The effect of class size on the teaching of pupils aged 7 – 11 years

There is still little consensus on whether and how teaching is affected by small and large classes, especially in the case of students in the later primary years. This study investigated effects of class size on teaching of pupils aged 7 – 11 years. We used a multimethod approach, integrating qualitative information from teachers' end-of-year accounts and data from case studies with quantitative information from systematic observations. Results showed that there was more individual attention in smaller classes, a more active role for pupils, and beneficial effects on the quality of teaching. It is suggested that teachers in both large and small classes need to develop strategies for more individual attention but also recognize the benefits of other forms of learning, for example, group work.     

Double standards and first principles: framing teaching assistant support for pupils with special educational needs

Teaching assistants (TAs) are part of a growing international trend toward paraprofessionals working in public services. There has been controversy over TAs’ deployment and appropriate role when supporting the learning of pupils with special educational needs (SEN) in mainstream schools. Such debates have been transformed by findings from a large study of school support staff in the UK (the DISS project). The findings from this study show that TA support has a negative impact on pupils’ academic progress, especially pupils with SEN. The findings render the current system of support for SEN highly questionable: TAs have inadvertently become the primary educators of pupils with SEN. This paper sets out the likely explanations for the negative effects in terms of three ‘frames’ – deployment, practice and preparedness – and then uses these frames to identify specific implications for pupils with SEN. We offer suggestions on how to make the most productive use of TA support.     

An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities

Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.     

Cultivating constructivist science internships for high school students through a community of practice with cogenerative dialogues

Working with scientists has been suggested as an effective way for high-school students to learn authentic science. However, little research has involved students’ perceptions of science learning environments in a university internship. This study drew on the theoretical framework of community of practice with cogenerative dialogues to design an internship program that aims to build a constructivist internship for students. Students who learned science in the internship program developed stronger constructivist learning perceptions than those who learned science in school. Specifically, students perceived that they had more opportunities to think independently of the instructors and other students. Three effective principles for program design are: (a) high school students conduct open-inquiry projects with the support of scientists; (b) high school students and scientists conduct cogenerative dialogues regularly to address issues and share experiences; and (c) high school students present their project proposals and scientific findings at open house events. Implications of the results are discussed.