Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

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 sixth 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 Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin

Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

Analytic frameworks for assessing dialogic argumentation in online learning environments

Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchers’ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation

Using Data in Undergraduate Science Classrooms

Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Exploration of Argumentation and Scientific Reasoning Ability in Phenomenon-Based Argument-Driven Inquiry Learning in Newton's Law Material

The ability of argumentation and scientific reasoning is part of scientific literacy which is a requirement for learning science. Students get knowledge according to the understanding of scientists, which is important for students to have, especially in Newton's law material, but learning in the classroom currently has not developed argumentation and scientific reasoning skills properly. This study used a mixed method design with an embedded experimental design carried out offline. This research design was used in order to obtain an in-depth picture of students' argumentation and scientific reasoning abilities. The results showed that students' argumentation and scientific reasoning abilities increased significantly after being given the phenomenon-based Argument-Driven Inquiry (ADI) learning model. This learning has a moderate effect on argumentation skills and a very large one on students' scientific reasoning. Even though students' argumentation abilities have increased significantly, on average students still have difficulty in associating a phenomenon with the appropriate Newton's Laws. Scientific reasoning students also experience positive changes. This is evidenced by the increase in post-test scores and effect sizes which are in the very large categor

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results