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As we know, there are four skills of language learning: reading, writing, listening, and speaking. This paper focuses on writing skills. In research many have discussed the effectiveness of learning systems on the Web in learning (English as a Second Language / English as a Foreign Language) (Meurant, 2010; Yang & Chan, 2008; Fan, 2011). It is very useful that another person can point out your errors and correct your entries, much like teachers correcting homework for students in a class. Corrective feedback is a technique to help learners correct errors by providing them with some kind of prompting (Lo et al., 2008). It is useful for learners to write in a foreign language and ask another person to correct it for them so they can be aware of their errors. However, a problem for learners' is how to reflect on corrections and then master the errors.
Reflection is a way of helping participants better understand what they know and do as they develop their knowledge and skills through reconsidering what they have learned (Loughran, 2002). Hatton and Smith (1995) identified that we should consciously account for the wider historic, cultural, and political values or beliefs in framing practical problems to arrive at a solution.
Nowadays, many web-based learning services are provided in many different forms, such as: chat forums, newsgroups, blogs, virtual environments, search engines and social networks. Of these, one of the fastest growing areas is that of Social Networks Services (SNS). There are many SNSs for language learning, such as Lang-8 (http://lang-8.com/). Lang-8 is a place for learning and practicing foreign languages. When you write an entry in the language you are learning, a native speaker will correct your entries.
In this paper, we used the writings and corrections created by users in Lang-8 as a source of data. In this sense, we used Lang-8 not as an SNS service, but as a crowdsourcing data collection method.
Using data collected from large groups of disparate people, known as crowdsourcing, has become a more and more popular with increasing Internet usage. As opposed to traditional data collection methods that use known groups of people to collect data, crowdsourcing data collection utilizes data that has been generated by an unknown public group. For example: Coleman (2012) used twitter in crowdsourcing data to create a web application aimed at learners of foreign languages.
We collected the diaries from Lang-8 (lang-8.com/) in which language learners have written a diary in a foreign language and a native speaker has corrected it for them. The error patterns and frequencies of writings in English were analyzed using these diaries. A quiz system was then created to train learners, using the error patterns and frequencies to determine which questions the user should undertake.
Kroll (1990) and Weltig (2004) classified the error patterns of English learners writings. These error patterns targeted the English students who were studying in their respective universities. With the development of the Web, many learners are studying English online on web sites such as Lang-8. Based on the error patterns of Kroll (1990) and Weltig (2004), analysis has been performed on the error patterns of Lang-8 users. Then, based on the error patterns of Lang-8 users, a demonstration quiz system has been developed, which is proposed to analyze the error characteristics of every student. Students can train their English focus on their error characteristics, and then improve their English.