Contentscontents MAIN FEATURE BUSINESS ENGLISH PROFESSIONAL

Total Page:16

File Type:pdf, Size:1020Kb

Contentscontents MAIN FEATURE BUSINESS ENGLISH PROFESSIONAL www.diako.ir www.diako.ir ContentsContents MAIN FEATURE BUSINESS ENGLISH PROFESSIONAL IS READING ALOUD ALLOWED? 4 NOT ‘THEM’ BUT ‘US’ 29 Jeremy Harmer rehabilitates round-the-class reading Monica Hoogstad uses humour to break down cultural barriers FEATURES TEACHER DEVELOPMENT CLASS-CENTRED TEACHING 8 Rose Senior finds group dynamics are key to establishing ALONE TOGETHER 51 a good classroom climate Deniz Kurtoglu Eken feels personal development is a fundamental factor in professional development WALKING STICKS 12 Ji Lingzhu arms her students with supportive listening strategies TECHNOLOGY ACTIVITIES UNDER ANALYSIS 16 CHOOSING ONLINE MATERIALS 56 Myrian Casamassima explains why we need to make Rafael Sabio puts forward some suggestions for a close assessment of the tasks we assign selecting texts and videos REVISING NEW WORDS 17 FIVE THINGS YOU ALWAYS WANTED 60 Angela Noble demonstrates that a vocabulary column TO KNOW ABOUT: MOODLE on the board has many benefits Nicky Hockly looks at a virtual learning environment LANGUAGE LEARNING IS LIKE ... 18 WEBWATCHER 61 Dede Wilson’s wall posters provide insights for students Russell Stannard has all his questions answered and teachers alike PHRASAL VERBS? THEY’RE EASY! 4 21 REGULAR FEATURES John Ryan finishes up his look at up ACTIVITY CORNER: 37 OVER THE WALL 27 PHONICS FUN Jon Marks Alan Maley recommends books that inspire creative writing ELUSIVE ESSAY WRITING SKILLS 34 PREPARING TO TEACH ... 40 Cheryl Morris finds innovative ways to teach academic Colourful language 2 John Potts writing A VOYAGE OF ADVENTURE 35 LET’S GET ENGAGED 63 Rose Senior James Porcaro sets goals and objectives for students and teachers IT WORKS IN PRACTICE 42 WHAT DO FOREIGNERS NEED TO SAY? 46 Peter Wells believes in teaching language that students REVIEWS 44 actually need to use SCRAPBOOK 54 LITERACY IN TWO LANGUAGES 49 Lois Spitzer sees success in skills transference between COMPETITIONS 41, 64 L1 and L2 TEACHING YOUNG LEARNERS INTERNATIONAL SUBSCRIPTION FORM 32 READY TO READ 23 Includes materials designed to photocopy Ana Lado examines criteria for choosing books for children • www.etprofessional.com • ENGLISH TEACHING professional • Issue 65 November 2009 • 1 www.diako.ir www.diako.ir www.diako.ir MAIN FEATURE Despite the fact that all of the readers spoke impeccable English, some of them stumbled over the words. One couldn’t pronounce a name, one got mixed up with concatenation and another found the last sentence of the extract almost impossible to read intelligibly, at sight. Afterwards, we discussed what they IsIs readingreading felt like, and it wasn’t good! Amongst other things, they were nervous, they didn’t understand what they were reading or why, and they hated the experience of not being able to pronounce things correctly in front of their peers, or of fighting to make sense aloud of long, complicated sentences. aloud And yet all they were doing was what has been happening in language classrooms all over the world for ages I have never really allowed?allowed? worried about reading Jeremy Harmer t a recent teacher-training aloud before, but for workshop in Bucharest, I recommends reading, handed out a text (see the various reasons it has box below) and asked the suddenly become more Ateachers to read it out, one by one, repeating and rehearsing. sentence by sentence. I wanted this first interesting for me activity to start a discussion of what it felt like to read aloud. and ages – though, of course, I had History, Karen Bailey used to tell specially chosen a text that would challenge even the most competent her students before the whistle at English speakers. The question that Siete Vientos changed everything, arises, therefore, is whether it has always is the random concatenation of been that bad for students, even with states and events, nothing more. less challenging texts. And if it has, does The job of the historian is to check it have to be? that each of these happenings, I have never really worried about each of the realities under reading aloud before, beyond feeling investigation, is as unambiguous, faintly negative about it, but for various as verifiable as possible, so that reasons it has suddenly become more when describing the past, one interesting for me. In the first place, I could have confidence that one have recently observed it taking place was telling truths, not weaving when watching lessons – something fantasies. This was the kind of way which I haven’t seen for some time, she talked, and she was thought of despite many years of observation. as very academic, very precise. But Secondly, its value – or lack of it – became a point of discussion in a the stories of Siete Vientos and writing project where I am one member what happened there banished that of a team. And finally, in the last few style from her repertoire completely months I have read three articles on this because it suddenly seemed to her topic, which is all the more remarkable that history, people’s histories, the since for many years hardly anyone history of a place breathed in the talked about it at all. air and sticking to the rocks, is Sally Gibson, for example, explains more than dusty accretions of the reasons why people have been sources and references.1 against reading aloud, but argues for its many virtues. Costas Gabrielatos says 4 • Issue 65 November 2009 • ENGLISH TEACHING professional • www.etprofessional.com • www.diako.ir www.diako.ir www.diako.ir www.diako.ir IN THE CLASSROOM Class-centredClass-centred teachingteaching Rose Senior ponders the principles of group development. ccepting that classes function Teachers can readily discern – often in class-centred ways with the myriad as groups is central to all through a spontaneous collective occurrences that are part of everyday effective teaching. Whether response by the class to an unforeseen classroom life. we like it or not, powerful occurrence – when the tipping point has The box on page 9 contains a Agroup forces are at work in all been reached. From this point on, their selection of concepts from general classrooms. Classes of learners are not confidence grows and they feel more at research into group dynamics upon simply collections of individuals who ease with their class. which the ten principles of class group happen to be studying the same learning If teachers do not behave in class- development which I will describe in materials in the same room under the centred ways, their classes can quickly this article are based. direction of a teacher: they are groups tip in the opposite direction, with their of students whose individual or students either becoming a fragmented Group development collective behaviour both influences and learning community or, worse still, principles is influenced by the individual or uniting against their common enemy: collective behaviour of others in the the teacher. The following principles were developed room. Teachers, too, participate in class Class-centred teachers have a higher by examining a wide range of social group processes – with their teaching proportion of classes that function in a processes occurring during intensive and class management practices closely cohesive manner than do other teachers. English language classes containing related to the social evolution of their With their intuitive understanding of adult learners from a range of cultural class groups. group behaviour, class-centred teachers and linguistic backgrounds. Language teachers who keep in mind sense when to go with the flow and 1 that their classes function as groups allow social processes to occur naturally, Creating the climate have a class-centred focus and teach in and when to pull back and adopt their As with any new skill, learning to speak class-centred ways. Through their own more traditional teacher roles. a new language involves trial and error. behaviour and the ways that they relate Confidence, combined with consistency Nobody wants to appear foolish in to their students, class-centred teachers in personal behaviour and a willingness public and yet, especially in encourage their classes to evolve into to be flexible, are the hallmarks of communicative classrooms where learning communities in which the effective class-centred teachers. students are expected to interact with overall atmosphere of the class their peers in English, errors are influences the behaviour of individuals. Research principles inevitable. Students become easily upset The crucial moment when a critical when they mispronounce or misuse a mass of the students in a class starts to The relevance of group dynamics to word, or fail to understand something behave in ways that promote the education is well known. In their classic that somebody says – particularly when development of class cohesion has been book, Schmuck and Schmuck relate those around them are amused. defined by Malcolm Gladwell (quoted insights from research into how groups Class-centred teachers make an effort by Tollefson and Osborn) as the develop and function in classroom to create classroom climates in which it situations. Dörnyei and Murphey Phillip Burrows ‘tipping point’. is clear that making errors is a natural provide an invaluable introduction to part of the learning process and nothing The tipping point group dynamics for language teachers, to be ashamed of. Such teachers while Hadfield presents an extensive regularly model desirable behaviour, collection of classroom activities for including behaving confidently and encouraging classes to develop and openly when they themselves make maintain a positive group feeling. In my mistakes. By smilingly admitting their own work I describe how teachers deal error (and thanking the person who 8 • Issue 65 November 2009 • ENGLISH TEACHING professional • www.etprofessional.com • www.diako.ir whispering with friends or using Group dynamics concepts mother-tongue talk in ways that make others feel excluded.
Recommended publications
  • Download Slides
    a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source:
    [Show full text]
  • Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web
    Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web Editor(s): Aldo Gangemi, ISTC-CNR Rome, Italy Solicited review(s): Claudio Sacerdoti Coen, University of Bologna, Italy; Alexandre Passant, DERI, National University of Galway, Ireland; Aldo Gangemi, ISTC-CNR Rome, Italy Christoph Lange data vocabularies and domain knowledge from pure and ap- plied mathematics. FB 3 (Mathematics and Computer Science), Many fields of mathematics have not yet been imple- University of Bremen, Germany mented as proper Semantic Web ontologies; however, we Computer Science, Jacobs University Bremen, show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be Germany fully integrated with RDF representations in order to con- E-mail: [email protected] tribute existing mathematical knowledge to the Web of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to inter- link them, and how to take advantage of these new connec- tions. Abstract. Mathematics is a ubiquitous foundation of sci- Keywords: mathematics, mathematical knowledge manage- ence, technology, and engineering. Specific areas of mathe- ment, ontologies, knowledge representation, formalization, matics, such as numeric and symbolic computation or logics, linked data, XML enjoy considerable software support. Working mathemati- cians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine sup- 1. Introduction: Mathematics on the Web – State port for knowledge organization and reuse, and they are dis- of the Art and Challenges connected from tools such as computer algebra systems or interactive proof assistants.
    [Show full text]
  • Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3
    DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets.
    [Show full text]
  • A New Kind of Science
    Wolfram|Alpha, A New Kind of Science A New Kind of Science Wolfram|Alpha, A New Kind of Science by Bruce Walters April 18, 2011 Research Paper for Spring 2012 INFSY 556 Data Warehousing Professor Rhoda Joseph, Ph.D. Penn State University at Harrisburg Wolfram|Alpha, A New Kind of Science Page 2 of 8 Abstract The core mission of Wolfram|Alpha is “to take expert-level knowledge, and create a system that can apply it automatically whenever and wherever it’s needed” says Stephen Wolfram, the technologies inventor (Wolfram, 2009-02). This paper examines Wolfram|Alpha in its present form. Introduction As the internet became available to the world mass population, British computer scientist Tim Berners-Lee provided “hypertext” as a means for its general consumption, and coined the phrase World Wide Web. The World Wide Web is often referred to simply as the Web, and Web 1.0 transformed how we communicate. Now, with Web 2.0 firmly entrenched in our being and going with us wherever we go, can 3.0 be far behind? Web 3.0, the semantic web, is a web that endeavors to understand meaning rather than syntactically precise commands (Andersen, 2010). Enter Wolfram|Alpha. Wolfram Alpha, officially launched in May 2009, is a rapidly evolving "computational search engine,” but rather than searching pre‐existing documents, it actually computes the answer, every time (Andersen, 2010). Wolfram|Alpha relies on a knowledgebase of data in order to perform these computations, which despite efforts to date, is still only a fraction of world’s knowledge. Scientist, author, and inventor Stephen Wolfram refers to the world’s knowledge this way: “It’s a sad but true fact that most data that’s generated or collected, even with considerable effort, never gets any kind of serious analysis” (Wolfram, 2009-02).
    [Show full text]
  • RIO: an AI Based Virtual Assistant
    International Journal of Computer Applications (0975 – 8887) Volume 180 – No.45, May 2018 RIO: An AI based Virtual Assistant Samruddhi S. Sawant Abhinav A. Bapat Komal K. Sheth Department of Information Department of Information Department of Information Technology Technology Technology NBN Sinhgad Technical Institute NBN Sinhgad Technical Institute NBN Sinhgad Technical Institute Campus Campus Campus Pune, India Pune, India Pune, India Swapnadip B. Kumbhar Rahul M. Samant Department of Information Technology Professor NBN Sinhgad Technical Institute Campus Department of Information Technology Pune, India NBN Sinhgad Technical Institute Campus Pune, India ABSTRACT benefitted by such virtual assistants. The rise of messaging In this world of corporate companies, a lot of importance is apps, the explosion of the app ecosystem, advancements in being given to Human Resources. Human Capital artificial intelligence (AI) and cognitive technologies, a Management (HCM) is an approach of Human Resource fascination with conversational user interfaces and a wider Management that connotes to viewing of employees as assets reach of automation are all driving the chatbot trend. A that can be invested in and managed to maximize business chatbot can be deployed over various platforms namely value. In this paper, we build a chatbot to manage all the Facebook messenger, Slack, Skype, Kik, etc. The most functions of HRM namely -- core HR, Talent Management preferred platform among businesses seems to be Facebook and Workforce management. A chatbot is a service, powered messenger (92%). There are around 80% of businesses that by rules and sometimes artificial intelligence that you interact would like to host their chatbot on their own website.
    [Show full text]
  • Surviving the AI Hype – Fundamental Concepts to Understand Artificial Intelligence
    WHITEPAPEr_ Surviving the AI Hype – Fund amental concepts to understand Artificial Intelligence 23.12.2016 luca-d3.com Whitepaper_ Surviving the AI Hype – Fundamental concepts to understand Artificial Intelligence Index 1. Introduction.................................................................................................................................................................................... 3 2. What are the most common definitions of AI? ......................................................................................................................... 3 3. What are the sub areas of AI? ...................................................................................................................................................... 5 4. How “intelligent” can Artificial Intelligence get? ....................................................................................................................... 7 Strong and weak AI ............................................................................................................................................................... 7 The Turing Test ..................................................................................................................................................................... 7 The Chinese Room Argument ............................................................................................................................................. 8 The Intentional Stance ........................................................................................................................................................
    [Show full text]
  • Now That You Have Siri Set the Way You Want, Close Settings and Give Siri a Try
    Now that you have Siri set the way you want, close Settings and give Siri a try. Using Siri to Run Apps and Find Information You can get Siri's attention in several ways: Press the Home button until you hear two beeps. This works whether the phone is locked or not, unless you have configured your phone not to allow Siri to work from the lock screen. Press the center button on your wired or Bluetooth headphones until you hear two beeps. Raise the phone to your ear if it's unlocked and you've turned on the Raise to Speak feature in Siri's settings screen. Now speak to Siri. When you're done, you should hear two higher-pitched beeps, and then you may need to wait a while for Siri's response. When Siri is ready to answer, you may hear a spoken response, or if you asked Siri to do something, such as open an app, the action will be performed, and Siri won't say anything. If you asked for something complicated, such as directions to the theater or the weather forecast for next week, Siri won't speak all that information, but it will be available onscreen. You can flick through the information, and VoiceOver will read it. Similarly, Siri won't read back what it thinks you said, but you can read that information with VoiceOver. Sometimes Siri will need more information. Siri will ask a question, and then beep twice. You don't need to do anything special; just speak your answer.
    [Show full text]
  • Master in Computer Science and Engineering Project for Knowledge
    Master in Computer Science and Engineering Project for Knowledge Representation and Reasoning Course 2019/2020 Knowledge Representation and Reasoning is an important area in AI since the very beginning of this field and the area has proposed different approaches to represent and reason about knowledge. In our KRR course we will come across several paradigms: - First Order Logic - Horn Clauses - Rules and Production Systems - Frames - Description Logics - Non-monotonic Logics - Inheritance Systems Current knowledge representation systems use one or a mixture of the studied approaches. The aim of the project is to look into some of today's interesting knowledge representation and reasoning working systems and try to answer the following questions: - what were they built for, what was/is the purpose, what was/is the scope? - who built them (company, research group)? - are these systems still active and in use? or dormant? - what kind of representation paradigm(s) do they use or rely upon? which language is used to represent knowledge? If needed explain a bit of the language. - what kind of reasoning paradigm(s)/inference do they use or rely upon? which reasoners are used to infer knowledge? If needed explain a bit the reasoning procedures. - how were they build? from scratch, reusing, automatic learning, free collaborative anonymous volunteers, carefully build by a small closed team? Describe details. - how were they build from a process and life cycle point of view? Describe details. - what, how and from whom was knowledge extracted ? How was it implemented in the system? - what domain do they deal about ? - what kind of reasoning do they do ? - which other areas of AI are used in the system and combined with the KRR component? How are they combined? Explain as much in detail as possible.
    [Show full text]
  • Natural Language Queries for Visual Data Analytics
    Quda: Natural Language Queries for Visual Data Analytics SIWEI FU, Zhejiang Lab, China KAI XIONG, Zhejiang University, China XIAODONG GE, Zhejiang University, China SILIANG TANG, Zhejiang University, China WEI CHEN, Zhejiang University, China YINGCAI WU, Zhejiang University, China 1. Expert Query Collection 2. Paraphrase Generation 3. Paraphrase Validation 36 data tables from 11 domains Crowd sourcing Crowd Machine Inteview with 20 experts 20 paraphrases per expert query Validation results 920 expert queries Paraphrasing a1. Which video is the most liked? Sorting a1. Which video is the most liked? a. What is the most liked video? a2. What video has the most likes? a2. What video has the most likes? b.Rank the country by population a3. Please name the most liked video. a3. Please name the most liked video. in 2000 a4. This is the most like video a4. Which video has the highest c. Do app sizes fall into a few clusters? amount of likes? a5. Which video has the highest d. Is the age correlated with amount of likes? a5. This is the most like video …the market value? Fig. 1. Overview of the entire data acquisition procedure consisting of three stages. In the first stage, we collect 920 queries by interviewing 20 data analysts. Second, we expand the corpus by collecting paraphrases using crowd intelligence. Third, we borrow both the crowd force and a machine learning algorithm to score and reject paraphrases with low quality. The identification of analytic tasks from free text is critical for visualization-oriented natural language interfaces (V-NLIs) tosuggest effective visualizations. However, it is challenging due to the ambiguity and complexity nature of human language.
    [Show full text]
  • Nicky: Toward a Virtual Assistant for Test and Measurement Instrument Recommendations
    2017 IEEE 11th International Conference on Semantic Computing Nicky: Toward A Virtual Assistant for Test and Measurement Instrument Recommendations Robert Kincaid Graham Pollock Keysight Laboratories Keysight Laboratories Keysight Technologies Keysight Technologies Santa Clara, USA Roseville, USA [email protected] [email protected] Abstract—Natural language question answering has been an area have to design a test plan for the device under test that includes of active computer science research for decades. Recent advances specifying the measurements and ranges required. These have led to a new generation of virtual assistants or chatbots, measurement requirements dictate the specific test equipment frequently based on semantic modeling of some broadly general necessary. Creating this test setup can be a somewhat complex domain knowledge. However, answering questions about detailed, task. A typical test scenario will require many different highly technical, domain-specific capabilities and attributes measurements to be made over varying combinations of remains a difficult and complex problem. In this paper we discuss conditions and will usually involve multiple instruments and a prototype conversational virtual assistant designed for choosing devices. Test and measurement companies such as Keysight test and measurement equipment based on the detailed have product lines consisting of hundreds of different measurement requirements of the test engineer. Our system allows instruments (both current and legacy models still in use) with a for multi-stage queries which retain sufficient short-term context to support query refinement as well as compound questions. In variety of potentially overlapping capabilities. Each device has addition to the software architecture, we explore an approach to its own characteristic measurement functions, measurement ontology development that leverages inference from reasoners and ranges, accuracy, software compatibility and more.
    [Show full text]
  • Ontomathp RO Ontology: a Linked Data Hub for Mathematics
    OntoMathPRO Ontology: A Linked Data Hub for Mathematics Olga Nevzorova1,2, Nikita Zhiltsov1, Alexander Kirillovich1, and Evgeny Lipachev1 1 Kazan Federal University, Russia {onevzoro,nikita.zhiltsov,alik.kirillovich,elipachev}@gmail.com 2 Research Institute of Applied Semiotics of Tatarstan Academy of Sciences, Kazan, Russia Abstract. In this paper, we present an ontology of mathematical knowl- edge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensi- ble models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE. Keywords: Ontology engineering, mathematical knowledge, Linked Open Data. 1 Introduction Recent advances in computer mathematics [4] have made it possible to formalize particular mathematical areas including the proofs of some remarkable results (e.g. Four Color Theorem or Kepler’s Conjecture). Nevertheless, the creation of computer mathematics models is a slow process, requiring the excellent skills both in mathematics and programming. In this paper, we follow a different paradigm to mathematical knowledge representation that is based on ontology engineering and the Linked Data principles [6]. OntoMathPRO ontology1 intro- duces a reasonable trade-off between plain vocabularies and highly formalized models, aiming at computable proof-checking. OntoMathPRO was first briefly presented as a part of our previous work [22]. Since then, we have elaborated the ontology structure, improved interlinking with external resources and developed new applications to support the utility of the ontology in various use cases.
    [Show full text]
  • Iphone User Guide for Ios 5.1 Software Contents
    iPhone User Guide For iOS 5.1 Software Contents 9 Chapter 1: iPhone at a Glance 9 iPhone overview 9 Accessories 10 Buttons 12 Status icons 14 Chapter 2: Getting Started 14 Viewing this user guide on iPhone 14 What you need 14 Installing the SIM card 15 Setup and activation 15 Connecting iPhone to your computer 15 Connecting to the Internet 16 Setting up mail and other accounts 16 Managing content on your iOS devices 16 iCloud 18 Syncing with iTunes 19 Chapter 3: Basics 19 Using apps 22 Customizing the Home screen 24 Typing 27 Dictation 28 Printing 29 Searching 30 Voice Control 31 Notifications 32 Twitter 33 Apple Earphones with Remote and Mic 34 AirPlay 34 Bluetooth devices 35 Battery 37 Security features 38 Cleaning iPhone 38 Restarting or resetting iPhone 39 Chapter 4: Siri 39 What is Siri? 40 Using Siri 43 Correcting Siri 44 Siri and apps 55 Dictation 2 56 Chapter 5: Phone 56 Phone calls 60 FaceTime 61 Visual voicemail 62 Contacts 63 Favorites 63 Call forwarding, call waiting, and caller ID 64 Ringtones, Ring/Silent switch, and vibrate 64 International calls 65 Setting options for Phone 66 Chapter 6: Mail 66 Checking and reading email 67 Working with multiple accounts 67 Sending mail 68 Using links and detected data 68 Viewing attachments 68 Printing messages and attachments 69 Organizing mail 69 Searching mail 69 Mail accounts and settings 72 Chapter 7: Safari 72 Viewing webpages 73 Links 73 Reading List 73 Reader 73 Entering text and filling out forms 74 Searching 74 Bookmarks and history 74 Printing webpages, PDFs, and other documents
    [Show full text]