New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods

Serious Games to Teach Ethics

In this paper, we are focusing on digital serious games (edugames) and how they can be utilized in teaching in the ethics and citizenship domain. Our aim is to combine narrative techniques with intelligent tutoring techniques in a single model that adopts and based on educational theories and classroom educational strategies. The model has been used to implement an adaptive educational interactive narrative system (AEINS). AEINS is an inquiry based edugame to support teaching ethics. The AEINS version presented in this paper targets students between the age of 8 and 11. The idea is centered around presenting and involving students in different moral dilemmas (called teaching moments) within which the Socratic Method is the used pedagogy in the teaching process. AEINS monitors and analyzes the students actions in order to provide an individualized story-path and an individualized learning process. The student is an active participant in the educational process and is able to interact with the edugame as a first person player. We claim that such interaction can help in developing new or deeper thoughts about different moral situations. Our aim is to contribute to the design of serious games and help raise awareness of ethics and citizenship in children

Medulla: A 2D sidescrolling platformer game that teaches basic brain structure and function

This article explores the design and instructional effectiveness of Medulla, an educational game meant to teach brain structure and function to undergraduate psychology students. Developed in the retro-style platformer genre, Medulla uses two-dimensional gameplay with pixel-based graphics to engage students in learning content related to the brain, information which is often pre-requisite to more rigorous psychological study. A pretest posttest design was used in an experiment assessing Medulla’s ability to teach psychology content. Results indicated content knowledge was significantly higher on the posttest than the pretest, with a large effect size. Medulla appears to be an effective learning tool. These results have important implications in the design of educational psychology games and for educational game designers and artists exploring the possibility of using a two-dimensional retro-style structure

2022-2023 Annual Report

An annual report for the University of North Florida Thomas G. Carpenter Library for the years 2022-23. Includes statistics from patron visits and Access Services, Digital Projects & Preservation, Special Collections & University Archives, Resource Acquisitions & Discovery, Reference & Instruction, Student Awards & Scholarships, and Library Research Prize winners

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic. As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes. The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions. To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making

Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach

Explanations--a form of post-hoc interpretability--play an instrumental role in making systems accessible as AI continues to proliferate complex and sensitive sociotechnical systems. In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of "who" the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems. In particular, we advocate for a reflective sociotechnical approach. We illustrate HCXAI through a case study of an explanation system for non-technical end-users that shows how technical advancements and the understanding of human factors co-evolve. Building on the case study, we lay out open research questions pertaining to further refining our understanding of "who" the human is and extending beyond 1-to-1 human-computer interactions. Finally, we propose that a reflective HCXAI paradigm-mediated through the perspective of Critical Technical Practice and supplemented with strategies from HCI, such as value-sensitive design and participatory design--not only helps us understand our intellectual blind spots, but it can also open up new design and research spaces.Comment: In Proceedings of HCI International 2020: 22nd International Conference On Human-Computer Interactio

Augmented Reality Technology Used To Enhance Informal Science Learning

With science advancements ever-changing and an increased use of multimedia to display information to the public, science literacy and critical thinking skills are important for the public to keep up to date. Students will need to know how to interpret science information they are faced with throughout their lives to make decisions and critique scientific arguments (Squire & Mingfong, 2007). Science education reform is becoming more focused on incorporating science practices with the use of tools and processes to enhance learning. An authentic learning experience can be described as experiencing real problems and consequences in context (Rosenbaum et al., 2007). Augmented reality technology can be used to create authentic learning experiences as it allows for many unique affordances in the field such as place based learning context, personal embodiment of a role, and solving a problem modeling real life science research.This paper will examine augmented reality technology in science education and the pedagogical support behind this technique. The project is comprised of a literature review discussing the benefits and support for augmented reality games used in science education followed by the descriptions of six different augmented reality science games that were created using the online platform “Taleblazer”