4 research outputs found
Machine and human observable differences in groupsâ collaborative problem-solving behaviours
This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position (fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill deficiencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have significant implications on the design and implementation of future learning analytics systems
Machine and Human Observable Differences in Groupsâ Collaborative Problem-Solving Behaviours
This paper contributes to our understanding of how to design
learning analytics to capture and analyse collaborative problem-solving
(CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics.
Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position ( fiducial marker) and heads direction (face recognition)
data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill defi ciencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have signifi cant implications on the design and implementation of future learning analytics systems.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech.
Agencia Estatal de InvestigaciĂłn (AEI) y el Fondo Europeo de Desarrollo Regional (FEDER),
TIN2016-80774-R
Analyzing groupsâ problem-solving process to characterize collaboration within groups
Peer reviewedPublisher PD
Artificial Intelligence and Education. Guidance for Policy-makers
Artificial Intelligence (AI) has the potential to address some of the biggest
challenges in education today, innovate teaching and learning practices,
and ultimately accelerate the progress towards SDG 4. However, these rapid
technological developments inevitably bring multiple risks and challenges,
which have so far outpaced policy debates and
regulatory frameworks.
This publication offers guidance for policy-makers on
how best to leverage the opportunities and address
the risks, presented by the growing connection
between AI and education.
It starts with the essentials of AI: definitions,
techniques and technologies. It continues with
a detailed analysis of the emerging trends and
implications of AI for teaching and learning, including
how we can ensure the ethical, inclusive and
equitable use of AI in education, how education can
prepare humans to live and work with AI, and how
AI can be applied to enhance education. It finally
introduces the challenges of harnessing AI to achieve SDG 4 and offers
concrete actionable recommendations for policy-makers to plan policies and
programmes for local contexts