Powered by Felder-Silverman Learning Model

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This captures how learners prefer to process, perceive, receive, and understand new information. Use the official survey mode to gather accurate styles in minutes, then adapt onboarding, coaching, and AI copilots without abandoning your current design language.

Please use this diagnostic tool as a reference for exploring learning preferences. Learning styles are not fixed, and we recommend trying various learning methods. Use this as a starting point for diversifying learning strategies, not limiting personal learning abilities.

Created for educators and learners

Trusted by 7+ organizations and 10,000+ students

Universities, bootcamps, and independent learning communities profile preferences before onboarding and tailored tutoring.

Seoul National University
Handong Global University
Gyeonggi University of Science and Technology
Sookmyung Women's University
University of Seoul
Geongsang National University
Jeonbuk National University
Seoul National University
Handong Global University
Gyeonggi University of Science and Technology
Sookmyung Women's University
University of Seoul
Geongsang National University
Jeonbuk National University
Seoul National University
Handong Global University
Gyeonggi University of Science and Technology
Sookmyung Women's University
University of Seoul
Geongsang National University
Jeonbuk National University

Scientific Approach to Learning Styles

Balanced view of learning science and personalization

FSLSM gives language for learner preferences, while modern educational research reminds us to combine those insights with high-impact strategies and adaptive technology.

Scientific Approach to Learning Styles

Felder-Silverman Learning Style Model

FSLSM, developed in 1988 by Richard Felder and Linda Silverman, classifies learner preferences across four categories. The instrument is used ~100,000 times a year and has been validated repeatedly. [Litzinger et al., 2007; Zywno, 2003]

Balanced Scientific Perspective

Evidence cautions against rigid matching

Meta-analyses show limited gains when instruction is matched strictly to learning style (effect size d = 0.04). Offering varied modalities acknowledges differences without reinforcing stereotypes. [Pashler et al., 2008; Willingham et al., 2015]

AI-Based Adaptive Learning

Modern systems focus on performance

Adaptive platforms now personalize based on demonstrated mastery and behavioral data, not just style preferences. This leads to higher persistence and better outcomes. [Carnegie Learning, 2024; Coursera, 2024]

Evidence-Based Learning Strategies

Pair FSLSM insights with proven methods

Techniques such as spaced repetition (d = 0.7), interleaving (d = 0.5), and elaborative interrogation outperform pure style matching, so this platform bakes them into every plan. [Dunlosky et al., 2013; Rohrer & Pashler, 2010]

Introduction

FSLSM Survey Steps

The FSLSM site walks respondents through four simple steps. Keep the wording familiar so visitors feel confident they are using the authentic questionnaire.

Tip

Include a printable scoring sheet or link to the reference PDF so instructors can tally results offline.

1

Access through online or print

FSLSM is available as a digital form or printable PDF.

2

Answer 44 items in total

Each prompt lists two statements. Select the option that best reflects how you usually learn, even if both feel partly true.

3

Score every category

Group answers into the four categories to calculate preference values ranging from -11 to +11.

4

Interpret intensity and plan

Use the magnitude to classify preference strength. Pair the results with tutoring plans, course design, or AI prompts.

Ways to use the results

Learner self-awareness

Equip learners with terminology for their preferences so they can ask for matching study strategies and balanced instruction.

Course and curriculum design

Audit lessons for a healthy mix of experimentation, reflection, factual grounding, and conceptual exploration.

Instructional coaching

Brief tutors before sessions so they can switch between visuals, dialogue, sequential steps, or global context on demand.

Assessment planning

Offer multiple demonstration formats - labs, diagrams, essays - so learners can show mastery in their preferred mode.

AI tutoring copilots

Feed FSLSM scores into prompts so copilots know when to lead with visuals, exercises, or reflections.

Program analytics

Track aggregate preference trends to decide which materials or facilitator skills to reinforce.

Ready to be tailored?

Plug the survey into your tutoring stack with no pain.