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Learning paths and adaptive learning

Learning paths and adaptive learning

Kurzinformation

Learning paths are structured routes that guide students through a series of learning activities. Adaptive learning uses these learning paths by adapting content and activities to individual prior knowledge and preferences. This provides students with personalized support.

Adaptive learning in RWTHmoodle uses non-linear learning paths which, when implemented effectively, allow students to not only learn in a fixed order, but to adapt their learning paths according to their individual performance and preferences. This encourages greater motivation and learning autonomy as learners have more control over their progress and can access content that is relevant to them. RWTHmoodle provides a number of tools that can be used to implement learning paths:

  • Lesson: Different multimedia content (texts, photos, videos etc. ) can be linked to questions to create learning paths.
  • Activity completion: Definition of criteria for when activities are considered completed. In conjunction with various Moodle activities, it is possible to model cross-activity learning paths.
  • H5P Branching Scenario: Learners make decisions by answering questions and thereby influence the course of their learning path. Numerous interactive H5P elements can be combined.
  • H5P Game Map: Learning map with H5P content that must be answered in a specific order.

Detailinformation

  1. What types of learning paths are there?
  2. What tools are available in RWTHmoodle that can be used to create learning paths?
  3. Tools for learning paths in comparison
 

1. What types of learning paths are there?

 

Linear learning paths: Students go through a predefined sequence of learning activities and content in a fixed order, without diversions or alternatives:

Exemplary representation of a linear learning path with alternating content and question pages. The processing sequence is precisely specified, which is why the pages must be completed in the predefined order.

Non-linear learning paths: Students make decisions that influence the course of the learning path. Depending on the decisions they make, they can access different content or activities:

Exemplary representation of a non-linear learning path in which content and question pages alternate. Students can choose between different paths depending on their answers to the questions. Questions that are answered correctly or incorrectly lead to different content or further question pages, allowing the learning process to be customized.

 

2. What tools are available in RWTHmoodle that can be used to create learning paths?

2.1. Lesson

  • A lesson is a compilation of several pages that are linked to each other.
  • A page can contain text, images or videos or even interactive content consisting of quiz questions.
  • Students can either be guided linearly from one page to the next or they are given a choice of several subsequent pages.
  • This allows individual learning paths to be modeled based on the students' answers or, for example, additional content to be offered to those students who require additional support.
 

2.2. Activity Completion

  • In RWTHmoodle, Activity Completion and Access Restriction can be combined to create simple and complex learning paths.
  • This combination allows teachers to control access to specific activities within a course based on the progress of learners.

Illustration of an “Add prerequisite” screenshot of an example Moodle activity. Under “Activity completion”, another activity is selected, which must be completed in order to access the currently open activity. The second screenshot shows two activities on the course page. The prerequisite set in the first screenshot appears for the lower of the two activities. This means that in order to access this activity, the displayed activity completion must be fulfilled.

 

2.3. H5P Branching Scenario

  • The Branching Scenario is a comparatively complex H5P content type in which students can choose from several options and thus decide what is shown to them next.
  • The Branching Scenario can be used to create interactive stories and simulate decision-making situations. In addition, learning paths can be designed for learners to choose from.
  • The Branching Scenario is also particularly suitable for digital edu breakout rooms, as it allows students to explore different decision-making paths and experience the consequences of their actions directly. In such scenarios, students can progress step-by-step by solving puzzles or making decisions, with different paths leading to different ends. This encourages critical thinking and problem-solving skills to overcome the challenges set and escape from the digital escape room.
 

2.4. H5P Game Map

  • Various H5P content types and their processing sequence are visualized on a map (map, game plan, etc.).
  • More than 20 different H5P content types can be integrated into the locations of the game map (including Interactive Video, Course Presentation, Drag and Drop, Multiple Choice, etc.).
  • A game map offers a wide range of possible uses and combinations of H5P content types and can be used in particular to map learning paths.

Exemplary representation of a learning map with different countries. A stage has been placed in a total of seven countries. An H5P content type is stored behind each stage. The stages are connected to each other, with one country forming the starting point. Three other stages can be reached from the starting point. The order in which the stages are accessed is not fixed; stages that are further away cannot yet be accessed, but can be accessed as soon as the immediately adjacent stage has been successfully completed.
 

3. Tools for learning paths in comparison

 

Feature

LessonActivity completionH5P Branching ScenarioH5P Game Map
Linear and non-linear learning paths possiblecriterion fulfilledcriterion fulfilledcriterion fulfilledcriterion fulfilled
Unterbrechnung und Wiederaufnahme möglichcriterion fulfilledcriterion fulfilledcriterion not fulfilledcriterion not fulfilled
Cross-activity learning paths possiblecriterion not fulfilledcriterion fulfilledcriterion not fulfilledcriterion not fulfilled
Visually attractive designcriterion not fulfilledcriterion not fulfilledcriterion fulfilledcriterion fulfilled
Erstellungsaufwandhigh complexitymedium complexityhigh complexitylower complexity
 

  Zusatzinformation

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last changed on 10/02/2024

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