Adaptive Learning Environments

Adaptive Learning Environments (Framework Components and Parameters)

Hello! I am Marwa ElMohamady Lecturer of Educational Technology, Faculty of graduate Studies for Education, Cairo University.

You can find me: [email protected] [email protected]

This presentation is not free to use, It is under Creative Commons Attribution license. If you use the graphic assets (photos, icons and typographies) provided with this presentation you must keep the Credits slide.

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Introduction

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Pedagogical Approaches Supporting Adaptive System

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Framework Components of Adaptive System

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Parameters of Abilities Variables In E-Learning Adaptive Systems

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Examples For Adaptive-learning Environment

Introduction E-learning courses are designed based on “One Content-Fits All”, this is not true, because there are individual differences between students in abilities, cultures, backgrounds, preferences, and learning styles. The key issues characteristic of populations of online learners are their diversity. This diversity requires the presentation of different information to different learners in a different format. That is why it is very important to develop adaptive educational systems which consider various aspects of individual students in order to make the learning process as effective, efficient, and motivating as To attempt to ensure the success of these types of learners, online instruction must adapt strategies that acknowledge the individual characteristics of these learners. .

Pedagogical

Pedagogical Approaches

Framework

Adaptive Systems Supporting Specific

Parameters

Parameters

Four important considerations when designing these systems: (1) situation-based contexts, (2) learning interactions in situation, (3) time-extended processes of interaction, and (4) affordances of learning situations .

Framework

Constructivist learning theories emphasize active roles for learners to construct their own knowledge through experiences in a learning context in which the target domain is integrated.

Pedagogical

1. Constructivist Adaptive Systems:

Contingent tutoring systems generally provide two assessment methods: model tracing and knowledge tracing.

Parameters

- The purpose of knowledge tracing is to choose the next problem that is appropriately challenging so the students can move in a timely.

Framework

- The purpose of model tracing is to keep track of all of the student’s actions as the problem is solved and to flag errors as they occur. It also adapts the help feedback based on specific problem-solving contexts.

Pedagogical

2. Contingent Teaching Systems:

Framework Parameters

Some adaptive instructional systems take into account student motivation. Proponents suggest that a comprehensive adaptive instructional plan should consist of a traditional instructional plan combined with a motivational plan, a student’s motivational state based on several variables i.e., control, challenge, independence, fantasy, confidence, sensory interest, cognitive interest, effort, satisfaction.

Pedagogical

3. Motivation-Based Adaptive Systems:

Metacognitive skills enable students to assess their own learning processes.


As ICT-based individualized instruction, including online learning environments becomes increasingly prevalent, metacognitive and self-regulatory processes are becoming more important in the design of systems, metacognitive processes can be easily understood and observed in a multi agent social system that integrates cognitive and social aspects of cognition within a social framework.

Pedagogical

4. Metacognition-Based Adaptive Systems:


One of new pedagogical approaches incorporated in adaptive instructional systems is collaborative learning. Through the use of an adaptive collaborative system identified five characteristics of effective collaborative learning behaviors: (1) participation, (2) social grounding, (3) performance analysis and group processing, (4) application of active learning conversation skills, (5) and promotive interactions.

Pedagogical

5.Collaborative Learning Systems:

Pedagogical Framework

Framework Components of Adaptive System Models

Parameters


Parameters

The structure of the Adaptive Instructional environment


Parameters

The structure of the Adaptive Instructional environment

Framework

Parameters

The domain model describes the structure of hypermedia as a set of components. The "Component" class is used to represent abstractly all components of the application domain: concepts, pages, fragments, goals and relationships between components. The domain model can also describe, through the class "Presentation specification“ A page consists of one or more fragments. A fragment belongs to a media channel (audio, video, Image, text, etc.). Each channel is described by properties (audio volume, text style, brightness of images, etc.) which are used to personalize the presentation.

Pedagogical

1- Domain Model

Framework

A course is the set of educational activities chosen to represent a specific material to meet a very specific purpose. The course structuring model is composed of four types of nodes:  Purpose  Goal.  Activity.  Component.

Pedagogical

Course Structuring Model

Parameters

The learner model describes the learner by an identifier (LID) and a set of attributes. several types of information contained in a learner model: name, background, experience, goals… classified in categories. The learner characteristics are given below:

Framework

Parameters

• Personal information: such as: name, age, language, educational level, diplomas, certificates, etc., • Physical preference: It is related to the channel of media (audio volume, font, video speed, etc.), • Cognitive characteristics: They are: – Cognitive capacity for example the speed of learning, – Cognitive preference, such as the type of interactivity with the system (active or passive), the density of content, the degree of difficulty, the resource type (formal, graphical, simulation, etc.).

Pedagogical

2- Learner Model

Framework

Parameters

• System Administrators Model. • Instructors Model. • Students Model.

Pedagogical

The users’ model is divided into three sub models that represent the users that are interacting with the system. The following are the defined user type’s models:

Administrators in the adaptive system will be responsible for different tasks that include:

different universities in order to create the appropriate course structure.

• Creating instructors / students accounts.

Parameters

• Creating the course structure and defining the course hierarchy and metadata used.

Framework

• Coordinating with instructors from

Pedagogical

2-1 System Administrators Domain Model

Pedagogical

Framework

Parameters

Instructors in the adaptive system will have four different responsibilities that are: • uploading different learning objects with respect to the course structure.

• adding assessments to be associated with each resource they upload to the course with respect to the course structure

Parameters

• creating sub-structures from the main course structure that are called (e-courses), so that they fit the subject’s requirements for the course being taught according to university’s curriculum.

Framework

• filling some of the metadata and tags that are associated with each resource with respect to the course structure.

Pedagogical

2-2 Instructors Model

Pedagogical

Framework

Parameters

Students will log to their learning space through their privileges that are given for them by their instructors.

Framework

Parameters

• Students will see the courses that have been registered to, • respect for each instructor teaching the course. • adding and displaying additional resources with respect to their adaptability preferences of initial knowledge, learning objectives or learning style.

Pedagogical

2-3 Students Domain Model

Pedagogical

Framework

Parameters

Parameters

• A condition necessary for the application of a rule, • An action resulting from a rule. It can be the update of the learner model or the adaptation of the content and presentation.

Framework

The adaptation model describes how the adaptation of link and content is made and how the learner model is updated. Adaptation is done using information from the domain model, the learner model and the learner interaction. The adaptation operation is performed by the adaptation engine. The basic element used for adaptation is the rule that determines how the pages are constructed and how they are presented to the learner. A rule consists of two parts:

Pedagogical

3- Adaptation Model

Framework

Parameters

This model identifies the interaction between the user and the application, represent the interface part that understood by the user, and deal with him directly, is the dialogue window, and the interrelationship between the user and the system. And it allows the user to interact with the other models of the educational system adaptive (domain model, adaptive model, the user model). And use the interface all methods and means, and patterns of interactive dialogue provided by modern technology, such as graphical presentations, forms, sounds, text, menus, natural language, and others.

Pedagogical

4- interface Model:

Pedagogical

Framework

Parameters


Parameters of Abilities Variables In E-Learning Adaptive Systems

Parameters

Parameters

For example, more structured and less complex instruction (e.g., expository method) may be more beneficial for students with low intellectual ability, while less structured and more complex instruction (e.g., discovery method) may be better for students with high intellectual ability.

Framework

1- Intellectual Ability General intellectual ability consisting of various types of cognitive abilities (e.g., such as verbal ability, deductive and logical reasoning, and visual perception such as spatial relations).

Pedagogical

Adaptive learning can be based on many different abilities and aptitudes variables:

Framework

Parameters

Cognitive styles are characteristic modes of perceiving, remembering, thinking, problem solving, and decision making. Among many dimensions of cognitive style (e.g., field dependence versus field independence, reflectivity versus impulsivity, haptic versus visual, leveling versus sharpening, cognitive complexity versus simplicity, constricted versus flexible control, scanning, breadth of categorization, and tolerance of unrealistic experiences), field-dependent versus field independent and impulsive versus reflective styles have been considered to be most useful in adapting instruction.

Pedagogical

2- Cognitive Styles

Framework

Parameters

Learning style is the way a student prefers to learn. Learning style influences the effectiveness of learning. Some people may be fast learners while some may be slow, some may need to practice more problems while others may need just example. These preferences are in general called the learning styles of an individual. Majority of the research work carried out are based on the learning styles as these are the most dynamic and give the best results if catered to properly. Efforts to match instructional presentation and materials with the student’s preferences and needs have produced a number of learning styles.

Pedagogical

3- Learning Styles

Framework

Parameters

Prior achievement measures relate directly to the instructional task, they should therefore provide a more valid and reliable basis for determining adaptations than other aptitude variables. The value of prior knowledge in predicting the student’s achievement and needs of instructional supports has been demonstrated in many studies. Research findings have shown that the higher the level of prior achievement, the less the instructional support required to accomplish the given task.

Pedagogical

4- Prior Knowledge

Framework

Many studies have shown that students with high test anxiety performed poorly on tests in comparison to students with low test anxiety. The high anxiety interferes with the cognitive processes that control learning, procedures for reducing the anxiety level have been investigated.

Pedagogical

5- Anxiety

Parameters

Framework

Parameters

7- Self-Efficacy Self-efficacy is a student’s evaluation of his or her own ability to perform a given task. It influences people’s intellectual and social behaviors, including academic achievement. The student may maintain widely varying senses of self-efficacy, depending on the context.

Pedagogical

6- Achievement Motivation Motivation is an associative network of affectively toned personality characteristics such as self-perceived competence, locus of control, and anxiety. Thus, understanding and incorporating the interactive roles of motivation with cognitive process variables during instruction are important.

each student has different learning methods, teachers need to realize and recognize the value of the difference Human cognitive abilities by multiple intelligences theory is divided into nine areas:

Framework

Parameters

1) Verbal/Linguistic Intelligent 2) Logical/Mathematical Intelligent 3) Musical/Rhythmic Intelligent 4) Body/Kinesthetic Intelligent 5) Visual/Spatial Intelligent 6) Interpersonal Intelligent 7) Intrapersonal Intelligent 8) Naturalist Intelligence 9) Existential Intelligence. Considering all nine areas, it has been discovered that many have a different dominant intellectual parts. The most important thing is that all areas are stimulated to encourage development. In addition, some dominated areas can be used to help weaker parts.

Pedagogical

8- Multiple Intelligences

Framework

Parameters

One of the major research issues in the field of adaptive interfaces is the “adaptivity versus adaptability” debate. In adaptive systems, the locus of control lies with the system, whereas in adaptable systems the locus of control lies with the learner. Therefore, adaptable systems are also referred to as customizable systems. The more these four stages are controlled by the system, the more adaptive is the environment.

Pedagogical

9- Locus of Control

Examples For Adaptive-learning Environment

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Desktop project Main page screen view of UZWEBMAT system


Desktop project Screen shot of learner registration interface


Desktop project Screen shot of teacher registration interface


Desktop project Screen shot of teacher login interface


Desktop project Sample screen shot viewing learner registrations in teacher interface


Desktop project Screen shot of the interface encountered by the learner logging into the system


Desktop project Screen shot of the learner directed to the content


Desktop project Screen shot of the feedback given whilst being directed to the next LO after successfully completing the LO


Desktop project Screen shot of feedback given to learner directed to and the same LO of secondary learning style


Desktop project Screen shot of the interface reporting all the movements of any learner


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References

Akhras, F. N. (2004). From Modelling Teaching Strategies to Modelling Affordances. In Proceedings of the ITS 2004 Workshop on Modelling Human Teaching Tactics and Strategies, in conjunction with the 7th International Conference on Intelligent Tutoring Systems (pp. 61-70). Retrieved from (http://www.cenpra.gov.br/noticiaseeventos/its04workshop/akhras.pdf) Akhras, F. N., & Self, J. A. (2000). System intelligence in constructivist learning. International Journal of Artificial Intelligence in Education, 11(4), 344-376. Retrieved from http://www.iaied.org/pub/950/file/950_paper.pdf Akhras, F. N., & Self, J. A. (2002). Beyond intelligent tutoring systems: Situations, interactions, processes and affordances. Instructional Science,30(1), 1-30. DOI: 10.1023/A:1013544300305 Andersson, A. S., & Grönlund, Å. (2009). A conceptual framework for elearning in developing countries: A critical review of research challenges.The Electronic Journal of Information Systems in Developing Countries, 38. Retrieved from http://144.214.55.140/ojs2/index.php/ejisdc/article/view/564 ,

Azevedo, R. (2005a). Computer environments as metacognitive tools for enhancing learning. Educ. Psychol. ,40(4),193–197. DOI: 10.1207/s15326985ep4004_1 Azevedo, R. (2005b). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educ. Psychol., 40(4), 199–209. DOI: 10.1207/s15326985ep4004_2

Cheung, R., & Kazemian, H. B. (2011). An Adaptive Framework for Personalized E-Learning. In Networked Digital Technologies (pp. 292306). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-22185-9_25 Ciloglugil, B., & Inceoglu, M. M. (2012). User modeling for adaptive elearning systems. In Computational Science and Its Applications– ICCSA 2012 (pp. 550-561). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-31137-6_42 Du Boulay, B., & Luckin, R. (2001). Modelling human teaching tactics and strategies for tutoring systems. International Journal of Artificial Intelligence in Education, 12(3), 235-256. Retrieved from http://sro.sussex.ac.uk/1330/ Hamada, M., Nishikawa, K., & Brine, J. (2013). A study of a Learning style index to support an intelligent and adaptive learning systems. In Intelligent and Adaptive Educational-Learning Systems (pp. 109-132). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-30171-1_5

Kahraman, H. T., Sagiroglu, S., & Colak, İ. (2013). A novel model for web‐based adaptive educational hypermedia systems: SAHM (supervised adaptive hypermedia model). Computer Applications in Engineering Education, 21(1), 60-74. DOI: 10.1002/cae.20451 Karampiperis, P., & Sampson, D. (2005). Adaptive learning resources sequencing in educational hypermedia systems. Journal of Educational Technology & Society, 8(4), 128-147. Retrieved from http://www.jstor.org/stable/jeductechsoci.8.4.128 Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). Integration of recommendations and adaptive hypermedia into Java tutoring system. Computer Science and Information Systems, 8(1), 211224. DOI:10.2298/CSIS090608021K Lee, J. & Park, O. (2004). Adaptive instructional systems. In: D. H. Jonassen (ed.), Handbook of Research on Educational Communications and Technology (pp. 651–684), second edition. Mahwah: Lawrence Erlbaum Associates. Matar, N. (2014). Multi-Adaptive Learning Objects Repository Structure Towards Unified E-learning. International Arab Journal of e-Technology, 3(3). Retrieved from http://www.iajet.org/iajet_files/vol.3/no.3/1-_9590.pdf

Paramythis, A., & Loidl-Reisinger, S. (2003). Adaptive learning environments and e-learning standards. In Second european conference on e-learning (Vol. 1, pp. 369-379). Retrieved from http://www.ask4research.info/Uploads/Files/Citations/issue1art11-paramythis.pdf Park, O. C., & Lee, J. (2003). Adaptive instructional systems. Educational Technology Research and Development, 25, 651-684. Retrieved from http://www.aect.org/edtech/25.pdf Quintana, C., Zhang, M., & Krajcik, J. (2005). A framework for supporting metacognitive aspects of online inquiry through software-based scaffolding.Educational Psychologist, 40(4), 235244. DOI: 10.1207/s15326985ep4004_5 Rebolledo-Mendez, G., Du Boulay, B., & Luckin, R. (2006, June). Motivating the learner: an empirical evaluation. In Intelligent Tutoring Systems (pp. 545-554). Springer Berlin Heidelberg. DOI: 10.1007/11774303_54 Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261-290. Retrieved from http://content.iospress.com/articles/international-journal-ofartificial-intelligence-in-education/jai15-4-02.

Tadlaoui, M., Chikh, A., & Bouamrane, K. (2013). ALEM: A Reference Model for Educational Adaptive Web Applications. In Intelligent and Adaptive Educational-Learning Systems (pp. 25-48). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-30171-1_2 Wang, T. I., Wang, K. T., & Huang, Y. M. (2008). Using a style-based ant colony system for adaptive learning. Expert Systems with Applications, 34(4), 2449-2464. DOI: 10.1016/j.eswa.2007.04.014

Wolf, C. (2007).Construction of an Adaptive E-learning Environment to Address Learning Styles and an Investigation of the Effect of Media Choice (Doctoral dissertation). RMIT University .Retrieved from http://researchbank.rmit.edu.au/eserv/rmit:9804/Wolf.pdf Zimmerman, B. J., & Tsikalas, K. E. (2005). Can computer-based learning environments (CBLEs) be used as self-regulatory tools to enhance learning?.Educational Psychologist, 40(4), 267-271. DOI: 10.1207/s15326985ep4004_8

Thanks! Any questions? You can find me at: ◇ [email protected] ◇ [email protected]