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 Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education.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

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

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

Modes and Mechanisms of Game-like Interventions in Intelligent Tutoring Systems

While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey\u27s Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students\u27 affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy

THE ROLE OF SIMULATION IN SUPPORTING LONGER-TERM LEARNING AND MENTORING WITH TECHNOLOGY

Mentoring is an important part of professional development and longer-term learning. The nature of longer-term mentoring contexts means that designing, developing, and testing adaptive learning sys-tems for use in this kind of context would be very costly as it would require substantial amounts of fi-nancial, human, and time resources. Simulation is a cheaper and quicker approach for evaluating the impact of various design and development decisions. Within the Artificial Intelligence in Education (AIED) research community, however, surprisingly little attention has been paid to how to design, de-velop, and use simulations in longer-term learning contexts. The central challenge is that adaptive learning system designers and educational practitioners have limited guidance on what steps to consider when designing simulations for supporting longer-term mentoring system design and development deci-sions. My research work takes as a starting point VanLehn et al.’s [1] introduction to applications of simulated students and Erickson et al.’s [2] suggested approach to creating simulated learning envi-ronments. My dissertation presents four research directions using a real-world longer-term mentoring context, a doctoral program, for illustrative purposes. The first direction outlines a framework for guid-ing system designers as to what factors to consider when building pedagogical simulations, fundamen-tally to answer the question: how can a system designer capture a representation of a target learning context in a pedagogical simulation model? To illustrate the feasibility of this framework, this disserta-tion describes how to build, the SimDoc model, a pedagogical model of a longer-term mentoring learn-ing environment – a doctoral program. The second direction builds on the first, and considers the issue of model fidelity, essentially to answer the question: how can a system designer determine a simulation model’s fidelity to the desired granularity level? This dissertation shows how data from a target learning environment, the research literature, and common sense are combined to achieve SimDoc’s medium fidelity model. The third research direction explores calibration and validation issues to answer the question: how many simulation runs does it take for a practitioner to have confidence in the simulation model’s output? This dissertation describes the steps taken to calibrate and validate the SimDoc model, so its output statistically matches data from the target doctoral program, the one at the university of Saskatchewan. The fourth direction is to demonstrate the applicability of the resulting pedagogical model. This dissertation presents two experiments using SimDoc to illustrate how to explore pedagogi-cal questions concerning personalization strategies and to determine the effectiveness of different men-toring strategies in a target learning context. Overall, this dissertation shows that simulation is an important tool in the AIED system design-ers’ toolkit as AIED moves towards designing, building, and evaluating AIED systems meant to support learners in longer-term learning and mentoring contexts. Simulation allows a system designer to exper-iment with various design and implementation decisions in a cost-effective and timely manner before committing to these decisions in the real world