From one school year to the next, students experience an escalation in the amount and difficulty of schoolwork.
Researchers have tried to identify instructional approaches which would reduce the cognitive burden on students, especially when they are in the early stages of learning—such as when they start a new academic year, a new subject, a new topic, etc. (Martin & Evans, 2018).
Cognitive load theory (CLT) has outlined major tenets of instruction that can help manage the cognitive burden on students as they learn (Sweller, 2012). Drawing on key ideas under CLT, a recent practice-oriented instructional framework was developed—referred to as “load reduction instruction” (LRI; Martin, 2016).
LRI is a pedagogical approach seeking to balance explicit instruction with independent learning as appropriate to the learner’s level of knowledge and skill. Through this balance, the cognitive load on students is eased as they learn.
LRI has been examined in STEM classrooms, with results showing it is associated with positive academic outcomes in mathematics (Martin & Evans, 2018) and in science (Martin et al., 2020). In a new study published in Contemporary Educational Psychology (Martin et al., 2023), we expanded this research to the non-STEM domain by investigating LRI in English classrooms.
What is load reduction instruction (LRI)?
LRI’s principles have been developed to accommodate students’ working memory and long-term memory (Martin & Evans, 2018). Working memory is a space for information that students are consciously and currently aware of, and where they focus their present attention (Baddeley, 2012). Working memory is very limited in duration and capacity—e.g., a retention of around three to five items (Cowan, 2010). In contrast, long-term memory has substantial duration and capacity. Long-term memory is where information is encoded so it can be retrieved later (Baddeley, 2012).
Learning is said to occur when information is moved from working memory and encoded in long-term memory (Sweller, 2012), for later retrieval and use.
If students’ working memory is over-burdened, they are at risk of misunderstanding the content, falling behind in the lesson, or learning only part of the necessary knowledge or skill. Given this, researchers have suggested that explicit instruction should be applied in the early stages of learning to reduce the cognitive burden on students when they are novices (Mayer, 2004). Then, as students develop the necessary knowledge and skill, they move to more independent learning (Kalyuga, 2007). LRI adopts these guidelines to comprise the following five principles (see Figure 1):
- Principle #1: Difficulty reduction in the initial stages of learning, as appropriate to the learner’s level of prior knowledge and skill
- Principle #2: Support and scaffolding
- Principle #3: Structured practice
- Principle #4: Feedback-feedforward, combining corrective information with specific improvement-oriented guidance
- Principle #5: Guided independent application
Figure 1. Load Reduction Instruction (LRI) Framework – adapted with permission from Martin (2016).
Extending LRI to English
There is a reason for the early focus on maths for LRI research. Maths is taught in a highly sequenced way, where each task escalates in difficulty. That particular set of attributes was considered a good initial test for the sequenced and scaffolded instructional approaches for which LRI argues.
Following “proof of concept” in mathematics (Martin & Evans, 2018), the focus moved to science because it was considered a highly challenging (cognitively burdensome) subject for many students and also amenable to sequenced linear, structured, and scaffolded instruction—such as LRI (Martin et al., 2020). Both sets of studies confirmed the five principles of LRI in mathematics and science and significant links between LRI and students’ motivation, engagement, and achievement.
These were promising findings so researchers sought to explore LRI in non-STEM subjects — especially in subjects where challenging tasks can be less well-defined and relatively more unstructured, such as in English, where it can be harder to sequence an escalation in difficulty, than in mathematics, for example. In our new study we explored LRI in English (and mathematics).
Our Study Methods
Participants were 1,773 high school students and their teachers in 94 English and 93 mathematics classrooms. Students were in years seven to 10, with an average age of 14 years. Nearly 60 per cent of the cohort were boys. Just over 60 per cent of the schools were single sex, all in independent schools in NSW. In both English and mathematics, women comprised just over 60 per cent of the teachers. Average years of experience for English teachers was 13 years and for mathematics teachers was 15 years. In English and mathematics classrooms, we administered: the Load Reduction Instruction Scale – Short (LRIS-S; Martin et al., 2020) to students and teachers (a survey tool capturing the five LRI principles in a classroom); a measure of students’ prior learning; students’ effort by way of the Effort Scale – Short (Nagy et al., 2022); and students’ achievement in each subject via an achievement test.
What Did We Find?
We found that student- and teacher-reports of LRI practices were associated with greater student effort and achievement in English and in mathematics. The findings extend prior research in STEM subjects by showing there are also academic benefits in English when load reduction instruction occurs. As described earlier, students with low prior learning need more help to ease cognitive load (Sweller, 2012) and our study confirmed this in both English and mathematics, with teachers mainly doing so via Principle #1 (difficulty reduction).
Concluding Thoughts
For decades there has been some tension between predominantly explicit instructional approaches and predominantly constructivist approaches (Tobias & Duffy, 2009).
Our findings suggest that framing the two as mutually exclusive may impede student learning. Under LRI, both are compatible, including in English and mathematics: after reducing the burden on working memory via explicit approaches, teachers can encourage students to apply that knowledge and skill in more independent ways as appropriate to their students’ levels of competence (see also Kalyuga, 2007). Taken together, our study provides a more comprehensive perspective on LRI as relevant to the subjects and classrooms within which instruction and learning take place.
Andrew Martin, PhD, is Scientia Professor, Professor of Educational Psychology, and Co-Chair of the Educational Psychology Research Group in the School of Education at the University of New South Wales, Australia. He specialises in student motivation, engagement, achievement, and quantitative research methods.
Paul Ginns is Associate Professor of Educational Psychology in the School of Education and Social Work at the University of Sydney. Paul uses numerous research methodologies (for example, experimental and survey-based research) and analytic methods, including general linear models, exploratory and confirmatory factor analysis, structural modelling and meta-analysis, to investigate student learning.
Robin Nagy is a PhD candidate in Educational Psychology at the University of NSW. His PhD focuses on high-school students’ academic effort. Robin has over 25 years’ experience as a teacher and in school leadership, having taught in the UK, Thailand and Australia, and as a Professional Learning Consultant for the Mathematical Association of NSW.
Rebecca Collie, Ph.D., is Scientia Associate Professor in Educational and Developmental Psychology at the University of NSW. Her research interests focus on motivation and well-being among students and teachers, psychosocial experiences at school, and quantitative research methods.
Keiko Bostwick, PhD, is a Research Officer in the School of Education at the University of New South Wales, Australia. She specialises in student motivation, teacher and classroom effects, and quantitative research methods.
Paul Ginns is Associate Professor of Educational Psychology in the School of Education and Social Work at the University of Sydney. Paul uses numerous research methodologies (for example, experimental and survey-based research) and analytic methods, including general linear models, exploratory and confirmatory factor analysis, structural modelling and meta-analysis, to investigate student learning. 
Professor Andrew Martin, PhD, is Scientia Professor, Professor of Educational Psychology, and Co-Chair of the Educational Psychology Research Group in the School of Education at the University of New South Wales, Australia. He specialises in student motivation, engagement, achievement, and quantitative research methods.