Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences

Return of the man-machine interface: violent interactions

This paper presents the design and evaluation of “the man-machine interface” a punchable interface designed to criticise and react against the values inherent in modern systems that tacitly favour one type of user (linguistically and technically gifted) and alienate another (physically gifted). We report a user study, where participants used the device to express their opinions before engaging in a group discussion about the implications of strength-based interactions. We draw connections between our own work and that of evolutionary biologists whose recent findings indicate the shape of the human hand is likely to have been partly evolved for the purpose of punching, and conclude by examining violent force as an appropriate means for expressing thoughts and feelings

Out with the Humans, in with the Machines?: Investigating the Behavioral and Psychological Effects of Replacing Human Advisors with a Machine

This study investigates the effects of task demonstrability and replacing a human advisor with a machine advisor. Outcome measures include advice-utilization (trust), the perception of advisors, and decision-maker emotions. Participants were randomly assigned to make a series of forecasts dealing with either humanitarian planning (low demonstrability) or management (high demonstrability). Participants received advice from either a machine advisor only, a human advisor only, or their advisor was replaced with the other type of advisor (human/machine) midway through the experiment. Decision-makers rated human advisors as more expert, more useful, and more similar. Perception effects were strongest when a human advisor was replaced by a machine. Decision-makers also experienced more negative emotions, lower reciprocity, and faulted their advisor more for mistakes when a human was replaced by a machine

A DSS generator for multiobjective optimisation of spreadsheet-based models

Copyright © 2011 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software Vol. 26 (2011), DOI: 10.1016/j.envsoft.2010.11.004Water management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support applications. GANetXL’s utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.Engineering and Physical Sciences Research Council (EPSRC

Understanding Complexity in Multiobjective Optimization

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity

Big Data Optimization : Algorithmic Framework for Data Analysis Guided by Semantics

Fecha de Lectura de Tesis: 9 noviembre 2018.Over the past decade the rapid rise of creating data in all domains of knowledge such as traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the process of analyzing large data volumes, in order to be able to manage them with more easiness and in addition, discover new relationships which are hidden in them Optimization problems, which are commonly found in current industry, are not unrelated to this trend, therefore Multi-Objective Optimization Algorithms (MOA) should bear in mind this new scenario. This means that, MOAs have to deal with problems, which have either various data sources (typically streaming) of huge amount of data. Indeed these features, in particular, are found in Dynamic Multi-Objective Problems (DMOPs), which are related to Big Data optimization problems. Mostly with regards to velocity and variability. When dealing with DMOPs, whenever there exist changes in the environment that affect the solutions of the problem (i.e., the Pareto set, the Pareto front, or both), therefore in the fitness landscape, the optimization algorithm must react to adapt the search to the new features of the problem. Big Data analytics are long and complex processes therefore, with the aim of simplify them, a series of steps are carried out through. A typical analysis is composed of data collection, data manipulation, data analysis and finally result visualization. In the process of creating a Big Data workflow the analyst should bear in mind the semantics involving the problem domain knowledge and its data. Ontology is the standard way for describing the knowledge about a domain. As a global target of this PhD Thesis, we are interested in investigating the use of the semantic in the process of Big Data analysis, not only focused on machine learning analysis, but also in optimization

Improving the performance of GIS/spatial analysts though novel applications of the Emotiv EPOC EEG headset

Geospatial information systems are used to analyze spatial data to provide decision makers with relevant, up-to-date, information. The processing time required for this information is a critical component to response time. Despite advances in algorithms and processing power, we still have many “human-in-the-loop” factors. Given the limited number of geospatial professionals, analysts using their time effectively is very important. The automation and faster humancomputer interactions of common tasks that will not disrupt their workflow or attention is something that is very desirable. The following research describes a novel approach to increase productivity with a wireless, wearable, electroencephalograph (EEG) headset within the geospatial workflow

Multiple Criteria Decision Making and Multiattribute Utility Theory

T his paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields