Login / Signup

Practical independent research projects in science: a synthesis and evaluation of the evidence of impact on high school students.

Judith M BennettLynda DunlopKerry J KnoxMichael J ReissRebecca Torrance Jenkins
Published in: International journal of science education (2018)
Practical independent research projects (IRPs) are a feature of school science in a number of countries. To assess the impact of IRPs on students, a systematic review of the literature was undertaken. Thirty-nine papers met the review inclusion criteria, reporting on work from twelve countries. The review indicates that IRPs are often associated with wider initiatives such as authentic science, problem-based learning, and project-based learning. There is considerable variability in the nature of IRP work in relation to focus, models of provision, assessment, the involvement of external partners such as universities and employers, and funding, and this diversity affects judgements on the quality of the evidence base on impact. The majority of the research reviewed explored areas such as conceptual understanding, motivation to study science once it is no longer compulsory and attitudes to science, and the development of practical skills. Benefits were identified in relation to the learning of science ideas, affective responses to science, views of pursuing careers involving science, and development of a range of skills. Studies focusing on traditionally under-represented groups indicated that such students felt more positive about science as a result of undertaking IRPs. The review findings indicate that further work is needed to enhance the quality of the available evidence, to consider the ways in which IRPs can be validly assessed, to explore more fully the potential benefits for traditionally under-represented groups, and to explore more fully the potential longer-term benefits of participation in IRPs at high school level.
Keyphrases
  • public health
  • high school
  • quality improvement
  • physical activity
  • mental health
  • machine learning
  • risk assessment
  • hepatitis c virus