In the framework of the PROLEARN project, we have addressed many of the issues raised in this paper. There we created a roadmap toward professional learning in 2020. It would be a nice exercise to compare our results in predicting the future of professional learning and work.
The implementation of a connectivist/LaaN-based learning system can be similar to the implementation of database systems (DBS). I don't mean that connectivist/LaaN adopts an objectivistic view of learning where knowledge is viewed as an object and as a consequence can be stored in a database. It's just that we can borrow some of the concepts used in the implementation of DBS and apply them when trying to implement a connectivist/LaaN-based approach to learning.
Let me first give a brief overview of the architecture of a DBS. More information about this architecture can be found in the following references:
- H. Garcia-Molina, J. Ullman, and J. Widom: Database Systems: The Complete Book. Prentice Hall, 2001.
- R. Ramakrishnan and J. Gehrke: Database Management Systems. 3rd Ed. McGraw-Hill, 2003.
- T. Härder, E. Rahm: Datenbankssysteme - Konzepte und Techniken der Implementierung. Springer, 1999.
A DBS consists of three main components: (1) Data System, (2) Access System, and (3) Storage System.
Data System:
The main component of the Data System is the query processor. The query processor’s task is to turn user queries into a sequence of database operations and execute those operations. The query processor performs two operations: (1) query compilation and (2) query execution.
At query compilation, the query is parsed and a parse tree for the query is generated. Then the query optimizer, which is the most important component of query compilation, first, converts the parse tree to an initial query plan, which is usually and algebraic representation of the query, and then transforms this initial plan into an equivalent plan that is expected to require less time for execution. Second, the query optimizer turns the generated equivalent plan (logical query plan) into a physical query plan by selecting algorithms to implement each of the operators of the logical plan, and by selecting an order of execution for these operators.
At query execution, the algorithms selected at the query compilation phase are executed. These algorithms fall mainly into three classes: (1) sorting-based methods, hash-based methods, and index-based methods.
Access System:
The Access System uses auxiliary structures to speed up the retrieval of records in response to certain search conditions in order to avoid scan of all pages or segments or even the whole DB. Indexes are often used in speeding up queries. The indexes commonly used in commercial DB systems are B-trees and hash tables.
Storage System:
The DB data is stored on external storage devices such as disks and fetched into main memory as needed for processing. The unit of information read from or written to disk is a page (block). The basic abstraction of data in a DBMS is a collection of records, or a file, and each file consists of one or more pages. In other words, a DB relation (table) consists of a set of records and is stored on one or more pages of a disk file.
The table below shows the analogy between a DBS and a connectivist/LaaN-based learning system.
In LaaN, in order to learn, we build, maintain, and extend our personal knowledge networks with new explicit/tacit knowledge nodes. The diagram below depicts the processing of a simple query in the different layers of a DBS and a connectivist/LaaN-based learning system.
Google announced yesterday a new nice feature in Google Reader: automatic feed translation.
Next time you find an interesting feed in another language, just subscribe to it as normal in Reader. When you view the feed in Reader, check off "Translate into my language" in the feed settings, and (voila!) the feed will be immediately translated for you. Also, this setting will be saved so you can always view this feed in your own language.
Obama’s movement has unleashed a bottoms up openness. The People can no longer be seen as a passive, fear-focused, manageable mob whose only job is to show up to vote every 4 years for the fear-monger in chief. The future will not look like the past. It will be better. There will be more thinking, more doing, more optimism and more real politics.
I believe this is exactly what our educational systems do urgently need...Change...A move away from command, control, and passivity toward openness, emergence, flexibility, participation, and dynamic.
Our educational institutions do need their Barack Obama...It’s long past time for change...
A video of Luis Suarez, speaking at the Web 2.0 Expo in Berlin, on his 9-month-long experiment in “Thinking Outside the Inbox”. Luis bas been trying in the past 9 months to use different social software tools rather than email in order to keep connected to his personal network. In this talk, he reports on that experience. Actually, I like the idea but I'm wondering why not to use both; i.e. social software tools and emails in parallel. I believe there are some contexts where it's better to use emails.
More than ever before, students have the potential to own their own learning—and we have to help them seize that potential. We must help them learn how to identify their passions; build connections to others who share those passions; and communicate, collaborate, and work collectively with these networks. And we must do this not simply as a unit built around "Information and Web Literacy." Instead, we must make these new ways of collaborating and connecting a transparent part of the way we deliver curriculum from kindergarten to graduation.
Prof. Dr. Mohamed Amine Chatti is professor of computer science and head of the Social Computing Group in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. He has a diploma degree in computer science from the Technical University of Kaiserslautern in 2004 and a PhD in computer science from RWTH Aachen University in 2010. His research focuses on Social Computing, Web Information Systems, Data Science, Visual Analytics, Learning Technologies, Learning Analytics, and Knowledge Management.
