Presentation on theme: "Personalized and adaptive eLearning Applications in LSMs Phạm Quang Dũng Dept. of Computer Science."— Presentation transcript:
Personalized and adaptive eLearning Applications in LSMs Phạm Quang Dũng Dept. of Computer Science
Content Main issues of personalized and adaptive eLearning Learning customization and web services approach Development and design of adaptive learning content Student modelling Tailoring learning materials to the individual learning styles
3 Problem Every learner has individual characteristics: learning preference, self-efficacy, knowledge, goal, experience, interest, background, etc. How to enhance learning process effectively? Solution: personalized and adaptive learning Adaptive system tunes learning material & teaching method to learner model
4 Learner model Learner profile: contains personal information without inferring or interpreting. Learner model: description of learner’s properties –has a higher level than profile, expresses abstract overview of learner Learner modelling
Learning objects any digital resource that can be reused to support learning (D.A. Wiley, 2000) –digital images or photos, video or audio snippets, small bits of text, animations, a web page Characteristics –Share and reuse –Digital –Metadata-tagged Description information: title, author, format, content description, instructional function –Instructional and Target-Oriented
Main issues of personalized and adaptive learning The personalization is a function able to adapt the eLearning content and services to the user profile. It include: - how to find and filter the learning materials that fit the user preferences, needs, background, learning style, etc.; - how to present them; - how to customize the learning process i.e. deliver just the right material to the learner on Demand and Just in Time; - how to give user tools to reconfigure the system; - how to construct effective user model and tracking of its continuous changes, etc.
Main issues of personalized and adaptive learning Types of personalization: - Personalization of the learning context, based on the learner’s preferences, background, experience, learning style, etc. - Personalization of the presentation manner and form of the leaning content (for example, adaptive learning sequences of learning objects); - Full personalization, which is a combination of the previous two types. Adaptive learning means the capability to modify the learning content and/or any individual student’s learning experience as a function of information obtained through its performance and progress on situated tasks or assessments.
Main issues of personalized and adaptive learning Personalization in current LMSs includes: -Editable user profile; -Changeable graphics design of the learning material; -Personal calendar tracking learning progress events; -Access to learning objects conditioned on part of the personal data including achievements, experience, preferences, etc. (rarely); -Information about the learner behaviour during the learning process and the system’s reactions – personalized instructional flows, adaptive learning content, etc. (rarely); -Presentation manner and form of the learning content according to learner’s style (rarely), etc.
Learning customization and web services approach Wlliam Blackmon and Daniel Rehak define the following ways for learning customization: -At random – repeat random selection of learning objects; -By profile – choose the course/content based on the learner’s profile (role, skills, learning style, etc.); -By discovery – for given learning objective, find a learning object that best meets the learning objective given the learning’s current skill set, learning platform, learning style, language preference, etc.; -By response – choose the next learning activity based on the learner’s responses to questions.
Learning customization and web services approach Wlliam Blackmon and Daniel Rehak offer a web-services-based methodology for customization by profile in particular a methodology for eliminating learning objects (LOs) from the course because either: - the learner’s current role does not require the learning objective taught by the LO, or - the learner’s profile indicates that the learner has already achieved the objective taught by the LO. The learning content and data used for customization are presented in a set of standards-based data models.
Learning customization and web services approach The overall web-services architecture for learning is divided into layered services. The layers from top to bottom are: - User agents - provide interface between users and the learning services and major element of LMS – authoring of content, management of learning, content delivery, etc.; - Learning services – they are collection of data models and independent behaviours. They are grouped into logical collections - tool layer – provide public interface to the learning tools (simulators, assessment engines, collaboration tools, registration tools, etc.) - common application layer (sequencing, managing learner profiles, content management, competency management, etc.) - basic services layer – core features and functionality that are not specific for the learning (storage, management, workflow, right management, query/data interfaces, etc.)
Learning customization and web services approach
Development and design of adaptive learning content Adaptive learning content can be defined as a relevant sequence of learning objects (LOs), each of them associated with learning activity that fulfill given learning objective. The flows of learning activities can be described by rules and actions that specify: - the relative order in which LOs have to be presented, and - the conditions under which a pieces of content have to be selected, delivered or skipped during sequence presentation according to the outcomes of learner’s interactions with content.
Development and design of adaptive learning content The process of defining a specific sequence of learning activities begins with the creation of a learning strategy for the achievement of the determined pedagogical aim/s. Learning strategy specifies: - types of learning activities; - their logical organization; - the prerequisites, and - expected results for each activities. IMS Simple Sequencing Specification and the SCORM standard allow the learning strategies to be translated into sequencing rules and actions based on learner progress and performance.
Student modelling The student model enables the system to: provide individualized course content and study guidance; suggest optimal learning objectives; determine students’ profiles and their actual knowledge; dynamically assemble courses based on individual training needs and learning styles; join a teacher for guidance, help and motivation, etc.
Student modelling - standards Incorporation between IEEE LTSC’s Personal and Private Information (PAPI) Standard and the IMS Learner Information Package (LIP)
Student modelling The Self e-Learning Networks Project (SeLeNe) is a one-year Accompanying Measure funded by EU FP5, running from 1st November 2002 to 31st October 2003, extended until 31st January 2004 SeLeNe learner profile
Problems with collaborative student modelling that use a questionnaire Uncertainty because of: –a lack of students’ motivation –a lack of self-awareness about their learning preferences –the influence of expectations from others Questionnaires are static and describe the learning style of a student at a specific point of time –The result depends much on students’ mood
Benefits of using automatic student modeling does not require additional effort from students is free of uncertainty can be more fault-tolerant due to information gathering over a longer period of time can recognise and update the change of students’ learning preferences
Automatic student modelling approaches data-driven vs. literature-based
Automatic student modelling The data-driven approach uses sample data in order to build a model for identifying learning styles from the behaviour of learners aims at building a model that imitates the ILS questionnaire Advantage: the model can be very accurate due to the use of real data Disadvantage: the approach strictly depends on the available data and is developed for specific systems
Automatic student modeling The literature-based approach uses the behaviour of students in order to get hints about their learning style preferences then applies a rule-based method to calculate LSs from the number of matching hints Advantage: generic and applicable for data gathered from any course Disadvantage: might have problems in estimating the importance of the different hints
Tailoring learning materials to the individual learning styles Keyword-based search of LOs Learner Filtering Ranking Presentation result LOs of the User profile (individual learning style) Personalized learner’s view of the LO information space Learner’s preferences help to the system to recommend individualized LOs or categories of LOs. Personalized LO browsing process according to: