1,721,103 research outputs found
Generative AI for Learning Analytics (GenAI-LA):Evidence of Impacts on Human Learning
No full textThe Second GenAI-LA workshop aims to examine the impacts of generative artificial intelligence (GenAI) on human learning. As technological advancements continue to reshape education, GenAI presents new opportunities for various aspects such as personalised learning, automated feedback and so on. However, empirical evidence of GenAI’s impacts on human learning remains limited, necessitating the adoption of learning analytics to offer rigorous and evidence-driven insights on how GenAI affects human learning. This workshop aims to ignite discussions and foster collaboration among a subcommunity of LA researchers and practitioners to scrutinise and envision how LA may shed light on GenAI’s impacts on human learning. We received a total of 13 paper submissions. Following a thorough peer review process, we accepted 10 papers. These papers present unique findings / directions to the utilisation of LA for enabling empirical evidence regarding how GenAI plays a role in human learning, from the theoretical discussions of concerns in leveraging GenAI, to the practical development and evaluation of GenAI-powered tools in supporting learning.</p
The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence
Get PDFThis paper presents a multi-dimensional view of AI\u27s role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools, as exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human-AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human-AI systems to extend human cognition, and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education research (AIED), which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for education, as well as the perils of overemphasis on externalising human cognition. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI, but also includes educating people about AI and innovating educational systems to remain relevant in an AI-ubiquitous world.20 pages, 7 figures, this paper is based on the keynote talk given by the author at the ACM International Conference on Learning Analytics & Knowledge (LAK) 2024 in Kyoto, Japan. https://www.solaresearch.org/events/lak/lak24/keynotes
Novices Make More Noise!: The D&K Effect 2.0?
No full textThis paper presents an approach that helps distinguish expert and novice performance easily by observing the sensor data without having to understand nor apply models to the sensor signal. The method consists of plotting the sensor data and identifying irregularities. We corroborate, with the help of sensors, that expert performances are smoother, contain fewer irregularities, and have consistently uniform patterns than novice performances. In this paper, we present six different cases pointing out this assertion, namely bachata and salsa dances, tennis swings, football penalty kicks, badminton, and running.Web Information System
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
Get PDFMultimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities
Real-Time Multimodal Feedback with the CPR Tutor
No full textWe developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: 1) an architecture design, 2) a methodological approach to design multimodal feedback and 3) a field study on real-time feedback for CPR training
Promoting and Protecting Teacher Agency in the Age of Artificial Intelligence
Get PDFArtificial intelligence (AI) is reshaping the
education landscape, yet its transformative
potential will ultimately be defined by the
people who design, implement, and mediate
its use. Central to this human infrastructure
are teachers, whose professional capacities
and agency should be foregrounded in any
AI integration strategy. Investing in teachers’
professional development, through robust,
future-oriented, and contextually grounded
initiatives, is thus a critical way to ensure that AI
technologies complement, rather than replace,
the pedagogical expertise and ethical judgment
at the heart of teaching.
This position paper is closely aligned with the
mandate of the International Task Force on
Teachers for Education 2030 (TTF), reflecting
the collective perspectives of TTF’s diverse
global constituency and reaffirming the
imperative to uphold teachers’ agency, dignity,
and professional autonomy as technology
changes. It sets forth the TTF’s initial position
on AI in education and aims to catalyse
deeper policy dialogue, collaboration, and
the joint construction of guiding principles
that acknowledge teachers’ central role in
equitable and sustainable education futures
The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence
Get PDFThis paper presents a multidimensional view of AI's role in education, emphasising the intricate interplay among AI, analytics and human learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools in Education, exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human–AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human–AI systems to extend human cognition and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education Research (AIED), which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualisations of AI: the externalisation of human cognition, the internalisation of AI models to influence human mental models and the extension of human cognition via tightly coupled human–AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for human competence development, as well as the perils of overemphasis on approaches that replace human learning opportunities with AI tools. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI and includes educating people about AI and innovating educational systems to remain relevant in an AI ubiquitous world
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Investigation of an Independent Learning Approach in University Level Chemistry: The Effects on Students' Knowledge, Understanding and Intellectual Attributes
Get PDFThe aim of this study is to provide a preliminary insight into a teaching strategy which deploys independent learning in degree-level chemistry. For this purpose, the impact of the teaching approach applied in a Macromolecules course on students’ knowledge of, understanding about chemical ideas, and intellectual attributes was investigated. To achieve this, diagnostic questions both before and after the teaching, descriptive questionnaires and standardised interviews were used.
The sample consisted of first-year undergraduate students who took the Macromolecules course in the Department of Chemistry in one of the top ten universities in the UK. In total, one hundred sixty-seven students took part in the study and interviews were carried out with twenty-four students.
The results revealed that the independent learning strategy applied in the Macromolecules course can be effective in improving students’ knowledge of and understanding about chemical ideas, as well as contributing to some of their intellectual attributes. It was also found that when students were left on their own to do independent investigations, without any support, their knowledge of and understanding about chemical ideas from the course content did not change statistically significantly. In addition, whilst there was no statistically significant change in student responses with a sign of misunderstanding for nine out of ten diagnostic questions, in one case there was statistically significant increase in the number of student responses with a sign of misunderstanding.
The results of this research study also presented a detailed and personal picture of students’ views of the independent learning approach. Student arguments for their appreciation and disapproval of the strategy were revealed and discussed.
The findings of this research study can offer a supplementary resource for teachers at tertiary level to use in situated ways when dealing with similar course contents and similar learning objectives which they encounter during their practice
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