Sociolinguistics reading materials


Do download for the classroom use only.  Click the links to retrieve the files. Thank you. Enjoy your reading!

  1. Anne Pakir Singapore
  2. Anne Pakir The Role of Language Planning in Singapore
  3. Gary Jones Planning Language Change
  4. Kamsiah Abdullah Brunei Darussalam
  5. Ruth Wong & Joyce James Malaysia
  6. sociolinguistics notes for 7th oct
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“Little-Micro-Mini” Project For Week 12:

“Based on the literature review from your reading assignment, conduct a document analysis and write a review to list definitions, factors and indicators of collaborative filtering.  Post it in your blog.”

Definitions, factors and indicators of Collaborative Filtering.

According to Wiki, Collaborative filtering (CF) is “the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets.”

“Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data – such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data – such as financial service institutions that integrate many financial sources; or in electronic commerce and web 2.0 applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.”

Collaborative filtering is also a method of “making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of the CF approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user’s tastes (likes or dislikes).”

Webwhompers defines CF as getting expert opinions without the experts using web techniques for generating personalized recommendations. Some of the examples include Amazon, iTunes, Netflix, LastFM, StumbleUpon, and Delicious.

It is also mentioned that CF “needs no built-in subject knowledge to generate recommendations. Many other Web sites do rely on built-in expert subject knowledge to generate recommendations, and so do not use collaborative filtering.”

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Week 12 Reflection

Week 12 Reflection

As an avid user of blogspot, switching to wordpress has certainly made quite an impact on my use of blog for teaching. Blogspot has it’s own limitation being formatted for online journal or log, WordPress on the other hand, makes sharing with my students easier.

It is an effective platform to share materials and although there are a lot of applications and buttons to deal unlike Blogspot, I believe that WordPress has a lot more to offer. For a young teacher like me, I don’t really face a lot of difficulties in understanding the use and the format of the blog, but maybe WordPress can be made user friendly in future for older teachers to utilize for teaching in the classroom.

When I learned that WordPress has a storage space for uploading files, I am eager to use it more for teaching and learning. I came up with a blog to use in the classroom and tried to encourage my students to interact and update their blogs too. I am sure they are as fascinated as I am to learn that they can host their own file to share with their peers.

Here’s the link for my teaching blog Feel free to use the materials provided.

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