Courseware in academic library user education: A literature review from the GAELS Joint Electronic Library Project

The use of courseware for information skills teaching in academic libraries has been growing for a number of years. In order to create effective courseware packages to support joint electronic library activity at Glasgow and Strathclyde Universities, the GAELS project conducted a literature review of the subject. This review discovered a range of factors common to successful library courseware implementations, such as the need for practitioners to feel a sense of ownership of the medium, a need for courseware customization to local information environments, and an emphasis on training packages for large bodies of undergraduates. However, we also noted underdeveloped aspects worthy of further attention, such as treatment of pedagogic issues in library computer‐aided learning (CAL) implementations and use of hypertextual learning materials for more advanced information skills training. We describe how these findings shaped the packages produced by the project and suggest ways forward for similar types of implementation

The Promise of Workplace Training for Non-College-Bound Youth: Theory and Evidence from German Apprenticeship

This paper assesses the potential of `workplace training' with reference to German Apprenticeship. When occupational matching is important, we derive conditions under which firms provide `optimal' training packages. Since the German system broadly meets these conditions, we evaluate the effectiveness of apprenticeship using a large administrative dataset. We find returns to apprenticeship for even the lowest ability school-leavers comparable to standard estimates of the return to school, and show that training is transferable across a wide range of occupations. We conclude that the positive experience with German Apprenticeship Training may guide the design of similar policies in other countries.

The Promise of Workplace Training for Non-College Bound Youth: Theory and Evidence from German Apprenticeship

This paper assesses the potential of `workplace training' with reference to German Apprenticeship. When occupational matching is important, we derive conditions under which firms provide `optimal' training packages. Since the German system broadly meets these conditions, we evaluate the effectiveness of apprenticeship using a large administrative dataset. We find returns to apprenticeship for even the lowest ability school-leavers comparable to standard estimates of the return to school, and show that training is transferable across a wide range of occupations, such as a one-digit occupation group. We conclude that the positive experience with German Apprenticeship Training may guide the design of similar policies in other countries.German Apprenticeship Training, Human Capital, Occupational Mobility, Wages.

EviPlant: An efficient digital forensic challenge creation, manipulation and distribution solution

Education and training in digital forensics requires a variety of suitable challenge corpora containing realistic features including regular wear-and-tear, background noise, and the actual digital traces to be discovered during investigation. Typically, the creation of these challenges requires overly arduous effort on the part of the educator to ensure their viability. Once created, the challenge image needs to be stored and distributed to a class for practical training. This storage and distribution step requires significant time and resources and may not even be possible in an online/distance learning scenario due to the data sizes involved. As part of this paper, we introduce a more capable methodology and system as an alternative to current approaches. EviPlant is a system designed for the efficient creation, manipulation, storage and distribution of challenges for digital forensics education and training. The system relies on the initial distribution of base disk images, i.e., images containing solely base operating systems. In order to create challenges for students, educators can boot the base system, emulate the desired activity and perform a "diffing" of resultant image and the base image. This diffing process extracts the modified artefacts and associated metadata and stores them in an "evidence package". Evidence packages can be created for different personae, different wear-and-tear, different emulated crimes, etc., and multiple evidence packages can be distributed to students and integrated into the base images. A number of additional applications in digital forensic challenge creation for tool testing and validation, proficiency testing, and malware analysis are also discussed as a result of using EviPlant.Comment: Digital Forensic Research Workshop Europe 201

A Review of automatic differentiation and its efficient implementation

Derivatives play a critical role in computational statistics, examples being Bayesian inference using Hamiltonian Monte Carlo sampling and the training of neural networks. Automatic differentiation is a powerful tool to automate the calculation of derivatives and is preferable to more traditional methods, especially when differentiating complex algorithms and mathematical functions. The implementation of automatic differentiation however requires some care to insure efficiency. Modern differentiation packages deploy a broad range of computational techniques to improve applicability, run time, and memory management. Among these techniques are operation overloading, region based memory, and expression templates. There also exist several mathematical techniques which can yield high performance gains when applied to complex algorithms. For example, semi-analytical derivatives can reduce by orders of magnitude the runtime required to numerically solve and differentiate an algebraic equation. Open problems include the extension of current packages to provide more specialized routines, and efficient methods to perform higher-order differentiation.Comment: 32 pages, 5 figures, submitted for publication. WIREs Data Mining Knowl Discov, March 201

ASPECTS OF TRAINING ECOLOGY STUDENTS USING INFORMATION AND COMMUNICATION TECHNOLOGIES

The paper discusses the areas of implementing information and communication technologies (ICT) in the educational process of training ecology students and the effect that they have on the fulfillment of innovative teaching functions with regard to the training of ecological professionals. The most efficacious information and communication technologies are pointed out. The distinguished general software tools included Internet resources, multimedia software packages, office software packages and educational software. The category of specialized software embraced geographic information systems (GIS) for various purposes, statistical software packages, software tools for analysis and forecasting of ecological processes, expert systems, information and reference systems. It was concluded that the combination of a large number of images, sound, graphic, video and animated materials by using educational e-resources is of a special importance for training ecology experts. Key words: innovative education technologies, information and communication technologies, ecological education, training of ecological experts, expert systems.

The development of «New information technologies» at the mathematical faculty of the Udmurt state university

Nowadays the students' training in the field of computational mathematics assumes obligatory acquaintance with modern methods of computational mathematics and Informatics. One of the Central places in this area is to familiarize the students acquire practical skills of work with modern packages to perform symbolic computation, and numerical calculations. Currently such packages developed quite a lot. The most popular are the following packages: MAPLE, MATHEMATICA, MATLAB, MATCAD. These packages are included in the course "New information technologies in mathematics" for 4th year students of the Udmurt State Universit

Courseware in academic library user education: a literature review from the GAELS Joint Electronic Library project

The use of courseware for information skills teaching in academic libraries has been growing for a number of years. The GAELS project was required to create a set of learning materials to support Joint Electronic Library activity at Glasgow and Strathclyde Universities and conducted a literature review of the subject. This review discovered a range of factors common to successful library courseware implementations, such as the need for practitioners to feel a sense of ownership of the medium, a need for courseware customization to local information environments, and an emphasis on training packages for large bodies of undergraduates. However, we also noted underdeveloped aspects worthy of further attention, such as treatment of pedagogic issues in library CAL implementations and use of hypertextual learning materials for more advanced information skills training. We suggest ways of improving library teaching practice and further areas of research

Co-training for Demographic Classification Using Deep Learning from Label Proportions

Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting, in which the training data consist of bags of unlabeled instances with associated label distributions for each bag. We introduce a new regularization layer, Batch Averager, that can be appended to the last layer of any deep neural network to convert it from supervised learning to LLP. This layer can be implemented readily with existing deep learning packages. To further support domains in which the data consist of two conditionally independent feature views (e.g. image and text), we propose a co-training algorithm that iteratively generates pseudo bags and refits the deep LLP model to improve classification accuracy. We demonstrate our models on demographic attribute classification (gender and race/ethnicity), which has many applications in social media analysis, public health, and marketing. We conduct experiments to predict demographics of Twitter users based on their tweets and profile image, without requiring any user-level annotations for training. We find that the deep LLP approach outperforms baselines for both text and image features separately. Additionally, we find that co-training algorithm improves image and text classification by 4% and 8% absolute F1, respectively. Finally, an ensemble of text and image classifiers further improves the absolute F1 measure by 4% on average