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A major research project is unearthing new insights into the interrelationship between teaching approaches, student motivation and engagement, biopsychology and educational outcomes in science-learning.

Science students learn better when they receive an appropriate balance of explicit instruction and discovery learning, according to a UNSW study. The research examined student learning experiences in more than 180 NSW science classrooms in real time. It assessed teaching practices, student engagement, self-efficacy and achievement.

Students are more successful in managing their learning when they experience explicit and structured instruction followed by inquiry-based learning, says lead investigator Scientia Professor Andrew Martin from UNSW’s School of Education. This sequential teaching approach, developed by Prof. Martin, is called load reduction instruction.

“Load reduction instruction (LRI) is named as such for its focus on easing the cognitive burden on students as they learn,” the educational psychologist says. “The research outcomes have very direct implications for educators and teacher training institutions. For decades, explicit instruction and discovery learning have often been thought of as mutually exclusive and incompatible.

“Load reduction instruction shows they can be brought together… Explicit instruction at the front end, then when students are reasonably knowledgeable and skilled, they move on to the discovery and inquiry-based learning.”

The research is the first large-scale study examining LRI in science learning. The project is part of a three-year partnership between education and psychology experts from UNSW Sydney and The King’s School, funded by an ARC Linkage Grant.

The project, New Generation Psychology Advances in Science Motivation and Engagement, is co-designed by Prof. Martin and Professor Joel Pearson from UNSW’s School of Psychology with Dr Vera Munro-Smith, Director of The Future Project, the science industry engagement and education centre at The King’s School.

The research draws on educational psychology, physiological psychology and neuropsychology to examine motivation and engagement in science-based learning using diverse methods, including bio-psychological markers. This comprehensive approach to the research aims to address declines in science participation and performance at school and beyond.

“Research tells us that, as school progresses, there is an escalation in academic challenge and a decline in motivation and engagement. We need to approach teaching in ways that help students manage this increasing burden,” Prof. Martin says.

The research tested a new LRI survey tool to facilitate student reporting on teaching practices. It concurrently measured students’ levels of engagement and their achievement. Student reports of their teacher’s LRI were significantly associated with higher levels of engagement, and engagement was significantly associated with higher achievement, the research found.

“LRI enables educators to develop and deliver instruction that appropriately manages the cognitive burden on science students as they learn, and in doing so, enhances these students’ science engagement and in turn, their science achievement,” Prof. Martin says.

Dr Munro-Smith from The King’s School says: “There are multiple factors that can affect students’ learning, motivation or achievement. This research provides us with greater insight into these factors, some of which were previously poorly understood or often ignored.

“We can now see how these factors interact and how that interaction affects student learning. Having a better understanding of their students can only help educators improve learning outcomes.” 

Bio-psychological indicators contribute insights on challenge/threat responses

The project also conducted the largest bio-psychological study of science student learning in classroom environments. It examined the physiological reactions of more than 400 NSW students in two scenarios: taking a science test and participating in a science experiment—and formed the basis of a UNSW PhD, completed by Dr Roger Kennett in 2021, under Prof. Martin and Prof. Pearson’s supervision.

Participating students wore a biometric wristband that assessed arousal through small changes in sweat, where positive arousal is being highly energised and negative arousal is being highly anxious. They also wore an electroencephalography (EEG) headset that measures brain activity on the scalp associated with two functions: attention (alertness) and working (or short-term) memory.

“Mobile, onsite accurate neuro-technology like this is very exciting,” Prof. Pearson says. “Streaming students’ physiological and brain activity data in real time, in the classroom, gives us invaluable insights into the neuroscience behind learning. We are currently at a turning point in history, for the first time we have the technology to do cognitive neuroscience in the classroom.”

Preliminary findings suggest that these bio-psychological indicators, along with key motivation factors, are associated with how much students enjoyed the two science scenarios and how well they performed in them. Importantly, these findings extend prior research by members of the team showing that students’ motivation and electro-dermal activity impact students’ achievement and ‘flow’ in science.

Combining physiological data with self-motivational data creates a more comprehensive picture of student learning, Prof. Pearson says. “By better understanding these relationships, science educators are in a stronger position to enhance and sustain high school students' science motivation and performance,” he says.

Developing self-insight and self-relaxation

Raising students’ awareness of their stress responses opens up opportunities to manage these more effectively, Prof. Martin says. For example, wearing bio-psychological apparatus (such as a Fitbit) is a way for students to learn about themselves—what situations might arouse stress—and, when they see that, there's an opportunity for teachers to teach some quick relaxation techniques.”

Dr Munro-Smith says: “The next step is to utilise this technology by implementing its use in the classroom. We hope that this will become a new tool for teachers to gain a better understanding of their students and how they learn.

“Students could also use this technology to learn how they respond to different learning environments and stimuli, and to help them recognise stressful situations and develop strategies to address this to better manage their own learning.” 

The results also show that alongside physiological measures, it's important to address motivational dimensions, such as students’ self-confidence, their valuing of science, and so on, Prof. Martin says. “The research thus provides a much more rounded perspective on how we help students switch on in STEM.”

To understand the “totality of student experience” is key, Prof. Martin says. “If we include measures from a broader range of sub-disciplines—from educational psychology to biopsychology—then we get to know students better and we get to help them more effectively.”

Images: Supplied.

This article was originally published in 2022.

Written by Kay Harrison
Scientia Professor and Professor of Educational Psychology Andrew Martin
Scientia Professor and Professor of Educational Psychology