Leisa Taylor is an educational advisor who specialises in the support of gifted and twice-exceptional students (2e). She has been involved in the field of education for the past 15 years. Leisa uses interventions that are based on evidence from the fields of education and psychology.

Having taught a wide range of subjects and developed curriculum at school and regional levels, Leisa has broad experience in education. Most recently, she has worked in ESL/learning support contexts. Leisa has experience in devising educational support programs for gifted and 2e students.

Education advisory role

In 2018, Leisa developed Taylored Solutions in response to her experiences working with gifted children in schools. She offers a range of options and skill development workshops to gifted and 2e children. A niche area of the service is providing guidance on curriculum planning for home-schooled children. Leisa hopes to develop online courses and face-to-face workshops in the near future.

Personal background

As the parent of three gifted children, Leisa appreciates the complexity that these children bring to life. She is a passionate advocate of equity in education. Leisa supports adaptive teaching methods that take student’s abilities, strengths and interests into account. She is currently researching the impact of negative school experiences on children and parents. Leisa undergoes regular professional development and is a member of SPELD Qld, SENG and QAGTC. She is the current secretary of the QAGTC West Branch.

Qualifications:

  • Bachelor of Education (Secondary) – biology major.
  • Graduate Diploma in Humanities
  • Graduate Certificate in Gifted Education
  • Graduate Diploma in Psychology (2019)

Areas of skill and understanding:

  • the literacy continuum
  • assistive technology
  • ASD in girls
  • Dyslexia, Dyscalculia and Dysgraphia
  • working memory
  • processing speed difficulties
  • extension for gifted students
  • programming for twice-exceptional students
  • home-schooling requirements
  • project based learning models