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Medium 9781601322401

E-Learning Factors and Definitions

Slice PDF October 23, 2014


Int'l Conf. e-Learning, e-Bus., EIS, and e-Gov. | EEE'13 |

E-Learning Factors and Definitions

Abdullah Abdulaziz AlShoshan

1000 FANER Drive 2125-4511

Southern Illinois University

Carbondale Illinois 62901


Technical and Vocational Training

Corporation - Saudi Arabia


Mehdi Zargham

1000 FANER Drive 2125 -4511

Southern Illinois University

Carbondale Illinois 62901

Contact author: AlShoshan

EEE ‘13

Abstract - This research has focused on the key factors that affect e-learning. These factors have been grouped into four broad categories of instructor, student, interface design and resource support. In terms of the instructors, this mainly refers to the ability of the teacher to provide different e-learning activities and facilitate the learning process. The student perspective involves the ability and willingness of students to collaborate in the teaching/learning process, as well as his/her ability to give feedback relating to the e-learning experience. Other influential factors relate to the interface design that refers to the appearance of the learning environment. Under this, consideration is given to the availability and security of the system, userfriendliness and the content that is delivered. The final category relates to resource support encompassing the availability and speed of the systems. Online help and support are also critical elements in enhancing learners’ experience.

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Medium 9780989175913

10. The No-Paradox Paradox

Slice PDF June 25, 2015

“What about time travel?” he asks.

“Nope,” Claire answers him, “you can’t do that.”

“Because of the paradox,” adds John.

“Because you could kill your parents before they meet,” says Claire.

“So your parents couldn’t give birth to you,” adds John.

“So there would have been no you to go back and kill them,” says Claire.“

Anything you do could totally change everything,” adds John.

“And so on,” says Claire.

“Yeah,” adds John, “and so on.” See more
Medium 9780596527549

9.2 Checking Your Source Code’s Complexity with SourceMonitor

Slice PDF May 29, 2014


Figure 9-5. Module coupling metrics for the selected assembly

Reflector.Graph and Reflector.CodeMetrics in a Nutshell

As of this writing, neither Reflector.Graph nor Reflector.CodeMetrics is completely stable, and they caused several crashes during the writing of this article. As with many

Reflector addins, documentation for them is scarce or nonexistent.

Given that, what compelling reason is there for choosing to use Reflector.Graph and

Reflector.CodeMetrics instead of other tools, such as NDepend or SourceMonitor? Simple: the addins’ integration with Reflector, a tool every developer should learn to use on a frequent basis. These addins are not suitable for historic tracking, nor can they be wrapped into an automated process. That’s not their selling point. Their selling point is their pure simplicity and availability inside Reflector, which makes them quick and easy to use without having to jump to another tool.


Checking Your Source Code’s Complexity with SourceMonitor

Adding a cyclomatic complexity check to your development process can quickly point you to potentially troublesome areas of your code base. SourceMonitor, written by James Wanner of Campwood Software, is a simple tool that gathers cyclomatic complexity statistics and other metrics for many languages, including C#,

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Medium 9781574412659

20. Earliest Merchants

Slice PDF May 17, 2014

20. Earliest merchants

Merchants were people who sold goods and therefore included the wholesalers and retailers and the receiving, forwarding, and commission merchants insofar as they expanded into such operations.

Merchants and their employee clerks constituted by far the largest occupational listings in the 1860 census, indicating the degree to which

Jefferson was a mercantile town. All merchants sold retail to the local population and people who came to town. Many sold wholesale to interior merchants and planters, providing one of the features that was integral to Jefferson’s unusual status as a commercial center.

First Advertisement

The first extant advertisement for a Jefferson business is for the druggists Alexander & Chrisman and appears in the June 17, 1846,

Clarksville Northern Standard (Fig. 20-1). The firm was composed of the partners Alexander Alexander (his name according to the deed records) and John Chrisman. The advertisement announces a desire to close the business and to sell the small stock of drugs and medicines along with equipment such as specie jars, tincture bottles, and drawers. That Jefferson had a drugstore at such an early date is not surprising because drugs and medicines were imported and sold to interior druggists. What is surprising is the dissolution of this firm shortly after it was started. The reasons remain unknown.

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Medium 9781449323950

12. Analyzing Data with Hadoop

Source: Spring Data
Slice ePub May 27, 2014

While the MapReduce programming model is at the heart of Hadoop, it is low-level and as such becomes a unproductive way for developers to write complex analysis jobs. To increase developer productivity, several higher-level languages and APIs have been created that abstract away the low-level details of the MapReduce programming model. There are several choices available for writing data analysis jobs. The Hive and Pig projects are popular choices that provide SQL-like and procedural data flow-like languages, respectively. HBase is also a popular way to store and analyze data in HDFS. It is a column-oriented database, and unlike MapReduce, provides random read and write access to data with low latency. MapReduce jobs can read and write data in HBases table format, but data processing is often done via HBases own client API. In this chapter, we will show how to use Spring for Apache Hadoop to write Java applications that use these Hadoop technologies.

The previous chapter used the MapReduce API to analyze data stored in HDFS. While counting the frequency of words is relatively straightforward with the MapReduce API, more complex analysis tasks dont fit the MapReduce model as well and thus reduce developer productivity. In response to this difficulty, Facebook developed Hive as a means to interact with Hadoop in a more declarative, SQL-like manner. Hive provides a language called HiveQL to analyze data stored in HDFS, and it is easy to learn since it is similar to SQL. Under the covers, HiveQL queries are translated into multiple jobs based on the MapReduce API. Hive is now a top-level Apache project and is still heavily developed by Facebook.

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