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

Intelligent Mentoring Systems for Making Meaning from Work Experience

This position paper presents a forward-looking view on addressing a long standing professional learning challenge faced by higher educational institutions, namely assisting students to make meaning from work-based experience and develop as reflexive professionals. We suggest that a synergetic approach, building on existing research in professional lifelong learning and intelligent learning environments and taking advantage of new opportunities provided by emerging technologies, will underpin a new breed of intelligent mentoring systems for professional learning. They will foster the learners’ meaning making process, as well as assist tutors in their roles as coaches/mentors

Defining the boundaries between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence

This review aims to provide a concise overview of four distinct research fields: Artificial Intelligence and EDucation (AIED), Computer-Supported Collaborative Learning (CSCL), Educational Data Mining (EDM), and Learning Analytics (LA). While all four fields are focused on understanding learning and teaching using technology, each field has a relatively unique or common perspective on which theoretical frameworks, methods, and ontologies might be appropriate. In this review we argue that researchers should be encouraged to cross the boundaries of their respective field and work together to address the complex challenges in education

How Simulation can Illuminate Pedagogical and System Design Issues in Dynamic Open Ended Learning Environments

A Dynamic Open-Ended Learning Environment (DOELE) is a collection of learners and learning objects (LOs) that could be constantly changing. In DOELEs, learners need the support of Advanced Learning Technology (ALT), but most ALT is not designed to run in such environments. An architecture for designing advanced learning technology that is compatible with DOELEs is the ecological approach (EA). This thesis looks at how to test and develop ALT based on the EA, and argues that this process would benefit from the use of simulation. The essential components of an EA-based simulation are: simulated learners, simulated LOs, and their simulated interactions. In this thesis the value of simulation is demonstrated with two experiments. The first experiment focuses on the pedagogical issue of peer impact, how learning is impacted by the performance of peers. By systematically varying the number and type of learners and LOs in a DOELE, the simulation uncovers behaviours that would otherwise go unseen. The second experiment shows how to validate and tune a new instructional planner built on the EA, the Collaborative Filtering based on Learning Sequences planner (CFLS). When the CFLS planner is configured appropriately, simulated learners achieve higher performance measurements that those learners using the baseline planners. Simulation results lead to predictions that ultimately need to be proven in the real world, but even without real world validation such predictions can be useful to researchers to inform the ALT system design process. This thesis work shows that it is not necessary to model all the details of the real world to come to a better understanding of a pedagogical issue such as peer impact. And, simulation allowed for the design of the first known instructional planner to be based on usage data, the CFLS planner. The use of simulation for the design of EA-based systems opens new possibilities for instructional planning without knowledge engineering. Such systems can find niche learning paths that may have never been thought of by a human designer. By exploring pedagogical and ALT system design issues for DOELEs, this thesis shows that simulation is a valuable addition to the toolkit for ALT researchers

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

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

Integrating Reading and Writing Instruction for Cultivating Students' Critical Thinking

This study aimed to cultivate students’ knowledge and skills imperative for the development of critical thinking.  This research employed qualitative method to explore students’ critical thinking through an integrated instruction of reading and writing tomeasure the extent to which the students understood and afford to learn English in reading and writing skills. In this research, researchers collected the data based on three factors, namely treatment, person, and outcome factor. Treatment factor is a technique of collecting data by observing students through the process of reading and writing instruction in classroom. Person factor, on the other hand, is a technique of collecting data by observing students observed directly by the other researchers in the process of English learning in classroom.Last, outcome factor is a technique of collecting data by using an open-ended question in a questionnaire based on students’ achievement in learning English. The data were analyzed and interpreted to answer the research questions. Based on the result of the study, we found out that the integration of reading and writing enabled the instruction to construct students’ prior knowledge and critical thinking by organizing the appropriate task-based activities. The study also developed the teaching strategies to be more effective in language teaching

Involving Users to Improve the Collaborative Logical Framework

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors’ workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants

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