Felder-Silverman Learning Styles Model and Lithuanian Intelligent Future School Project for STEM Education

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“One size fits all” approach doesn’t longer work in education. We strongly believe that future school means personalization plus intelligence. It’s the essence of Lithuanian Intelligent Future School (IFS) project organised by Vilnius University in collaboration with 40 Lithuanian comprehensive schools. IFS project is aimed mainly at improving the quality and effectiveness of STEM education.

According to IFS concept, learning personalization means creating and implementing personalized learning scenarios based on recommender systems suitable for particular learners according to their personal needs. Educational intelligence means application of intelligent technologies and methods enabling personalized learning to improve learning quality and efficiency.

In personalized learning, first of all, integrated learner profiles (models) should be implemented. After that, ontologies-based personalized recommender systems should be created to suggest learning components (learning objects, activities, methods, tools, apps etc.) suitable to particular learners according to their profiles. Thus, personalised learning scenarios could be created for particular learners for each STEM topic according to curriculum. A number of intelligent technologies should be applied to implement this approach, e.g. ontologies, recommender systems, intelligent agents, multiple criteria decision making models, methods and tools etc. to evaluate quality and suitability of the learning components etc.

This IFS approach could be implemented by the following steps:

  • Creating learners’ models (profiles) based on their learning styles and other particular needs such as cognitive traits (working memory capacity, inductive reasoning ability, and associative learning skills)
  • Interconnecting learners’ models with relevant learning components (learning content, methods, activities, tools, apps etc.)
  • Creating corresponding ontologies and recommended systems
  • Creating and implementing personalized learning scenarios for STEM education
  • Creating educational multiple criteria decision making models and methods to evaluate the quality and suitability of these learning scenarios for students’ personal needs

Learning styles are the main component of students’ learner profiles (models). Learning style designates everything that is characteristic to an individual when she/he is learning, i.e. a specific manner of approaching a learning task, the learning strategies activated in order to fulfill the task. Learning styles represent a combination of characteristic cognitive, affective and psychological factors that serve as relatively stable indicators of how a learner perceives, interacts with, and responds to the learning environment. Learning styles model systems differ in several aspects: underlying learning style model, diagnosing method (implicit or explicit), modelling techniques (rule-based approach, data mining, machine learning techniques), number of modeled student characteristics besides learning preferences (knowledge level, goals) and the type, size and conclusions of the reported experiments.

Ontologies and recommender systems should be based first of all on established interconnections between students’ learning styles and aforementioned learning components. While establishing those interconnections, high-quality learning styles models and vocabularies of learning components should be used, on the one hand, and experienced high-quality experts should participate in this work, on the other.

The first step to implement IFS is identification of students’ learning styles. Felder-Silverman learning styles model (FSLSM) is used for this purpose. FSLSM is known as the most suitable for STEM and particularly for engineering education. Students learn in many ways – by seeing and hearing; reflecting and acting; reasoning logically and intuitively; memorizing and visualizing and drawing analogies and building mathematical models; steadily and in fits and starts. Teaching method also vary. FSLSM classifies students according to where they fit on a number of scales pertaining to the ways they receive and process information, i.e.:

INFORMATION TYPE: Sensory (concrete, practical, oriented towards facts and procedures) and Intuitive (conceptual, innovative, oriented towards facts and meaning);

SENSORY CHANNEL: Visual (prefer visual representations of presented material – pictures, diagrams, flow charts) and Verbal (prefer written and spoken explanations);

INFORMATION PROCESSING: Active (learn by trying things out, working with others) and Reflective (learn by thinking things through, working alone);

UNDERSTANDING: Sequential (linear, orderly, learn in small incremental steps) and Global (holistic, systems thinkers, learn in large leaps).

Felder and Silverman propose that a student’s learning style may be defined by the answers to the following questions:

  1. What type of information does the student preferentially perceive: sensory (external) – sights, sounds, physical sensations, or intuitively (internal) – possibilities, insights, hunches?
  2. Through which sensory channel is external information most effectively perceived: visual – pictures, diagrams, graphs, demonstrations, or auditory – through words or sounds?
  3. How does the student prefer to process information: actively – through engagement in physical activity or discussion, or reflectively – through introspection
  4. How does the student progress toward understanding: sequentially – in continual steps, or globally – in large jumps, holistically?

Currently, FSLSM-based Felder-Soloman questionnaire http://www.engr.ncsu.edu/learningstyles/ilsweb.html is under localisation to be used in IFS schools. After translating this tool to Lithuanian, piloting activities will be organised in IFS schools in order to analyse whether it works well in Lithuanian learning context.

After that, the following IFS implementation step will be organised, e.g. interconnection of learners’ models with relevant learning components (learning content, methods, activities, tools, apps etc.).

Article written by: Virginija Birenienne, Scientix Ambassador

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