Adaptive learning paths for improving Lifelong Learning experiences

1. Adaptive learning paths for improving Lifelong Learning experiences Ana Elena Guerrero Roldán, Julià Minguillón Universitat Oberta de Catalunya (UOC) Manchester, 2007

2. Contents Motivation and goals Open University of Catalonia environment Ateneu courses and Data Mining subject Adaptive learning paths Conclusions Current and future research Manchester, 2007

3. Motivation and Goals Motivation: To improve lifelong learning experiences in UOC’s virtual learning environment, focusing on competences Main goal: To design adaptive learning paths for developing competences using a case study (Data Mining) Specific goals: • To identify the main competences of a Data Mining course, and recognize the student professional experience • To design adaptive learning paths for improving lifelong learning experiences, integrating them into the official courses • To evolve towards a competence based learning design Manchester, 2007

4. Open University of Catalonia • Virtual university was created in 1995 with a user-centered pedagogical model • 19 official degrees, masters, Ph.D and Ateneu courses • 35.000 students with different profile than traditional university students • Under a major evolution process: • Adaptation to the Bologna process • Towards a lifelong learning model • Integration of e-learning standards (SCORM, IMS LD) Manchester, 2007

5. Ateneu courses • Our lifelong learning scenario is called Ateneu courses • Courses are selected from subjects offered as part of the official degrees • They are oriented towards lifelong learning students who want to improve their knowledge and professional competences • Virtual classrooms (and learning environment) are the same for all students (official and Ateneu) • Students enroll into Ateneu courses without any university access prerequisite → large diversity of student profiles Manchester, 2007

6. Data Mining course (I) • Data mining is one of Ateneu courses, such as history, languages or economics (Lifelong Learning courses) • Available as an optional course in the Computer Science degree and free choice in other degree programs, even for other universities → diversity of student profiles • Students choose this subject for the real possibilities of immediately applying the acquired competences • 28% of students wanted to improve in the exercise of their professions Manchester, 2007

7. Data Mining course(II) • The course aims to provide an introduction to the basic principles, methods, and applications of data mining • It combines the application of previous knowledge with new concepts and techniques from multiple fields: Statistics, Databases, Artificial Intelligence, … • Students learn to identify a problem and to address it with a complete analysis using a data mining process • Students apply competences learned during the course in a final practice following a case of study, adopting several roles Manchester, 2007

8. Our proposal • To identify which are the main competences (goals) to enable students with professional skills and abilities • To identify the competences required for data mining course • To design adaptive learning paths for improving the learning process, integrating the different user profiles • To provide lifelong learning scenarios with adaptive itineraries at the Open University of Catalonia • To study the possibilities of the IMS-LD standard for describing such proposal Manchester, 2007

9. Data mining learning process • All data mining students have to: • acquire some basic competences • study several didactical materials (M1, M2, M3) • do some learning exercises (E1, E2, E3) • do some evaluation activities (A1, A2, A3) • But usually…. • Ateneu students (among others) need also reinforced didactical materials (M1’, M2’) and also reinforced exercises (E1’, E2’) • Students’ previous knowledge is not the same for some topics for all students, depending on their background • Some topics are not required but they may be very useful for some students (i.e. Java for Computer Science) • In order to improve the learning process, adaptive learning paths could be designed as follows Manchester, 2007

10. Example:Adaptive learning path E1’ E2’ M1 E1 A1 M2 E2 A2 M3 E3 A3 M1’ M2’ M1-3: Didactical materials E1-3: Exercises A1-3: Evaluation activities Reinforced theory learning path: M1’, M2’ Reinforced exercises: E1’ and E2’ Manchester, 2007

11. Learning design? • It stores more information than simple learning objects, including competences and activities • Learning environment, roles, activities, methods and all the relationships need to be defined • Complex scenarios such as the Data Mining subject case can be described • Adaptive learning paths can be created Manchester, 2007

12. Conclusions • European universities need to include competences in their degrees and curricula • The Bologna process may improve subjects related to professional scenarios such as Data Mining • Subject competences have to be defined as a part of the learner’s profile and must be incorporated into a lifelong learning curricula • A flexible and adaptive learning process needs to be designed: personalization, learning paths, … • Activity-oriented formative itineraries can be built by means of combining required and acquired competences • IMS-LD is a first step towards describing the learning process, but it needs further study Manchester, 2007

13. Current and future research • To identify all the elements involved in the learning process in the UOC virtual campus, according to the IMS-LD standard • To create professional formative itineraries based on the evaluation of acquired competences in an truly open lifelong learning scenario • To offer more flexibility and personalization in the learning process, improving user experience • To implement a pilot course part of the Ateneu courses using IMS-LD Manchester, 2007

14. Thank you! Ana Elena Guerrero aguerreror@uoc.edu Julià Minguillón jminguillona@uoc.edu Manchester, 2007