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

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 Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education.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

Let's Set Up Some Subgoals: Understanding Human-Pedagogical Agent Collaborations and Their Implications for Learning and Prompt and Feedback Compliance

International audienceResearch on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co-and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners' compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co-and socially-shared regulated learning theories to include learner-PA interactions are proposed

Scaffolding for Optimal Challenge in K–12 Problem-Based Learning

Establishing optimal challenge enhances intrinsic motivation, interest, and the probability of success in the learning activity. In K–12 problem-based learning (PBL), students may struggle to address associated tasks that are beyond their current ability levels. This paper suggested learner-centered scaffolding systems (LSS) to improve K–12 students’ perception of optimal challenge by addressing their learning issues in PBL. LSS enhances students’ experience in autonomy and competence by providing multiple types of scaffolding in accordance with students’ different needs and difficulties in PBL. Students can control the nature and frequency of scaffolding by themselves according to their needs and ability, and it plays a role in improving their self-directed learning skills. Last, peer scaffolding between students with similar abilities satisfies students’ needs for relatedness

Possible approaches to personalisation at Cass Business School

This report discusses the result of an investigation into the personalisation of learning and how it could be applied in a university with primarily face-to-face instruction, and, a diverse student body

Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning

Schodde T. Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning. Bielefeld: Universität Bielefeld; 2019.Language education is a global and important issue nowadays, especially for young children since their later educational success build on it. But learning a language is a complex task that is known to work best in a social interaction and, thus, personalized sessions tailored to the individual knowledge and needs of each child are needed to allow for teachers to optimally support them. However, this is often costly regarding time and personnel resources, which is one reasons why research of the past decades investigated the benefits of Intelligent Tutoring Systems (ITSs). But although ITSs can help out to provide individualized one-on-one tutoring interactions, they often lack of social support. This dissertation provides new insights on how a Socially Assistive Robot (SAR) can be employed as a part of an ITS, building a so-called "Socially Assistive Robot Tutoring System" (SARTS), to provide social support as well as to personalize and scaffold foreign language learning for young children in the age of 4-6 years. As basis for the SARTS a novel approach called A-BKT is presented, which allows to autonomously adapt the tutoring interaction to the children's individual knowledge and needs. The corresponding evaluation studies show that the A-BKT model can significantly increase student's learning gains and maintain a higher engagement during the tutoring interaction. This is partly due to the models ability to simulate the influences of potential actions on all dimensions of the learning interaction, i.e., the children's learning progress (cognitive learning), affective state, engagement (affective learning) and believed knowledge acquisition (perceived learning). This is particularly important since all dimensions are strongly interconnected and influence each other, for example, a low engagement can cause bad learning results although the learner is already quite proficient. However, this also yields the necessity to not only focus on the learner's cognitive learning but to equally support all dimensions with appropriate scaffolding actions. Therefore an extensive literature review, observational video recordings and expert interviews were conducted to find appropriate actions applicable for a SARTS to support each learning dimension. The subsequent evaluation study confirms that the developed scaffolding techniques are able to support young children’s learning process either by re-engaging them or by providing transparency to support their perception of the learning process and to reduce uncertainty. Finally, based on educated guesses derived from the previous studies, all identified strategies are integrated into the A-BKT model. The resulting model called ProTM is evaluated by simulating different learner types, which highlight its ability to autonomously adapt the tutoring interactions based on the learner's answers and provided dis-engagement cues. Summarized, this dissertation yields new insights into the field of SARTS to provide personalized foreign language learning interactions for young children, while also rising new important questions to be studied in the future

Building Artificially Intelligent Learning Games

The idea of digital game-based learning (DGBL) is gaining acceptance among researchers, game designers, educators, parents, and students alike. Building new educational games that meet educational goals without sacrificing what makes games engaging remains largely unrealized, however. If we are to build the next generation of learning games, we must recognize that while digital games might be new, the theory and technologies we need to create DGBL has been evolving in multiple disciplines for the last 30 years. This chapter will describe an approach, based on theories and technologies in education, instructional design, artificial intelligence, and cognitive psychology, that will help us build intelligent learning games (ILGs)

Negotiated Tutoring: An Approach to Interaction in Intelligent Tutoring Systems

This thesis describes a general approach to tutorial interaction in Intelligent Tutoring Systems, called "Negotiated Tutoring". Some aspects of the approach have been implemented as a computer program in the 'KANT' (Kritical Argument Negotiated Tutoring) system. Negotiated Tutoring synthesises some recent trends in Intelligent Tutoring Systems research, including interaction symmetry, use of explicit negotiation in dialogue, multiple interaction styles, and an emphasis on cognitive and metacognitive skill acquisition in domains characterised by justified belief. This combination of features has not been previously incorporated into models for intelligent tutoring dialogues. Our approach depends on modelling the high-level decision-making processes and memory representations used by a participant in dialogue. Dialogue generation is controlled by reasoning mechanisms which operate on a 'dialogue state', consisting of conversants' beliefs, a set of possible dialogue moves, and a restricted representation of the recent utterances generated by both conversants. The representation for conversants' beliefs is based on Anderson's (1983) model for semantic memory, and includes a model for dialogue focus based on spreading activation. Decisions in dialogue are based on preconditions with respect to the dialogue state, higher level educational preferences which choose between relevant alternative dialogue moves, and negotiation mechanisms designed to ensure cooperativity. The domain model for KANT was based on a cognitive model for perception of musical structures in tonal melodies, which extends the theory of Lerdahl and Jackendoff (1983). Our model ('GRAF' - GRouping Analysis with Frames) addresses a number of problems with Lerdahl and Jackendoff's theory, notably in describing how a number of unconscious processes in music cognition interact, including elements of top-down and bottom-up processing. GRAF includes a parser for musical chord functions, a mechanism for performing musical reductions, low-level feature detectors and a frame-system (Minsky 1977) for musical phrase structures

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers