Monday, March 19, 2018

AI has and will change language learning forever

Just before the dawn of the internet I worked with the CEO of a major CD language learning company. His business model was fascinating, 
I don’t sell language learning, I sell the false promise…. My customers are ‘false starters’ mostly middle-class people who think they’ll learn a language in a few months before they go to Italy, Spain or France on their holidays… they never do.” He explained that the whole market was based on this model. The BBC packages at the time were the worst, he explained, “They’d send a film team to France for a month or so, come back and write a book around it…. it is literally impossible to learn a language from their materials”. I stayed out of that market. But times they are a changin’….
Internet
The technology moved on from CDs, along came the internet, and we saw the first big effect on learning languages – mainly English. The abundance of music, films, sport in all media on the web, allowed ready access to content, allowing contact, practice and immersion. Huge numbers have learnt languages without direct instruction. But learning a second language remains one of the most difficult things one can do in life and direct instruction still has a place. The problem with online instruction is that the technology was still too flat, text based and restricted to simple drill and practice. The content was too linear, often dull and struggled when it came to the spoken word, practice and immersion. Technology is now influencing not only what languages we learn but how and even why we learn languages. Some argue that the Anglo-saxon domination of the internet has accelerated the expansion of English as a global language. Machine translation raises the interesting possibility in that it may lead to less people learning new languages, if frictionless, real-time translation is available. But the most obvious and immediate impact will be on the practical teaching and learning of languages, where smart technology is already having a global impact.
AI
Of one thing we can be sure; AI brings a new paradigm to language learning. Natural Language Processing (NLP) has brought entity analysis, sentiment analysis, classification and machine translation. In addition we have text to speech and speech to text, now revolutionising interfaces. At the same time algorithmic techniques and machine learning brought adaptive, personalised and spaced learning. Even image recognition is being brought into identification and assessment. These technologies are being blended to produce sophisticated language learning and the possibility of learning a language without human instruction. One has to look across the whole learning journey to see how this is potentially possible.
Machine learning
To see how far AI has come in languages, Machine Translation is a good starting point . Google Translate can handle over 100 languages and us used over half a billion times a day. Launched in 2006 it used Statistical Machine Translation to match strings by probability against strings in another language, basically pattern matching. But in late 2016 it switched to Neural Machine Translation, making it much more successful and contextual. It is available as a browser extension and on Google Home and Google’s Pixel Buds. The ear ‘Buds’ can translate 40 languages in real time. To be fair, like Skype’s real time translation, it’s far from fluid and perfect but the direction of travel is clear – it will get better and better. 
Learning journey
So what about learning a language? Most successful language learning models take the learner on a learning journey from simple basics to practice then production. This progression normally starts with structural basics on the alphabet, vocabulary and grammar. Practice usually starts with limited and controlled practice and moves towards more open and free practice. Finally, there is generative production and use of the language. In addition to the actual learning there are also pedagogic issues such as motivation (a particular problem in language learning) and assessment. AI has a role to play across the whole of this learning journey.
Drill and practice
My first ever computer-based learning programme was teaching the Russian alphabet, which I built using the Commodore 64 graphic characters.  You saw a character and had to type in the corresponding English sound (as a letter or letters). I then programmed a behavioural drill and practice vocabulary programme. Randomisation was a feature, stratified with progress dependent on scores. This was typical of most early computer assisted language learning programmes.
Adaptive learning
Basic drill and practice is still a feature of most adaptive systems, such as Duolingo, with 200 million registered users, where structured topics are introduced, alongside basic grammar but adaptive algorithmic techniques track your progress and take into consideration, your forgetting curve, short-term success rate and effort. Adaptive systems can blend individual with aggregate data to optimise progress for the learner, depending on need. Every new learning event can be uniquely presented to that learner thus personalising the learning, an important form of optimisation in language learning, give the distribution of ability.
Spaced practice
Spaced practice, where the learners use retrieval techniques in a structured reinforcement pattern to push knowledge and skills from working to long term memory is a good starting point for the consolidation of acquired knowledge and skills. Anki is a free package that uses the algorithmic control of spaced practice to determine the learning path.
Chatbots
Controlled practice, to varying degrees, can also be delivered using chatbots. There are many species of chatbots from learning engagement, teaching, mentorbots, and practice bots. Chat has overtaken social media on mobiles and is clearly the preferred interface. We seem to have a natural affinity to chat interfaces and in some cases, with wellbeing bots, even the anonymity of the machine has been shown to be an advantage. They have been successfully used in educational and corporate training environments. They offer a dialogue interface, so are eminently suitable for language learning, with flexibility around the recognition of replies by the learner and, of course, speech. They have huge potential and when embodied in consumer, home devices can bring language learning into to the home.
Open practice
But active immersion is also now possible with home devices. You can switch your Amazon Echo to respond in German. Consumer technology, such as Alexa, Google Home and others will offer cheap, free and increasingly sophisticated language learning in your home. Ask it a question in English and it will reply in German. This is a bit like having a German person in your own home 24/7.
Immersion
The internet provides a wide and deep set of resources in most major languages. There’s an endless amount of content in your target language, in all media – text, audio and video - movies, box sets, music videos, Youtube, Wikipedia, whatever. Here other immersive technologies come into play, such as VR and AR. These are not AI technologies but AI techniques can be used within these environments to provide immersion, attention and context for language learning. In a current project (WildFire) we have successfully integrated speech input within VR, which not only allows you to navigate through the learning using just your voice but also input open response input and so on.
Assessment
Both Babbel and Doulingo offer paid English assessment testing. Face and digital recognition allow unique identification of candidates for assessment. Keyboard typing patterns can be recognised, along with adaptive assessment, which adapts to the candidate’s ability level, are all being used. Online assessment is now here, which increases accessibility and progress in language learning.
Conclusion
AI, with its rapid advances, specifically in technologies that aid language learning, may turn out to be the most significant technology in this field to date. The technology provides behind the scenes language processing that allows machine translation, speech recognition and many other services to be used across the learning journey to keep learners moving forward, optimising and personalising delivery. It has already accelerated the digitisation, disintermediation, decentralisation and democratisation of language learning.  Yet we must be careful in attributing too much efficacy to AI. Its translation ability is nowhere near as good as human translation, speech recognition still a bit ropey and with other services, such as chatbots you need to be a bit forgiving. Nevertheless, it is constantly improving and on current rates of progress, it seems likely that it will have a major impact in language learning.


Thursday, March 15, 2018

Should you listen to music while studying? No... here's why

There are those who extoll the Mozart Effect, I know of one who extolled the virtues of playing Mozart to her children when they were very young and when they were learning. This, she claimed, had been proved scientifically to improve IQ and their ability to retain knowledge. Remarkably, she extended her claim to the foetus.
This baloney was sparked off by a paper in Nature by Rauscher, Shaw and Ky (1993), which showed a small improvement in spatial reasoning score (very specific), the effect lasted no longer than 15 minutes, then disappeared. The theory also disappeared, as several follow up studies could not replicate the effect. Rauscher herself, disclaimed the idea, saying that they had made no claim linking the playing of Mozart to intelligence. Chabris and Steele in a meta-studies paper in 1999 put the nail in the coffin by showing that such effects are merely the result of short-term and temporary ‘enjoyment arousal'.
But education can never resist a fad and there's always someone in education who can't let a bandwagon pass  in this case Don Campbell, who published The Mozart Effect (1997) and The Mozart Effect for Children. These books are, quite simply, bogus. His claims bear no resemblance to the actual research and, if you have this idea floating around in your brain, it’s largely down to him trade-marking the effect, then publishing these books, that were then taken up by lazy ill-informed journalists. This is how it ended up in the minds of so many parents and teachers. It was even funded and applied in some states in the US, notably Georgia and Florida.
Music in general
On the general proposition, that listening to music helps one learn, we have to be as equally careful. There is a large and complex literature on this subject, testing the effect of music on various cognitive phenomena and there is some evidence that it improves mood, even motivation, but one must be careful when it comes to actual learning.
In this interesting study, silence is used as a control, along with the two major components in popular music - music and lyrics. Perham and Currie (2014) created four groups:
Silence
Music without lyrics
Music with lyrics they liked
Music with lyrics they did not like
Results
The sample (30) was small, and I'd like to see this replicated with a larger group but the results were interesting:
Revising in silence was signifiantly better than revising while listening to music with yrics (liked or disliked)
'Silence' and 'music with no lyrics'
Revising to 'music without lyrics' was produced better scores than revising to 'music with lyrics'
Revising in 'silence' group could preict olearning outcomes better than other groups
Music in online learning
Moreno and Mayer (2000) tried e-learning with the following groups: 
Learning with music
Learning with sounds
Both
Neither

When retention and transfer were tested the groups with ‘music’ performed worse than those without music. This is a well known phenomenon where cogntitive overload inhibits learning.
Why?

It's to do with the overloading of working memory, especially with spoken words. One quick experiment you can do with your kids, or students, is to take a random page from a book on a subject they are unfamiliar with. Now tell them to read it in silence. Now choose another page and ask them to read it while repeating the word ‘boing-boing’ over and over. They will be unable to meaningfully learn from the text. The reason is the overloading of working memory, the phonological loop to be exact. Music takes up valuable bandwidth, therefore inhibits learning.
Conclusion

It may be devilishly difficult to convince your offspring that music is bad when they’re studying but when faced with a 60% differential it may be worth telling them about this study. There is lots of bad advice around study techniques that focus on superficial, low retention study methods and ignore attention, effort, retrieval and deliberate practice. No doubt some wag will tell us that music is good for those with an auditory learning style... that's also bullshit.