Active Learning for Adaptive Internet

CopperCore is world's first open source IMS Learning Design (IMS LD) Engine that supports all three levels of IMS LD (A, B and C). IMS LD is a complex, semantically rich specification, so it is not trivial to provide full support for it. IMS LD specifies a template of a synchronized and personalized workflow through a course. The CopperCore Run Time environment is a ready to use, pre-configured version of CopperCore. It is provided for everyone who wants to experience IMS Learning Design and CopperCore first hand without going through the hassle of setting up a complex client server application. CopperCore, a J2EE runtime engine for IMS LD can be used to incorporate IMS LD in your own applications. The targeted audience are therefore system developers. CopperCore provides three API's and a Test Suite. Here are some characteristics: - full support for IMS LD including level A, B and C - has three API's covering publication, administration and delivery of IMS Learning Design - exposes J2EE, native Java and SOAP interfaces - provides a validation library - includes a command line interface to most of the API calls - includes an example of a publication interface - includes an example of a web delivery interface - platform independent - has built-in support for four relational databases (MS SQL Server/MSDE, PostgreSQL, Derby (AKA-IBM cloudscape) and HSQLDB) - is ready for use with JBoss 3.2.x application server, but runs on other application servers as well - licensed under the GNU GPL

1. Global Description The QTI engine provides support for presentation and evaluation of QTI questionnaires, including dynamic and adaptive questionnaires. 2. Functionality The QTI engine interprets a questionnaire defined under the IMS-QTI standard, and presents it to the final user (learner). The functionality includes the dynamic generation of questionnaires based on defined attributes, evaluate them, present feedback to the user, and provide an effective integration of the overall test result with the eLearning application. 3. Potential offered The QTI engine can be exploited in eLearning platforms that do not provide facilities for structured and dynamic questionnaires. In addition this package can be exploited in other applications (not just eLearning platforms). 4. Added Value and Benefits - Presentation of items (questions) of very different types (previously defined with the IMS-QTI standard): True/False (Text), Multiple Choice (Text, Images), Multiple Choice with Slider Rendering, Multiple Response (Text, Decimal, Integer), Multiple Response with (Multiple) Image Hot Spot Rendering, Single and Multiple Fill-in-Blank (Text), Standard Short Answer (Text), Order Objects (Text, Image), Connect-the-points, Drag-and-drop (Images), and a combination of types, e.g: Multiple Choice with Fill-in-Blank, etc. - Presentation of dynamic questionnaires incorporating items from different object banks and attending to different conditions. - Evaluation (assessment) of questionnaires, presenting test results in a consistent manner, including feedback to the user. - Possibility of specific integration with the eLearning platform, i.e. it is possible to integrate the course instructional design with the tests results, thus providing control on course evolution. 5. Requirements This module does not impose any condition to the target eLearning platform. The QTI engine component is offered as an independent service. 6. Technical Features - The IMS-QTI module is supported by the IMS-QTI standard defined by IMS. - It takes advantage of flexibility inherent to IMS-QTI standard and thus offers the capability of integration with other standards as IMS-LD and IMS-LIP, through attributes (meta-data). Integration with IMS-LD provides Instructional Design based courses with the capability of acting upon results of questionnaires. Integration with IMS-LIP makes that this integration is based on standardised user-defined features. - The component is implemented in Java and Flash The QTI Engine is provided under GNU GPL licence and can be downloaded from http://sourceforge.net/projects/alfaqtiautor/ and accessed from http://rtd.softwareag.es/alfanetqtitools/

1. Functionality: The QTI Authoring Tool is a tool that building on existing QTI files (defined with QTI compliant tools) adds on them to define adaptive and dynamic questionnaires based on the "Selection and Ordering" specification provided by the IMS-QTI standard. The QTI Authoring tool provides two main functionalities required for further adaptation of questionnaires: - Items metadata definition. It allows being associated metadata with QTI items previously created. - Dynamic questionnaires definition. This component complements the previous one to add adaptive capabilities as defined in IMS-QTI standard, supporting the definition of questionnaires that may combine items from different object banks having into account specific conditions. The QTI Authoring Tool can be exploited in those applications that making use of QTI specification does not take advantage of the benefits provided by dynamic questionnaires. The Added Value and Benefits are the generation of dynamic questionnaires: - incorporating items from different object banks and attending to different conditions - adaptive. - able to provide different evaluations (zero or various scoring variables) as desired by the course designer- assessment. 2. Requirements: This module does not impose any condition to the target eLearning platform. The QTI module is offered as an independent service. Current state of IMS-QTI module requires the usage of an external QTI tool to generate the basic items following the QTI standard. 3. Technical Features: - The IMS-QTI module is supported by the IMS-QTI standard defined by IMS. - Java based software It takes advantage of flexibility inherent to IMS-QTI standard and thus offers the capability of integration with other standards as IMS-LD and IMS-LIP, through attributes (meta-data). Integration with IMS-LD provides Instructional Design based courses with the capability of acting upon results of questionnaires. Integration with IMS-LIP makes that this integration is based on standardised user-defined features. The QTI Authoring Tool is provided under GNU GPL licence and can be downloaded from http://sourceforge.net and it is accessible from http://rtd.softwareag.es/alfanetqtitools/

The ALFANET project has provided significant Knowledge in different fields, which in turn have benefit from their interactions generating new knowledge relevant for the e-Learning in Europe. In the following lines the main knowledge generated by the project is outlined: 1. Pedagogical methods and guidelines. ALFANET focus on adaptation have been managed from a conceptual and methodological perspective that describes a baseline pedagogical model (conceptual template) to define courses together with other templates supporting the definition of adaptation, that in run-time will be supported by different ALFANET components. 2. The ALFANET project has an important experience in usage of e-Learning standards (IMS-CP, IMS-LD, IMS_QTI, IEEE-LOM, IMS-LIP) and interoperability of these standards for the purpose of adaptation along the e-learning lifecycle. Based on these standards and on its innovative architecture components, the ALFANET platform supports multiple adaptive scenarios, among others: - it provides different e-learning paths for different user profiles, - it offers the learner the possibility to reinforce the knowledge when the system detects bad performance, - it provides the learner with adaptive assessments, - it provides the learner with a particular view of e-Learning objects as they fit with learner's interest, etc, - it provides the learner with tutoring support, - it offers the learner diverse recommendations about what material should be further studied, what activities should be performed, what additional tests could be made, what fora should be consulted, etc. The report D32. Standards Contribution report provides a full description of the ALFANET contribution to standards and the experienced pedagogical methods. 3. Services based architecture. ALFANET System has been designed as services based architecture that provides the platform with a great flexibility and modularity. Within the ALFANET three-layer architecture, the Server layer acts as an integration platform for all the services providing core functionality for the e-Learning adaptive Services and for its integration to achieve adaptation. The report D43. Final System describes the ALFANET technical architecture and goes into more detail of each of ALFANET components, explaining also how integration among ALFANET components has been performed. 4. Adaptive pilot courses. Five adaptive pilot courses have been designed in the project, corresponding with the four pilot sites: "Spanish course for German Learners" (KLETT), "Ambient maintenance" (EDP): "Aprender a Formar en Internet" (UNED) and "Communication technology" (OUNL). These courses implement different adaptive features and will be used for dissemination activities by the consortium partners. 5. Evaluation Results. Experiences with pedagogical methods, and adaptation along the e-learning lifecycle have been compiled at D66. Compilation of Evaluation activities, this providing the basis for further development of ALFANET components. 6. In addition, the ALFANET consortium has produced an important number of contributions to different scientific fora, making ALFANET research results available to the general public, in particular to the scientific communities. Scientific contributions are releted to e-Learning standards and e-learning standards interoperability, the User Model supporting adaptation, etc. A summary of these results is provided at D77. Compilation of Dissemination activities. The D65. User Documentation provides users with a baseline reference to use the ALFANET system at the different stages of the e-Learning life-cycle, and as such it incorporates User Manuals for the different e-learning actors: the Designer, the Administrator, Learners and Tutors, etc. The Final Public report (D81) provides a global view of ALFANET architecture, as well as ALFANET scientific results, including contribution to standards.

The Interaction Engine provides the different educational services that allow the learners to perform a course in the system, using both modes: individual learning and collaborative tasks. The Interaction Engine offers their services in three different contexts: the personal area (workspace), the course area and the activities area. For the personal area it provides an integrated vision of each one of the services and contents the user has access in the different courses s/he is enrolled, and the contributions performed in them. The course area provides the services that have to be used in each course, both if they were specified at design time by the author or at run time by the tutor. The activity area is similar to the course area, but under the scope of an activity inside the course. Provided services are as follows: - Forums - File Storage - Calendar - Notifications - Comments - Ratings - See information about Learning Objects For the tutor, the Interaction Engine provides administration functions for each one of the available services. The publication facility imports a course defined in IMS-LD and generates a Course Model accessible to the Adaptation Module, storing the services, Learning Objects, educational metadata and objectives associated to that course.

The Adaptation Module is in charge of providing run-time adaptations to cope with the learning needs and unpredictable situations that come across while interacting with the system. In other words, it identifies relevant pedagogical situations to enhance learning experience based on previous users' interactions. When detected one of these situations, the system will recommend to each individual learner in each relevant pedagogical situation of the course the most appropriate operation taking into account the interactions performed by a group of similar learners, based on implicit collaboration interactions. As result a rich interaction model about the implicit collaborations performed in our system is obtained, updating the learner model. In our approach, inference and machine learning techniques are used in (1) modelling tasks to build and update the models, (2) diagnosis tasks (computing the risk level of an educational problem for a learner) and (3) identifying similar learners with collaborative filtering techniques. All them are based on the analysis of the interactions performed (individual and implicit collaborative interactions). The recommendation process is supported by a multiagent architecture, where different types of agents (coordinator, recommendator, model and modelling) interact with each other to give the corresponding recommendation to the learner.

This component allows Authors to define course structure based on pedagogical methods, as this is supported by the IMS-LD standard, and further upload the course preparing it to be used by different users. The LD Authoring Tool is part of the Alfanet LMS prototype and its objectives are to provide an easy and user-friendly interface to create and edit courses that will run in the ALFANET System. The tool was developed in VB.Net based on a peer-to-peer application called Groove Workspace that offers several communication facilities and it's intended to be a collaboration platform for working teams. The tool can be downloaded from https://sourceforge.net/projects/alfanetat/ This component allows Authors to define course structure based on pedagogical methods, as this is supported by the IMS-LD standard, and further upload the course preparing it to be used by different users. The LD Authoring Tool is part of the Alfanet LMS prototype and its objectives are to provide an easy and user-friendly interface to create and edit courses that will run in the ALFANET System. The tool was developed in VB.Net based on a peer-to-peer application called Groove Workspace that offers several communication facilities and it's intended to be a collaboration platform for working teams. The tool can be downloaded from https://sourceforge.net/projects/alfanetat/

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