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

The use of Artificial intelligence in school science: a systematic literature review

Artificial Intelligence is widely used across contexts and for different purposes, including the field of education. However, a review of the literature showcases that while there exist various review studies on the use of AI in education, missing remains a review focusing on science education. To address this gap, we carried out a systematic literature review between 2010 and 2021, driven by three questions: a) What types of AI applications are used in school science? b) For what teaching content are AI applications in school science used? and, c) What is the impact of AI applications on teaching and learning of school science? The studies reviewed (n = 22) included nine different types of AI applications: automated assessment, automated feedback, learning analytics, adaptive learning systems, intelligent tutoring systems, multilabel text classification, chatbot, expert systems, and mind wandering detection. The majority of the AI applications are used in geoscience or physics and AI applications are used to support either knkowledge construction or skills development. In terms of the impact of AI applications, this is found across the following: learning achievement, argumentation skills, learning experience, and teaching. Missing remains an examination of learners’ and teachers’ experiences with the use of AI in school science, interdisciplinary approaches to AI implementation, as well as an examination of issues related to ethics and biase

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Technology-supported personalised learning: Rapid Evidence Review

This Rapid Evidence Review (RER) provides an overview of existing research on the use of technology to support personalised learning in low- and middle-income countries (LMICs). The RER has been produced in response to the widespread global shutdown of schools resulting from the outbreak of COVID-19. It therefore emphasises transferable insights that may be applicable to educational responses resulting from the limitations caused by COVID-19. In the current context, lessons learnt from the use of technology-supported personalised learning — in which technology enables or supports learning based upon particular characteristics of relevance or importance to learners — are particularly salient given this has the potential to adapt to learners’ needs by ‘teaching at the right level’

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

The effectiveness of using intelligent tutoring systems to increase student achievement

Intelligent Tutoring Systems could be used to provide differentiated instruction. This review examines qualities of Intelligent Tutoring Systems and their impact on student achievement. Thirty peer-reviewed research studies published from 1997 to 2019 were selected for analysis. This review considers how intelligent tutoring systems compare with other methods of instruction, and how an intelligent tutoring system’s on-screen tutor impacts student achievement. Finally, this review considers methods of ITS personalization and how those methods impact student achievement. The reviewed research studies indicated that ITS was more effective than all forms of instruction except small group and individualized instruction. Additionally, on-screen agents in and personalization of Intelligent Tutoring Systems often have a positive impact on student learning. Recommendations for classroom implementation of intelligent tutoring systems and suggestions for future research are discusse

Learning-by-Teaching in CS Education: A Systematic Review

To investigate the strategies and approaches in teaching Computer Science (CS), we searched the literature review in CS education in the past ten years. The reviews show that learning-by-teaching with the use of technologies is helpful for improving student learning. To further investigate the strategies that are applied to learning-by-teaching, three categories are identified: peer tutoring, game-based flipped classroom, and teachable agents. In each category, we further searched and investigated prior studies. The results reveal the effectiveness and challenges of each strategy and provide insights for future studies

Sharing system of learning resources for adaptive strategies of scholastic remedial intervention

[EN] This paper presents a model for school remedial, focusing on improving the digital materials sharing process for the diversification of tutoring strategies. The model involves the characterization of materials for automatic assessment shared within a community of tutors. The characterization expects materials to be linked with natural language descriptors explicating their intended instructional objectives. The possibility of implementing a recommendation system on the basis of natural language processing techniques is discussed taking in consideration an analysis of the application of the model within a local-scale project. Clustering techniques searching for materials that have the same educational purposes but involve the activation of different cognitive processes are proposed, in order to continuously extend the database of shared materials in favour of the effectiveness of ongoing tutoring actions. The results collected from questionnaires submitted to students, tutors, and teachers involved in the project are shown, and clustering data are discussed highlighting the feasibility of the application of the model.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Barana, A.; Di Caro, L.; Fioravera, M.; Floris, F.; Marchisio, M.; Rabellino, S. (2018). Sharing system of learning resources for adaptive strategies of scholastic remedial intervention. Editorial Universitat Politècnica de València. 1495-1503. https://doi.org/10.4995/HEAD18.2018.8232OCS1495150