environments are complex Computer-Based Language-Learning Environments are particularly complexComputer-Based Language-Learning Environments are particularly complex The rewards and potential far outweigh/


Statistical Methods for Mining Big Text Data ChengXiang Zhai Department of Computer Science Graduate School of Library & Information Science Institute.

) –Assist information organization (e.g., discover hidden structures to link scattered information) 7 Text Mining Methods Data Mining Style: View text as high dimensional data –Frequent pattern finding –Association analysis –Outlier detection Information Retrieval Style: Fine granularity topical analysis –Topic extraction –Exploit term weighting and text similarity measures Natural Language Processing Style: Information Extraction –Entity extraction –Relation extraction –Sentiment analysis Machine Learning/


Accessibility in Education WORKSHOP. Top 3 learning objectives 1.Every classroom has a student who can benefit from accessibility 2.Accessibility features.

Apps Find out more: tutorials Impairments & Technology Solutions Types of impairments/disabilities Vision Learning Mobility and dexterity Hearing and deafness Language and speech Vision impairments Includes Low vision Colorblindness Blindness Accessibility features in Windows and/Computer sounds Text and visual alternatives for sounds – such as captions Assistive technology Personal listening device Headphones Sign language translator Four ways to make a PC easier for students to hear How-to article Language/


1 Use of CALL in Language Acquisition: Values, Beliefs and Practices of EFL Teachers using CALL in Sharjah – Dubai, UAE Jack Ross Doctor of Education in.

research methodology in which a set of specific guiding questions was developed and posed To selected language teachers Computer Assisted Language Learning 7 C A L L Themes Emerging from the Interview Data 1. A good teacher/ by teachers 5. Questions foster other questions Computer Assisted Language Learning 8 C A L L Computer Assisted Language Learning Findings Distinction between pre- and post- Internet CALL (Mayes 1995; Oblinger & Oblinger 2005) Language teachers are able to - undertake meaningful self/


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