Learning Management System Algorithm
A Suggested Learning Management System Algorithm of how to connect “Learning Nuggets” from Learning Library to a Learning Management System for delivery to the learner.
Many educational institutes used an eLearning platform Learning Management System (LMS) to automate the administration of their training events. In the same time, these institutes have a lot of different libraries, but without any integration of their local LMS and local libraries.
As a result, the learners have to search on their own for learning nuggets related to their learning goals. Consequently, the learners suffer from the Information Overload problem, when they find thousands of results that are not suitable and related to their goals.
To solve this problem, the ALC LMS presents interesting, relevant, and personalized learning nuggets. Figure 1 shows between DL and LMS.
learning library: Terms such as digital library, electronic library and virtual library are often used synonymously with learning library. A learning library id an organized and focused collection of digital objects, including text, images, video and audio along with methods for access and retrieval and for selection, creation, organization and maintenance.
The Learning Library is more than a collection of books, documents and materials of traditional library on an electronic (digital) form it also contains any digital material, e.g., Software, multimedia.
One can review a list of digital libraries in Yahoo! Search directory, <http://dir.yahoo.com/Reference/Libraries/Digital_Libraries>.
Besides ALC Learning Library, the majority of the modern traditional libraries have been automated and computerized by saving information of objects and resources (e.g., books, magazines, CD’s, etc.).
Furthermore, some of these libraries adopt also some LL functions (electronic resources) by saving digital objects.
Consequently, the user can search for these resources via internet anywhere to review full content of eBook or to see some information about a paper based book. This kind of library could be named as On-Line Library.
Digital library software packages: The market of Digital Library Software is growing some of those Packages are commercial Software, while others are free Open-Sources. The following list shows some examples:
SirsiDynix digital library, http://www.sirsidynix.com/%20Solutions/Products/digitalarchive.php
Artesia digital asset management-DAM, <http://www.artesia.com/our-products.aspx
Greenstone Digital Library Software, <http://www.greenstone.org
Fedora digital object repository management system, <http://www.fedora.info
Learning management system: LMS is software that automates the administration of training events; it manage the log-in of registered users, manage course catalogs, track learner activities and results as well as provide reports to management.
The market of LMS is increasing very fast and there are >70 vendors some of LMSs are commercial Software, while others are free Open-Source LMSs. The following list
shows some LMSs:
Commercial LMS: e.g., WebCT <http://www.WebCT.com>
and eCollege <http://www.ecollege.com
Open-Source LMS: e.g., MOODLE <http://www.moodle.org>
and ILIAS <http://www.ilias.de
Recommendations systems: Last decade, RSs have been widely implemented and accepted in many sectors of Internet. We are familiar with recommendations of products (e.g., books, music, movies) and of services (e.g., restaurants, hotels, Web sites), likewise recommendation is not arising from the digital era but an existing social behaviour in daily life. In everyday life, we rely on recommendations from others.
More and more information is available electronically; moreover, the World Wide Web is still growing faster as a result, the users suffer from the Information Overload problem when searching on Internet. Generally, the aim of RSs in Web applications is to present interesting information that fits the users tastes and preferences with little effort. In contrast, sometimes RSs are used to hide special information! and specifically, the aim of RSs in eLearning applications (e.g., LMS) is listing the closest available learning objects to what the instructor describes as the module’s content
Current usage of RS: RSs have been widely used in many Internet activities. It is worth mentioning some examples of the current actual uses of RS.
eCommerce: RSs are used to suggest products to their customers and provide consumers with information to help them decide which products to purchase. Examples: Amazon.com and barnesnoble.com.
Web pages: RS is used to solve the overload problem in the Internet, when using search engines (e.g., Google, Yahoo) which produce thousands of pages to one researched item, most of them have worthless relation to the researched item or of no interest to the user. Example of search engines which used RS:
Mi Yahoo! http://www.my.yahoo.com
Censorship systems: RSs used to protect children from accessing undesirable material on the internet. e.g., cyberpatrol.com as well as Prevent citizens from exploring some Web sites; which some governments already did.
Other sectors: Examples:
Encyclopedia: e.g., <http://www.en.wikipedia.org
RS and eLearning: eLearning somehow is a new field to apply RS, which may be used to recommend the most appropriate content to students.
MATERIALS AND METHODS
The suitability of RS approaches: Actually, RSs consist of approaches everyone has techniques. However, there are many systems that use Hybrid Recommender System (HRS), which combines two or more recommendation techniques to gain better performance.
Here, we are going to study the suitability of the main RS approaches to recommend digital objects from DL to a LMS.
Content-Based System (CBS): In this type, the objects are selected by
having correlation between the content of the objects and the user’s preferences.
Examples: Infofilter (Elkhalifa, 2004) and InfoFinder
In the case of LMS, CBS can be used within LMS as a primary approach to recommend
digital learning objects from DL by detecting similarities between the current
eCourse attributes (name, keywords, abstract etc.) and the digital objects attributes
Collaborative Filtering Systems (CFS): It recommends items or objects to a target user based on similar users’ preferences and on the opinions of other users with similar tastes. It employs statistical techniques to find a set of users known as neighbours to the target user, examples: Amazon.com and ebay.com.
CFS has some methods to calculate the likeliness from the rating matrix, the suitable one to our RS is Memory-Based Algorithm (also known as k-Nearest Neighbour Method), because it is suitable to environments where the user preferences have to be updated rapidly.
Demographic-Based System (DBS): It uses prior knowledge on demographic
information about the users and their opinions for the recommended items as
basis for recommendations (Nageswara and Talwao, 2008).
It aims to categorize the user based on personal explicit attributes and make
recommendations based on demographic group that a user belongs to such as (income,
age, learning level or geographical region) or a combination of these clusters/groups.
Examples: Grundy, a book RS, where people’s descriptions of themselves
were used to build a user model and then predict characteristics of books that
they would enjoy (Rich, 1979) and the Free e-mail suppliers
put advertisements based on the user demographic information such as RS used
in Hotmail and Yahoo.
The DBS could be used in the process of recommending digital objects as a complementary approach.
Rule-Based Filtering (RBF): It is filtering information according to
set of rules expressing the information filtering policy (Terveen
and Hill, 2001). These rules may be part of the user or the system profile
contents and it may refer to various attributes of the data items. In general,
this system is used widely with:
Censorship: RBF is useful in the protection domain e.g., the protection
of kids from accessing some materials, e.g., Cyberpatrol.com and Cybersitter.com
(Itmazi and Gea, 2006).
Spam filtering: RBF is useful to be used against the Spam e-mails, e.g.,
Spam Assassin <spamassassin.apache.org/> and MailEssentials <http://www.gfi.com>.
In RS, RBF could be used to filter the recommendations list of digital objects
upon some rules of system and student.
Hybrid Recommender System (HRS): It combines two or more recommendation
techniques to gain better performance with fewer of the drawbacks of any individual
one (Robin and Burke, 2002). Examples of systems: Tapestry
(Goldberg et al., 1992), which mixed CBS and CFS,
hybrid algorithm system (Vozalis and Margaritis, 2004)
which mixed CFS and DBS and Information lens, which combines the CBS with the
RBF (Mackay et al., 1989).
RESULTS AND DISCUSSION
A general RS proposal: The suitable RS approach to recommend digital
objects from local DL to LMS will not be a pure one but it will be a HRS, which
mixed some of the previous approaches. The following general RS structure could
be suggested to be used in LMS to recommend digital objects (Fig.
We list some consideration of this proposal structure:
The stage of content-based system: In this stage, the digital objects are selected by detecting similarities between the items of current eCourse (the active eCourse which the student already enters) and the items of digital objects in the DL. These eCourse items include: name, keywords, abstract etc. Therefore at the first stage, the CBS retrieves a list of the related digital objects from the DL database.
Algorithm of CBS: The general steps of the CBS (Fig. 3)
The stage of teacher recommendations: The teacher recommendations are
the resources which the teacher put in his eCourse as recommended ones. They
The algorithm filtered the resources to allow only the digital objects from the local DL to be added to the recommendation list.
Algorithm of teacher recommendations: The general steps of the teacher
recommendations stage (Fig. 4) are:
The stage of collaborative filtering: We use CFS as a complementary approach to organize the priorities of the recommendations. The general mechanism of CFS based on defining subgroups (every subgroup known as the nearest neighbours) whose preferences are similar to the active user, so the nearest neighbours of the active student are those students who share the same institute (department, school). Then this stage calculates the average of the subgroups rating to order the recommendations upon the high rates.
The rating matrix: The target LMS, must have a way to capture the rating by explicit, implicit methods or mixture of them. These students’ rates of the digital objects saved in the LMS database as a table of two dimension matrix where the row represents all the rates of one student on all digital objects, while the column represents all the rates of all students on one digital object (Table 1).
Algorithm of the collaborative filtering: The general steps of this
stage are as the following (Fig. 5):
The stage of demographic-based filtering: Theoretically, the role of DBF in a LMS is to filter the incoming recommendations from the previous stage upon the students’ demographic (and personal) data that related to education issues. For example, the following demographic-personal data could be related to the education issues: preferred language, student specialization, study level year faculty and department.
The language filtration as an example, means that the active student needs
all the recommended digital objects in his preferred language, so any language
of digital objects in the recommendations list defer from his preferred language
will be deleted.
Algorithm of the demographic-based filtering: DBF could be work as follow
The stage of rule-based filtering: RBF will filter the incoming recommended digital objects upon a set of rules, which could be found in the student profile and in the system profile. The system administrator put some rules in the system profile, while the student can put his own rules in his profile.
We suggest that the following types of rules that could be used in the student profile and the system profile to filter the listed digital objects (Fig. 7):
Link: The system will filter out any digital object whose link found
in the rules profiles.
Phrase or word: The system will filter out any digital object which his name, keywords or abstract match any phrase or word found in the rules profiles.
Date: The system will not show any digital object does not fit the date criteria.
Size: The system will not show any digital object does not fit the size criteria.
Type: The system will not show any digital object does not fit the type criteria.
Algorithm of the rule-based filtering: RBF could be work as follow (Fig.
8): Receiving the list of the recommended digital objects from the previous
stage. Reading the following fields of the system rules:
The system deletes from the recommendations list every digital object that
matches any link or keywords as well as any digital object whose dates are out
of the minimum-maximum dates.
It also deletes any digital object, whose size is larger than the allowed size
and whose type matches the forbidden types. Reading the same fields of rules
from the student profile and repeating the filtration process. Finally, the
recommended digital objects are prepared to be presented in a suitable way on
the windows of active student eCourse.
Content-Based System, Collaborative Filtering, Rule-Based Filtering and Demographic-Based System.