Mathematical thinking of undergraduate students when using three types of software

The research investigates how conceptual understanding of mathematics is promoted when using three types of software: black-box (no mathematical intermediate steps shown), glass-box (intermediate steps shown) and open-box (interaction at each intermediate step). Thirty-eight students were asked to think-aloud and give detailed explanations whilst answering three types of tasks: mechanical (mostly procedural), interpretive (mostly conceptual) and constructive (mixture of conceptual and procedural). The software types had no impact on how students answered the mechanical tasks; however students using the black-box did better on the constructive tasks because of their increased explorations. Students with low maths confidence resorted to using real-life explanations when answering tasks that were application related

Beginning to Unlock the Black Box in the HR Firm Performance Relationship: The Impact of HR Practices on Employee Attitudes and Employee Outcomes

Theoretical models in strategic human resource management research commonly include employee attitudes and behaviors as key mediating links between human resource practices and firm performance. However, almost all empirical SHRM work to date has ignored the mediating hypothesis and merely examined the direct relationship between HR practices and firm outcomes. The purpose of this study is to test the relationship between HR practices and employee attitudes and behaviors. Using a sample of 174 independent work groups, we examined the relationship between HR practices and collective behaviors (turnover and absenteeism) mediated by collective attitudes (job satisfaction and commitment). Results indicate attitudes partially mediate the relationship between HR practices and employee behaviors. The direct and indirect relationships identified in this study support the notion that attitudes and behaviors play a mediating role between HR practices and firm outcomes. These findings illustrate the varying impacts of HR practices and the importance of utilizing multilevel theory and methods

Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semi-unstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation 201

The Grammar of Interactive Explanatory Model Analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits. Combined, these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table

Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM

Unlocking the black box: line managers and HRM performance in a call centre context

Purpose – The purpose of this paper is to show the way to unlock the black box of HRM and performance linkages by exploring one of the key variables that mediates the link, namely whether line managers can stimulate improvements in firm performance by eliciting appropriate employee outcomes in a call centre context. Design/methodology/approach – The research draws on Purcell's "People-Performance Model" as a sensitising framework to inform an in-depth case study of a call centre. This provides a mechanism to unlock the HRM-Performance black box by focusing on the ability, motivation and opportunities for line managers to perform and any subsequent impact on employee outcomes. Data were collected over multiple site visits by means of multi-level interviews and a survey of telesales representatives (TSRs). Findings – Research findings indicate that one large client exerted significant control over the HRM policies developed within the call centre. Evidence suggests, however, that line managers'interventions ameliorated some of the negative aspects of work tasks and the HRM imposed by this dependency relationship. Research limitations/implications – This research is an exploratory attempt to better understand HRM-Performance linkages in one specific context. Results are not generalisable across contexts or even within call centres, which can vary extensively. Nonetheless, the research suggests that exploring line management behaviour is a promising avenue for more extensive research. Originality/value – This paper considers HRM-Performance linkages in a service context. Results indicate that both external relations and line managers are critical mediating variables conditioning HRM-Performance linkages, thereby lending support to the notion that hard and soft HRM practices are not necessarily irreconcilable