Suchergebnisse

This systematic review is written in the frame of a European Union funded project called Supporting Lifelong learning with ICT Inquiry-Based Education (LIBE). Through this project, an e-learning environment will be developed for young low educational achievers (aged 16-24). A crucial part in every educational intervention is assessment and feedback. Hence, to contribute to this project, this study aims to review literature regarding computer based feedback and formative assessment in e-learning. Three different approaches are considered as formative assessment, namely: 'diagnostic testing' (DT), 'data based decision making' (DBDM) and 'assessment for learning' (AFL). The search and selection procedure for this review resulted in 19 studies about the effects of feedback and formative assessment in elearning. Four interrelated themes emerged within this body of literature: (1) the effect on achievement, (2) the effect on motivation, (3) self-regulated learning and (4) the effect of learner characteristics. Results are elaborated around those themes and implications for the LIBE project are drawn.

For various reasons, respondents to forced-choice assessments (typically used for noncognitive psychological constructs) may respond randomly to individual items due to indecision or globally due to disengagement. Thus, random responding is a complex source of measurement bias and threatens the reliability of forced-choice assessments, which are essential in high-stakes organizational testing scenarios, such as hiring decisions. The traditional measurement models rely heavily on nonrandom, construct-relevant responses to yield accurate parameter estimates. When survey data contain many random responses, fitting traditional models may deliver biased results, which could attenuate measurement reliability. This study presents a new forced-choice measure-based mixture item response theory model (called M-TCIR) for simultaneously modeling normal and random responses (distinguishing completely and incompletely random). The feasibility of the M-TCIR was investigated via two Monte Carlo simulation studies. In addition, one empirical dataset was analyzed to illustrate the applicability of the M-TCIR in practice. The results revealed that most model parameters were adequately recovered, and the M-TCIR was a viable alternative to model both aberrant and normal responses with high efficiency.

Introduction: Virtual reality (VR)-based interventions, wearable technology and text mining hold promising potential for advancing the way in which military and Veteran mental health conditions are diagnosed and treated. They have the ability to improve treatment protocol adherence, assist in the detection of mental health conditions, enhance resilience and increase a patient's motivation to continue therapy. Methods: This article explores five cutting-edge research projects designed to leverage VR-based interventions, wearable technology, and text mining to improve military and Veteran mental health. A computer-animated virtual agent provides online coaching for posttraumatic stress disorder (PTSD) patients in their own homes to enhance treatment compliance. A head-mounted display safely immerses PTSD patients in a virtual world to relive past experiences and associate them with new meanings. Gaming and simulation technology are tested as a way to improve resilience and performance in military members in deployment-related scenarios. Guidelines are developed for the creation of wearable assistive technology for military members and Veterans. Text mining is explored as a way to assist in the detection of PTSD. Results: VR-based therapy, gaming and simulation, wearable assistive and sensory technology, and text mining hold promise for diagnosing, monitoring, and treating military mental health conditions. Discussion: The five research projects presented have made promising contributions to the field of military and Veteran mental health, either by advancing diagnostic trajectories, contributing to therapy or enhancing the process by developing new approaches to delivering preventive or curative care.

The European Union (EU) Commission's whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society.