Analytics of learning tactics and strategies in an online learnersourcing environment

Background

The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.

Objectives

To contribute to filling this gap, this study explores how students engage with learnersourcing tasks across a range of course and assessment designs.

Methods

We conducted an exploratory study on trace data of 1279 students across three courses, originating from the use of a learnersourcing environment under different assessment designs. We employed a new methodology from the learning analytics (LA) field that aims to represent students' behaviour through two theoretically-derived latent constructs: learning tactics and the learning strategies built upon them.

Results

The study's results demonstrate students use different tactics and strategies, highlight the association of learnersourcing contexts with the identified learning tactics and strategies, indicate a significant association between the strategies and performance and contribute to the employed method's generalisability by applying it to a new context.

Implications

This study provides an example of how learning analytics methods can be employed towards the development of effective learnersourcing systems and, more broadly, technological educational solutions that support learner-centred and data-driven learning at scale. Findings should inform best practices for integrating learnersourcing activities into course design and shed light on the relevance of tactics and strategies to support teachers in making informed pedagogical decisions.     

Process optimization via confidence region: a case study from micro-injection molding

Journal of Intelligent Manufacturing - In industrial research, experiments are designed to determine the optimal factor levels of the process parameters. Typically, experimental data are used to...     

A framework for dynamic multiple-criteria decision making

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Discovering invariants via simple component analysis

We propose a new technique combining dynamic and static analysis of programs to find linear invariants. We use a statistical tool, called simple component analysis, to analyze partial execution traces of a given program. We get a new coordinate system in the vector space of program variables, which is used to specialize numerical abstract domains. As an application, we instantiate our technique to interval analysis of simple imperative programs and show some experimental evaluations.     

Discrete time sliding mode control of robotic manipulators: Development and experimental validation

This paper presents a robust discrete-time sliding mode control coupled with an uncertainty estimator designed for planar robotic manipulators. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies, disturbances and payload perturbations. Ultimate boundedness of the tracking errors is proved, as well as boundedness of the estimation error with arbitrary precision.     

Harmonic solution of semiconductor transport equations for microwave and millimetre-wave device modelling

The hydrodynamic transport equations for charges in a semiconductor have been solved for a periodic excitation by means of a harmonic approach, in order to model microwave and millimetre-wave active devices. The solution is based on the expansion of physical variables in a Fourier series in the time domain, and on discretisation in the space domain. A waveform-balance technique in the TD is used to solve the nonlinear equations system. This approach allows for a longer time step with respect to standard TD solutions for most cases of interest, greatly reducing simulation time by at least two orders of magnitude in typical cases. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14: 36–48, 2004.     

Proteins as biomarkers of oxidative/nitrosative stress in diseases: the contribution of redox proteomics

Reactive oxygen species (ROS) and reactive nitrogen species (RNS) contribute to the pathogenesis and/or progression of several human diseases. Proteins are important molecular signposts of oxidative/nitrosative damage. However, it is generally unresolved whether the presence of oxidatively/nitrosatively modified proteins has a causal role or simply reflects secondary epiphenomena. Only direct identification and characterization of the modified protein(s) in a given pathophysiological condition can decipher the potential roles played by ROS/RNS-induced protein modifications. During the last few years, mass spectrometry (MS)-based technologies have contributed in a significant way to foster a better understanding of disease processes. The study of oxidative/nitrosative modifications, investigated by redox proteomics, is contributing to establish a relationship between pathological hallmarks of disease and protein structural and functional abnormalities. MS-based technologies promise a contribution in a new era of molecular medicine, especially in the discovery of diagnostic biomarkers of oxidative/nitrosative stress, enabling early detection of diseases. Indeed, identification and characterization of oxidatively/nitrosatively modified proteins in human diseases has just begun.     

Top-Down Preparation of Nanoquartz for Toxicological Investigations

Occupational exposure to quartz dust is associated with fatal diseases. Quartz dusts generated by mechanical fracturing are characterized by a broad range of micrometric to nanometric particles. The contribution of this nanometric fraction to the overall toxicity of quartz is still largely unexplored, primarily because of the strong electrostatic adhesion forces that prevent isolation of the nanofraction. Furthermore, fractured silica dust exhibits special surface features, namely nearly free silanols (NFS), which impart a membranolytic activity to quartz. Nanoquartz can be synthetized via bottom-up methods, but the surface chemistry of such crystals strongly differs from that of nanoparticles resulting from fracturing. Here, we report a top-down milling procedure to obtain a nanometric quartz that shares the key surface properties relevant to toxicity with fractured quartz. The ball milling was optimized by coupling the dry and wet milling steps, using water as a dispersing agent, and varying the milling times and rotational speeds. Nanoquartz with a strong tendency to form submicrometric agglomerates was obtained. The deagglomeration with surfactants or simulated body fluids was negligible. Partial lattice amorphization and a bimodal crystallite domain size were observed. A moderate membranolytic activity, which correlated with the number of NFS, signaled coherence with the previous toxicological data. A membranolytic nanoquartz for toxicological investigations was obtained.