Thank you to all those who attended my presentation Online English Learning: Resources, Activities, and Evidence at JALT Fukuoka. Thank you also to the CALL SIG for giving me the opportunity to attend the event. The slides shown during the presentation are now available to download as a PDF.
- Do it without looking. Tell the students to look down at the line, then look up and say it.
- Do it with the book closed (students can open it briefly to check if they forget the line).
- Substitute words and phrases for the students’ own ideas, change names, places, or any other words.
- Do it with emotion – happy, sad, angry, confused, etc. Get the students to try a variety of combinations.
- Do it with an accent – American, British, robot, zombie – get the students to use their imaginations!
- Do it with gesture only but no sound, over emphasizing the gestures to convey the meaning of the text.
- Tell the students to stand up and act it out. Get them to use props and costumes if available.
- Have the students write another five or ten lines for the dialogue, and then repeat steps 1 to 7.
- Repeat steps 1 to 7 with a different partner.
- Have the students translate the dialogue into their first language(s), and then back to English again without looking at the original.
The list is available under a Creative Commons license, and can be viewed and downloaded here.
The list of real sounding “fake” words used for the new Apps 4 EFL activity “Fight the Fakes” is now available for download.
The list was generated by looping through each of the words from the SIL list and splitting them into three-letter chunks. A Markov chain process was then used to determine which of the three letter chunks were most likely to precede or follow each other. The three-letter chunks were then recombined according to these likelihoods in order to create realistic sounding neologisms of various lengths, e.g.
The words were doubled checked against the SIL list to ensure no real words were accidentally generated.
Fun ways to teach with the words
- Try the new Apps 4 EFL activity Fight the Fakes, which uses the words as distractors against low frequency items from the BNC
- Ask your students to try and invent “definitions” for the fake words based on what they sound like, e.g. “hispanelist (n.), chat show panelist from Latin America”, “mandibilious (adj.), used to describe an animal with extraordinarily strong jaws”, “rattlesnatcher (n.), a person who goes around stealing toys from small children”
- Use them as in Yes/No vocabulary knowledge tests to ensure students don’t cheat by clicking “Yes, I know this word” for every item
- Word: the word (lemma) as it appears on the original list
- POS: the most common part-of-speech for the word according to the Moby Part-of-Speech database
- BNC Rank: the frequency ranking of the word according to the British National Corpus (lower number equals higher frequency)
- Google Rank: the frequency ranking of the word according to the Google Corpus (lower number equals higher frequency)
- IPA: the International Phonetic Alphabet transcription of the word, using data derived from the CMU Pronuncing Dictionary
- Conjugations: variations of the form of the word according to tense, person, etc*
- Synonyms: a list of words with similar or related meanings*
- – 23. Multilingual definitions: Arabic, Chinese, German, Greek, English, French, Italian, Japanese, Korean, Dutch, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish*
*Data provided by public domain dictionary/thesaurus sources, where available.
Download the data:
- New General Service List (NGSL)
- New Academic Word List (NAWL)
- TOEIC Service List (TSL)
- Business Service List (BSL)
(Click the name of the list you require to open a read-only Google Spreadsheet. From the Google Spreadsheet, click “File” => “Download as” then choose your required format)
This supplementary data is available under the same license as the original lists: Creative Commons Attribution-ShareAlike 4.0 International License.
The final list consists of 3,773 high frequency TOEFL words, and can be downloaded here.
Step 1: Assemble a corpus of TOEFL materials
For my corpus, I used material from both the older CBT (Computer Based Test) and the current iBT (Internet Based Test). I found most of the materials online for free. Some were already in plain text format, but most were PDFs and required Optical Character Recognition (OCR) to convert to plain text. I used ABBYY’s FineReader Pro for Mac, but there are plenty of other options out there too. Some files were Microsoft Word format (.doc/.docx), and MacOS X’s batch conversion utility came in hand for these. I included model answers, listening transcripts, reading passages and multiple choice questions (prompts, distractors and answers). I tried to exclude explanations, advice and instructions from the authors and/or publishers.
Ultimately, I ended up with corpus just shy of a million words (959,124 to be precise). In general, bigger is better when it comes to corpus research. The TOEIC Service List (TSL) utilizes a corpus of about 1.5 million words, so my TOEFL corpus seems roughly comparable to this.
Step 2: Count the number of occurrences of each word
I used some custom PHP code to process my corpus data (although Python is probably more suited for corpus analysis). I lemmatized each token where possible using Yasumasa Someya’s list of lemmas. I then cross referenced each lemma occurrence with the NGSL, NAWL and TSL. Finally, I exported to a CSV, and ended up with 13,287 rows of data.
Step 3: Curate the final list
For my final list I removed any words which also appear on the NGSL, any contractions (e.g. “Don’t”,”I’m”,”that’s”), any numbers written in word form (e.g. “two”,”million”), any vocalizations (e.g. “uh”,”oh”), any ordinals (e.g. “first”,”second”,”third”), any proper nouns (“James”, “Elizabeth”, “America”, “San Francisco”, “New York”), and any words with fewer than 5 occurrences in the corpus. Next, I ran the list through a spell checker, and excluded any unrecognized words. I also excluded any non-lexical words, to leave a list consisting only of nouns, verbs, adjectives and adverbs.
The video for my recent presentation at JALT International conference is now available! Error Spotter is a new web-app for improving students’ recognition of English grammatical errors.
BYOD (Bring Your Own Device) is the only solution for educators who wish to use technology in the classroom when access to a CALL lab or institutional set of devices is not available.
Almost all university freshmen in Japan now possess a smartphone of some description. These are generally either iPhones running iOS or OEM handsets running Android. iOS seems to be somewhat more popular in Japan, but there are still a fair number of students with Android handsets, and a few with rarer hardware/software combinations.
If you are relying on BYOD for your tech-powered teaching, the fact that not all your students will have the exact same device is where your problems begin, but not, unfortunately, where they end.
OS fragmentation is “a barrier to a consistent user experience, a security risk, and a challenge for app developers.” It is caused by mobile device owners’ unwillingness or inability to update to the latest version of their device operating system whenever an update is released. This problem is particularly pronounced for Android handsets, but also exists in relation to iOS.
This might not be a problem for individual users, but it becomes a major issue when leading a group of students in lock-step through a structured learning process. The fact that the “user experience” is inconsistent means that there is no single set of instructions that all students will be able to follow. The fact that developing for every possible OS/handset combination is a challenge means that many apps only run on the latest OS versions of the most popular handsets.
So, although every student may possess a smartphone, not every smartphone will be able to run the cool CALL app you have in mind. Even if they can, you will either have to give individual support to every student in helping them set up the activity, or create multiple iterations of the instructions to cover every OS/device eventuality.
Unlike institutionally owned devices, which can be easily wiped after the user logs out or finishes the class, student owned devices contain a trove of personal data: photos, messages, appointments, contact information, and more.
Most students would probably feel uncomfortable sharing at least some of this information with their teachers. So when we walk around the room monitoring students to make sure they are on-task, or helping them set up the mobile-based CALL activities, we have to be careful not to inadvertently peek into the personal lives behind the tiny glowing screens in their hands.
Ever since Apple overhauled the iOS notification system, it seems that every app and its dog wants to send me updates, offers, news and status reports. While I endeavor to disable notifications for any app that doesn’t absolutely need them, my students tend to be less discerning. There’s nothing worse than setting up a class activity on mobile devices, only to have students navigate away from the app or site the moment a giant emoji-laden message drops down from the top of the screen. Even the students who diligently dismiss annoying messages from friends must find them a distraction from the learning process.
And I haven’t even begun to mention the students who will double click the home button and go back to Candy Crush the minute you’re not hovering over their shoulders and spying on their screens.
The modified version of Maslow’s hierarchy of needs now puts battery life right at the bottom of the pyramid, directly below “Wi-Fi”. Yes, this a sarcastic dig at millennials’ seeming inability to pull themselves away from their devices and do something healthy like.. climb a tree. However, in the CALL-based EFL classroom, it is a very pertinent observation.
Battery life hasn’t really improved as much as we’d like in recent years, and certainly not as much as storage capacity or processor speeds. It seems that battery life isn’t subject to Moore’s law, as the science behind it is based on thermodynamics rather than electrodynamics.
This means that students, who are already heavy mobile users, may simple not have enough juice to utilize their devices during study time as well as break time. Where this is the case, you’d better hope that you have enough power outlets and charging cables to get them hooked back up to the mainline.
Capped data plans on mobile are generally the norm these days. There may be actual technological reasons behind this, but the cynical side of me suspects it’s just the carriers trying to milk heavy users for more money.
In any event, if you don’t have an easily accessible Wi-Fi network in your classroom (which isn’t restricted to just teachers) and you’re asking students to use their own data connections to engage with your chosen app or website, you have to be careful not to inadvertently incur additional charges for your students. Usually they will be quick to let you know when this is the case, but it can be yet another barrier to the successful exploitation of BYOD.
If you can overcome the difficulties presented by various models of various handsets running various versions of various operating systems, and all students have a fully juiced up device with plenty of bandwidth, and they are able to pull themselves away from Candy Crush, and ignore messages from their friends in other classes, then BYOD can be a good way to gain access to mobile technology in the classroom.
However, we must be careful not to appropriate students personal (and often private) devices as our own teaching tools, despite how cool that new ELT app may be.