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  • Slide 1
  • aDeNu Research Group http://adenu.ia.uned.es Adaptive accessible design as input for runtime personalization in standard- based eLearning scenarios Olga C. Santos, Jess G. Boticario ocsantos@dia.uned.esocsantos@dia.uned.es jgb@dia.uned.esjgb@dia.uned.es ADDW 2008 York, September 22-25
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  • 2 Technology is expected to attend the learning needs of students in a personalised and inclusive way following the lifelong learning paradigm But very ofen technology is inapropriate or introduced with insuficient support Further exclusion for people with disabilities EU4ALL (IST-2006-034778)
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  • ADDW 2008 York, September 22-25 3 Meaning of disability Learners experience a disability when there is a mismatch between the learners needs (or preferences) and the education or learning experience delivered ISO JTC1 SC36 Individualized Adaptanbility and Accessibility in eLearning, Education and Training
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  • ADDW 2008 York, September 22-25 4 Our research goal Improve the learning efficiency Task performance (speed) Course outcomes (results) User satisfaction
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  • ADDW 2008 York, September 22-25 5 Improving learning experiences Universal design Follow specifications Accessible contents W3C WAI WCAG Learning paths for different learning needs IMS-LD Contents metadata IEEE-LOM / IMS MD User characterization IMS-LIP, IMS-AccLIP, ISO PNP Device capabilities CC/PP Personalization AI techniques Knowledge extracted from users interactions Infer user features & preferences (user modelling) Help manage the collaboration Audit performance Context-awareness Recommender systems Design Runtime + EU4ALL (IST-2006-034778) = aLFanet (IST-2001-33288) + inclusion
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  • ADDW 2008 York, September 22-25 6 Outcomes from evaluations with users Carried out in ALPE project (eTEN-029328) Contents developed using the WCAG to suit end-users accessibility preferences Dynamic support would have improved the learning performance and increased the learners satisfaction
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  • ADDW 2008 York, September 22-25 7 The educational experience is holistic Provide accessible learning experiences The learning path that the student chooses to follow should be accessible while individual online components or learning objects may not. Rather than aiming to provide an e-learning resource which is accessible to everyone, resources should be tailored for the students particular needs Although the WCAG guidelines can be used to ensure that learning objects are accessible this may not always be desirable from a pedagogic standpoint.
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  • ADDW 2008 York, September 22-25 8 Dynamic support demanded on ALPE Need 1: Adapt the language used and offer glossaries that clarify terms (PREVIOUS KNOWLEDGE) if the difficulty level of a particular content is high and the user has not passed the evaluation of the associated learning objective recommend more detailed content and a glossary with complex terms from the text Need 2: Standing out what information is most important (INTEREST) if the semantic density of a content is high alert the user of its relevance Need 3: Suggest functionality from the browser (TECH. SUPPORT) If user low experienced in the usage of Internet and uses screen-reader suggest and explain how to access abbreviations and acronyms Need 4: Provide dynamic guide and embedded help (TECH. SUP.) If technology level is low and new to the platform Explain how to navigate in the platform, how to use their user agents and provide technical assistance
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  • ADDW 2008 York, September 22-25 9 Learning performance Factors Factors identified from brainstorming with psycho-pedagogical experts Motivation for performing the tasks Platform usage and technological support required Collaboration with the class mates Accessibility considerations when contributing Learning styles adaptations Previous knowledge assimilation
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  • ADDW 2008 York, September 22-25 10 Our research goals Improve the learning efficiency Task performance (speed) Course outcomes (results) User satisfaction by offering the most appropriate recommendation in each situation in the course get familiarized with the platform get used to the operative framework of the course carry out the course activities addressing the required factors
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  • ADDW 2008 York, September 22-25 11 Personalized content and service delivery Dynamic support in terms of recommendations which focus on the learning factors Covers the learning needs of the learners and the current context along the learning process Reduces the workload of the tutors Based on a standard-based user model (IMS-LIP/AccLIP) Demographic information Learning styles Technology level Collaboration level Interest level per learning objective Knowledge level per learning objective Accessibility preferences (display, control, selection) Past interactions
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  • ADDW 2008 York, September 22-25 12 The A2M recommendation model Objectives: 1.Support the course designer in describing recommendations in inclusive eLearning scenarios 2.Manage additional information to be given to the user to explain why the recommendation has been offered 3.Obtain meaningful feedback from the user to improve the recommender Aims: to be integrated in LMS with an accessible, usable and explicative GUI with generality in mind to be adapted to other domains if useful
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  • ADDW 2008 York, September 22-25 13 RECOMMENDATION TIMEOUT RESTRICTIONS TECHNIQUECATEGORY PREFS/CONTEXT ORIGIN EXPLANATION CONDITIONS offered fits in fulfills limited by applies belongs togenerated by has A model for Recommendations in LLL
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  • ADDW 2008 York, September 22-25 14 Factors Categories Motivation Learning styles Technical support Previous knowledge Collaboration Interest Accessibility Scrutability
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  • ADDW 2008 York, September 22-25 15 dynamic static Process Rec. types Recs. Human Expert Artificial Intelligence techiques context userdevicecourse Design time Runtime time = Rec. instances in the LMS USER (Learner/Tutor)
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  • ADDW 2008 York, September 22-25 16 Recommender User interface (page 1) If applicable, the recommendation is offered to the user in a usable and accessible user interface, together with a detailed explanation.
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  • ADDW 2008 York, September 22-25 17 Recommender User interface (page 2) Explanation page with additional information regarding the origin, category, technique and high level description Feedback requested from this page
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  • ADDW 2008 York, September 22-25 18 Small-scale experience Objective Get feedback of the recommendation model not to validate the generation of recommendations Settings Access to a course space in dotLRN LMS 13 static recommendations available Method 30 questions test Experience with eLearning platforms Recommender output Type of recommendations
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  • ADDW 2008 York, September 22-25 19 29 users from two summer courses 16 valid responses: 50% accessibility experts 20% people with disabilities 80% experience with web-based application for learning and teaching
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  • ADDW 2008 York, September 22-25 20 Experience with the platform Perception Very good: 18.75% Good: 75% Regular: 6.25 % Bad or very bad: 0% Compared to previous experiences Better: 70% Worst: 15% Not Answered: 15% Reasons: Positive opinions: WebCT was not friendly this one adjusts to my learning style this one presents an easier navigation this one is more accessible sections are clearly separated in this one Negative opinion: depends on the time spent to get used to the platform
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  • ADDW 2008 York, September 22-25 21 Recommender system output (I) All users were aware the RS None wanted to get rid of it Positive feedback: Very useful service: 56.25% Another service of the platform: 43.75% (it is a demand from the users!) Usage of icons A third of students (31.25%) had not paid attention to them For 2/3: Useful and clear: 56.25% Good idea but requiring a redesign: 12.5% Origin of recommendations Most liked to receive this info: 93.75% Preferred origins: recommended by the professor: 93.75% adapted to my preferences: 68.75% defined by the course design: 43.75% useful for my classmates: 43.75%
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  • ADDW 2008 York, September 22-25 22 Recommender system output (II) Additional information page Not accessed: 37.5% Useful: 62.50% Preferred information: Detailed explanation: 66% Category: 43.75% Origin: 31.25% Technique: 31.25% Categories No other category was identified. Relevance: Learning styles: 68.75% Previous knowledge: 62.50% Interest level: 56.25% Motivation: 43.75% Technical support: 31.25% Scrutability: 31.25% Accessibility: 31.25% Collaboration: 25%
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  • ADDW 2008 York, September 22-25 23 Feedback on the type of recommendations Learner point of view Types of recommendations selected for more that 60% of the users: Fill in a learning style questionnaire, so the system can be adapted to me Read some section of the help, if there is a service in the platform that I don't know Read a message in the forum that has information that may be relevant to me Read a file uploaded by the professor or a classmate Get alerts on deadlines to hand in an activity Types selected by less than 25% of users: Fill in a self-assessment question