Reducing school exclusion

THE USE OF SCHOOL SUSPENSION AND EXPULSION TO EXCLUDE STUDENTS FROM SCHOOL PRESENTS A MAJOR DILEMMA FOR STAFF, WRITES STUDENT WELLBEING EXPERT, SHERYL HEMPHILL.

Sheryl Hemphill PhD is a freelance writer, presenter, and researcher. She has conducted research for over 25 years on the prevention of violence, antisocial behaviour, and cyberbullying, as well as school behaviour management approaches. Her current focus is on sharing research findings with schools and the broader community.

When a student engages in behaviour that threatens the safety of the student themselves or others, leadership teams need to use approaches such as exclusion.

But excluding a student from school is inconsistent with the aim of school communities to be inclusive. There is also the risk that if the excluded student does not want to be in class (as is often the case), the problematic behaviour is rewarded.

On top of this, research has shown that there are a range of negative consequences of suspension for the suspended student including increased antisocial behaviour, alcohol and drug

A recent systematic review and meta-analysis of 37 studies of school-based interventions found a short-term reduction in school exclusion.

use, delayed graduation, and not completing school.

School leaders continue to use suspension and expulsion because they are the highest level of response available to them for serious problem behaviours. Sometimes, school staff find other ways of handling serious student behaviours that fit with the circumstances of their local communities. However, these approaches may not have been evaluated.

To date, the research literature has not provided clarity on effective ways to reduce the use of school exclusion. This may now have changed.

In a recent systematic review and metaanalysis of 37 studies of school-based interventions that aimed to reduce the use of school exclusion, a short-term (six months) reduction in school exclusion was found. Reductions in the use of school exclusion for 12 months or more were not found.

In the review, school exclusion was defined as removing students from teaching for a period of time and included in-school and out-ofschool suspension and expulsion. The latter two approaches remove students from the school setting, whereas the former removes students from the classroom. Students included in the review were aged four to 18 years of age andattended mainstream schools.

Published online in March in the Journal of Experimental Criminology, the review was conducted by Sara Valdebenito and colleagues at the University of Cambridge and the RAND Europe research institute.

The review showed that 73 per cent of the interventions focused on changing students’ skills or behaviour, whereas 27 per cent focused on changes at the level of the school or teacher. On average, the interventions lasted for 20 weeks. Interventions were more effective at reducing

in-school suspensions and expulsions.

Several potential school-based approaches to reduce school exclusion were identified. The two interventions with the strongest and most reliable effects were: • Mentoring/monitoring for students; and • Skills training for teachers.

The mentoring/monitoring interventions were structured and supportive relationships between students with academic, behavioural or emotional problems and non-parental adults such as teachers, counsellors, and other members of the community. The specific role of the mentors differed in each study but, in general, they were role models. They also provided support, assisted students with academic tasks, supervised academic performance, and gave students advice or counselling. There were five studies on this form of intervention included in the review.

The skills training for teachers comprised establishing clear rules in the classroom, facilitating mutual respect between teachers and students, and strategies for teachers to work with parents to encourage students’ participation in school activities. A total of four studies on this intervention were included in the review.

Two other types of school-based interventions were promising: • Improvement of the students’ academic skills (two studies in the review); and • counselling/mental health services for students (three studies in the review).

The researchers cautioned that the number of studies in their review on the specific types of school-based intervention was small. Only randomised controlled trials were included in the review. These are studies in which participants, classrooms or schools are randomly assigned to a treatment or a control group and are the gold standard for tests of interventions.

Another caution about the findings was that most of the studies had been conducted in the United States of America so we do not know how well the findings apply in other countries like Australia. An important area for future research is to conduct studies in a range of countries around the world.

The results of the review did not show a reduction in students’ antisocial behaviour following participation in school-based interventions compared with a control group. This is not surprising since the interventions focused on reducing rates of exclusion rather than changing student behaviour.

The authors of the review called for more intervention studies that seek to understand how the intervention may impact on the use of school exclusion – what are the key elements of the interventions that show reductions in school exclusion?

In addition, the review authors encouraged the use and study of innovative approaches to the reduction of the use of school exclusion.

Although the results of the systematic review and meta-analysis are instructive, further high quality research is needed to address the question of how schools can reduce their use of exclusion. Ensuring that any reductions in exclusion continue beyond six months is an area that needs to be addressed. EM

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Improving student learning in a connected world

DATA SCIENCE RESEARCHERS FROM THE UNIVERSITY OF TECHNOLOGY SYDNEY HAVE TEAMED UP WITH ACER IN A PROJECT THAT USES ARTIFICIAL INTELLIGENCE AND DATA SCIENCE TO ADVANCE LEARNING OUTCOMES.

The University of Technology Sydney (UTS), in partnership with computer technology companies Acer and Intel, has launched a new pilot program that trials new methods of monitoring student attentiveness and learning in the classroom.

An industry first-of-its-kind, the UTS x Acer Learner Attention Analytics Pilot Program is currently being piloted with 200 data science students in the faculty of Engineering and IT at UTS, with several high schools expected to participate in the future.

The program employs the latest technology in attention analysis, with the aim of establishing a fuller understanding of student behaviour in a classroom setting. The desired outcome is the development of a proof-of-concept platform that could help enhance student learning experiences and outcomes.

The pilot program was announced in early April at the UTS campus in Ultimo by Acer’s Oceania managing director, Darren Simmons, and the university’s executive director of data science, Professor Fang Chen.

Mr Simmons spoke of the necessity of harnessing developments in computer technology to aid the process of learning in a classroom setting in an age of ever greater connectivity. He says that with Acer selling 70,000-80,000 laptops in the education space annually, he was regularly confronted with questions regarding the efficacy of computer usage in student learning outcomes.

“Do students really learn better? Do they really interact better with the environment? Is the process individualised, is it actually having an impact? These are questions that are directed to me all the time. It’s a challenge, and one that this project is working on,” says Mr Simmons.

Mr Simmons says the program, developed in partnership with UTS, would revolutionise personal learning in and out of schools, enable live learning and information sharing, and help develop hardware and software that will enhance the learning experience and

promote the wellbeing of students.

“Acer is thrilled to support the UTS Data Science team led by Professor Chen and to be part of a pilot program that will transform the education sector and be crucial in preparing students for the future,” Mr Simmons says.

“In addition to education, it will also assist technology providers, such as Acer, to develop new computers and software applications and behaviouraware computer technology to better facilitate the changing needs of the education sector.”

The project involves the collection of learner data using hand gesture and eye-tracking technology combined with a graphical user interface (GUI) to record mouse movements, keyboard and digital pen usage and eye movements. The data will then be analysed using artificial intelligence (AI) and machine learning algorithms to determine behaviour patterns and the linkage to learning outcomes.

According to Professor Chen, students in a highly-connected, digitalised world now face more distractions than ever before. Combined with the old issue of different students learning at different ways at varying paces, the presence of devices, and their potential to disrupt student attention, is putting traditional teaching approaches that rely on a standardised curriculum to the test. It also creates a greater need for educators to better understand how to capture the attention of different students.

“There’s so much for a student to learn. How can they can use the limited time and limited space to learn quickly and in the way that is best for their learning outcomes? The current system is that you read results or your report card after semester or after you finish the course, and you get the score,” says Professor Chen.

“How can you know in between how you are performing and how you’re dealing with the content – whether you feel the content is suitable for you or not, and how the pace of the learning is for you?”

Professor Chen explains the purpose of the project was to facilitate the development of learning experience that was more personalised and more responsive to the needs of individual students.

“The aim of the UTS x Acer Learner Attention Analytics Pilot Program is to create an education industry blueprint that can generate tailored personalised learning programs according to learners’ behaviour patterns,” she says.

“Using learners’ behaviour as a fundamental indicator of attention and analysing this with AI and machine learning technologies will enable the education sector to optimise the pace and learning materials for the needs of different learners.”

During the launch, the technology was demonstrated to those assembled. When a learner sits in front of a computer, a camera will capture what the learner’s eyes are attending to, while software will register whether, and in what manner, the learner is touching and using the mouse and keyboard. According to Professor Chen, these forms of student monitoring will help determine if the individual learner is focussing on the content they should be and to what extent the student is preoccupied with other distractions.

“Basically, that’s the concept: to capture all the behaviours of the learner in front of a digital device. This is tracking where you’re looking at, mouse movement, and – how you’re interacting with the device.”

In this manner, Professor Chen says, data would be collected and analysed to establish what insights can be established on the basis of particular patterns of recorded behaviour. “It is in data analysis that we can try to find out what different behaviours mean. Do they mean that the student is more engaged? Does the engagement link

The UTS x Acer Learner Attention Analytics Pilot Program was demonstrated during a launch event in April.

to a better learning outcome or not?”

The pilot project is now in the initial data collection stage of the program, involving two classes within the School of Computer Science UTS Data Arena, including an undergraduate class of 150 students studying software engineering, and a postgraduate class of 50 students using machine learning.

According to the UTS research team, the project will also have the potential to be further extended to detect learner frustration and hesitation, the determination of which, they claimed, was essential in developing customised teaching and learning, and integral in improving student experience and wellbeing.

“The purpose of the project is to make the learning experience better and more positive, and to help students learn not only in school, but in life beyond school,” Professor Chen says.

“We hope that this is a step towards providing a more engaging and personalised learning experience for every student.” EM

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