1 / 9

Dependency Matrix and Duration in Time Based Composition of Learning Paths

Dependency Matrix and Duration in Time Based Composition of Learning Paths. Ayman Moghnieh, Fabien Girardin, Joseph Blat Department of Technologies, Pompeu Fabra University, Barcelona, Spain. Planning Learning Paths for L.L.L. Environment

decker
Download Presentation

Dependency Matrix and Duration in Time Based Composition of Learning Paths

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dependency Matrix and Duration in Time Based Composition of Learning Paths Ayman Moghnieh, Fabien Girardin, Joseph Blat Department of Technologies, Pompeu Fabra University, Barcelona, Spain

  2. Planning Learning Paths for L.L.L. • Environment • Digital, Collaborative, Asynchronous, Social, Personalized, Interest-Based.... • Challenges • Immersiveness, Learner Support, Sustainability, Usability, communication...

  3. Context of work, an introduction • Contemporary challenges in L.L.L. • Curriculum planning and development • Learner’s learning path composition • Contingency planning in competence development • Providing tools for learner support • Simple approach suggested • Based on R.D.M. • Addresses a basic scenario • Provides an infrastructure for automation

  4. Basic Scenario • A professional planning to study virtual sets production technologies • Effort: average of 6 h/week • Curriculum: • Small set of interdependent CDPs • Accredited • Tasks: • Explore learner’s possible choices • Partially automate process planning

  5. Computing Dependency Matrix • Each CDP represented as a vector of competences • prerequisite competences define a transitive relation • CDPs can be sorted by dependency relations • RDM can also encompass the learning goal and the learner’s current position

  6. Path existance • Path Existance • CDPs with no prerequisite competences offer a set of competences C0  C /  c  C0, A0(c) = 1. Starting points • A1 = step(A0) represents the set of CDPs accessible from A0 • Path(A0, A1) = true if CR1 , CR1 = { c  C0 / A1(c) = -1}. • a goal is attainable by RCDP if  A0  / Path(A0, G) = true.

  7. Path segmentation and length • Path Steps and Segmentation • Any segmentation of Path(A0, G) is a composition of these unitary steps: A0  A1  A2 ……. An  G • Segments are more or less homogeneous in time and respect dependency relations among CDPs. • Path length • LENGTH( Path(A0, G) ) = LENGTH( Path(Ai, Aj) ) = TIME(Ai)

  8. Pre-planning: identifying choices

  9. Conclusions • Two main factors govern the planning process • Time restrictions / requirements • Competence dependency and prerequisites • This work forms a foundation for learner support in learning path planning • Visual aspects and interaction are key for success of the planning process

More Related