The use of animated agents in e‐learning environments: an exploratory, interpretive case study

There is increasing interest in the use of animated agents in e‐learning environments. However, empirical investigations of their use in online education are limited. Our aim is to provide an empirically based framework for the development and evaluation of animated agents in e‐learning environments. Findings suggest a number of challenges, including the multiple dialogue models that animated agents will need to accommodate, the diverse range of roles that pedagogical animated agents can usefully support, the dichotomous relationship that emerges between these roles and that of the lecturer, and student perception of the degree of autonomy that can be afforded to animated agents

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)

Virtual pedagogical model: development scenarios

info:eu-repo/semantics/publishedVersio

Pedagogical agents: Influences of artificially generated instructor personas on taking chances

Educational institutes are currently facing the new normality that an ongoing pandemic situation has brought to teaching and learning. Distributed learning with content that blends over several platforms and locations needs to be created with didactic expertise in a feasible manner. At the same time, the possibilities for creating and distributing digital content have developed rapidly. Advanced computing supports the creation of artificial images, natural speech, and even natural-looking but non-existent persons. Since such generative content is often also published under a Creative Commons license, it presents as viable option for designing learning content, assignments, or instructions for tasks. However, there is still limited evidence on how, for example, generated pedagogical agents (tutors) influence behaviour and decisions. This study investigated the influences of artificially generated tutor personas in a decision-making task distributed internationally on the Google Play store. The field experiment extended the balloon analogue risk task (BART) with instructions from generated persona photographs to evaluate potential influences on risk-taking behaviour. In a between-subject design, either a female tutor, a male tutor, or no tutor picture at all was presented during the task. The results (N=74) show a higher risk propensity when displaying a male artificial instructor compared to a female instructor. Participants also proceed with greater caution when instructed by a female tutor as they reflect longer before initiating the next step to pump up the balloon. Further lines of research and experiences from the distribution of an investigative instruction app on Google Play are summarised in the conclusive implications

Explicit Feedback Within Game-based Training: Examining The Influence Of Source Modality Effects On Interaction

This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supports the application of using an EPA embedded in GIFT’s TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game

Using an animated pedagogical agent to support motivation, reflected in task persistence, by delivering formative assessment at classroom level

https://www.ester.ee/record=b5510786*es

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