4,888 research outputs found
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Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.
We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap.
In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts
Dialogue as Data in Learning Analytics for Productive Educational Dialogue
This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers
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Smart labs and social practice: social tools for pervasive laboratory workspaces: a position paper
The emergence of pervasive and ubiquitous computing stimulates a view of future work environments where sharing of information, data and knowledge is easy and commonplace, particularly in highly interactive settings. Much of the work in this area focuses on tool development to support activities such as data collection, data recording and sharing, and so on. We are interested in this kind of technical development, which is both challenging and essential for science communities. But we are also interested in a broader interpretation of knowledge sharing and the human/social side of tools we develop to support this. We are keen to know more about how groups of different kinds of scientists can make their work understandable and shareable with each other in a multidisciplinary setting. This is a complex task because boundaries and barriers can emerge between disciplines engendered by differences in discourses and practices, which may not easily translate into other discipline areas. In the worst case, there may be some hostility between disciplines, or at least doubt and scepticism. Nevertheless, sharing approaches to research, research expertise, data and methods across disciplines can be a very fruitful exercise, and encouragement to engage in this activity is particularly pertinent in the digital era. Issues of privacy and security are also key aspects – knowing when and how to release data or information to other groups is crucial to providing a safe environment for people to work, and there are several sensitivities to be explored here.
In this paper we describe an evolving situation that captures many of these issues, which we aim to track longitudinally
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
Combining exploratory learning with structured practice educational technologies to foster both conceptual and procedural fractions knowledge
Educational technologies in mathematics typically focus on fostering either procedural knowledge by means of structured tasks or, less often, conceptual knowledge by means of exploratory tasks. However, both types of knowledge are needed for complete domain knowledge that persists over time and supports subsequent learning. We investigated in two quasi-experimental studies whether a combination of an exploratory learning environment, providing exploratory tasks, and an intelligent tutoring system, providing structured tasks, fosters procedural and conceptual knowledge more than the intelligent tutoring system alone. Participants were 121 students from the UK (aged 8–10 years old) and 151 students from Germany (aged 10–12 years old) who were studying equivalent fractions. Results confirmed that students learning with a combination of exploratory and structured tasks gained more conceptual knowledge and equal procedural knowledge compared to students learning with structured tasks only. This supports the use of different but complementary educational technologies, interleaving exploratory and structured tasks, to achieve a “combination effect” that fosters robust fractions knowledge
State of the art and practice in AI in education
Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We develop a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway
Introductory programming: a systematic literature review
As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming.
This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research
Generative Artificial Intelligence for Software Engineering -- A Research Agenda
Generative Artificial Intelligence (GenAI) tools have become increasingly
prevalent in software development, offering assistance to various managerial
and technical project activities. Notable examples of these tools include
OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent
publications have explored and evaluated the application of GenAI, a
comprehensive understanding of the current development, applications,
limitations, and open challenges remains unclear to many. Particularly, we do
not have an overall picture of the current state of GenAI technology in
practical software engineering usage scenarios. We conducted a literature
review and focus groups for a duration of five months to develop a research
agenda on GenAI for Software Engineering. We identified 78 open Research
Questions (RQs) in 11 areas of Software Engineering. Our results show that it
is possible to explore the adoption of GenAI in partial automation and support
decision-making in all software development activities. While the current
literature is skewed toward software implementation, quality assurance and
software maintenance, other areas, such as requirements engineering, software
design, and software engineering education, would need further research
attention. Common considerations when implementing GenAI include industry-level
assessment, dependability and accuracy, data accessibility, transparency, and
sustainability aspects associated with the technology. GenAI is bringing
significant changes to the field of software engineering. Nevertheless, the
state of research on the topic still remains immature. We believe that this
research agenda holds significance and practical value for informing both
researchers and practitioners about current applications and guiding future
research
Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education
This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models
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