Visual Literacy and New Technologies

This body of research addresses the connection between arts, identity and new technology, and investigates the impact of images on adolescent identities, the relationship between online modes of communication and cyber-bullying, the increasing visualization of information and explores the way drawing and critical analysis of imagery develops visual literacy. Commissioned by Adobe Systems Pty Ltd, Australia (2003) to compile the Visual Literacy White Paper, Bamford’s report defines visual literacy and highlights its importance in the learning of such skill as problem solving and critical thinking. Providing strategies to promote visual literacy and emphasizing the role of technology in visual communication, this report has become a major reference for policy on visual literacy and cyber-bullying in the UK, USA and Asia

After-School Care and Parents' Labor Supply

Does after-school care provision promote mothers' employment and balance the allocation of paid work among parents of schoolchildren? We address this question by exploiting variation in cantonal (state) regulations of after-school care provision in Switzerland. To establish exogeneity of cantonal regulations with respect to employment opportunities and preferences of the population, we restrict our analysis to confined regions along cantonal borders. Using semi-parametric instrumental variable methods, we find a positive impact of after-school care provision on mothers' full-time employment, but a negative impact on fathers' full-time employment. Thus, the supply of after-school care fosters a convergence of parental working hours

After-School Care and Parents' Labor Supply

Does after-school care provision promote mothers' employment and balance the allocation of paid work among parents of schoolchildren? We address this question by exploiting variation in cantonal (state) regulations of after-school care provision in Switzerland. To establish exogeneity of cantonal regulations with respect to employment opportunities and preferences of the population, we restrict our analysis to confined regions along cantonal borders. Using semi-parametric instrumental variable methods, we find a positive impact of after-school care provision on mothers' full-time employment, but a negative impact on fathers' full-time employment. Thus, the supply of after-school care fosters a convergence of parental working hours

Overeducation and spatial flexibility in Italian local labour markets

According to a recent strand of literature this paper highlights the relevance of spatial mobility as an explanatory factor of the individual risk of being overeducated. To investigate the causal link between spatial mobility and overeducation we use individual information about daily home-to-work commuting time and choices to relocate in a different local area to get a job. In our model we also take into account relevant local labour markets features. We use a probit bivariate model to control for selective access to employment, and test the possibility of endogeneity of the decision to migrate. Separate estimations are run for upper-secondary and tertiary graduates. The results sustain the appropriateness of the estimation technique and show a significantly negative impact of the daily commuting time for the former group, as well as, negative impact of the decision to migrate and of the migration distance for the latter one.Overeducation; Spatial flexibility; Local labour markets; Sample selection bias

Several categories of Large Language Models (LLMs): A Short Survey

Large Language Models(LLMs)have become effective tools for natural language processing and have been used in many different fields. This essay offers a succinct summary of various LLM subcategories. The survey emphasizes recent developments and efforts made for various LLM kinds, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models. The survey gives a general summary of the methods, attributes, datasets, transformer models, and comparison metrics applied in each category of LLMs. Furthermore, it highlights unresolved problems in the field of developing chatbots and virtual assistants, such as boosting natural language processing, enhancing chatbot intelligence, and resolving moral and legal dilemmas. The purpose of this study is to provide readers, developers, academics, and users interested in LLM-based chatbots and virtual intelligent assistant technologies with useful information and future directions

Automated Assessment of Students' Code Comprehension using LLMs

Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based models such as Large Language Models(LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks, evaluating LLMs in the realm of automated answer assesment has not received much attention. To address this gap, we explore the potential of using LLMs for automated assessment of student's short and open-ended answer. Particularly, we use LLMs to compare students' explanations with expert explanations in the context of line-by-line explanations of computer programs. For comparison purposes, we assess both Large Language Models (LLMs) and encoder-based Semantic Textual Similarity (STS) models in the context of assessing the correctness of students' explanation of computer code. Our findings indicate that LLMs, when prompted in few-shot and chain-of-thought setting perform comparable to fine-tuned encoder-based models in evaluating students' short answers in programming domain

Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing

The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of encoder-only language models and the decoder-only language models. Our findings reveal that while some decoder-only LLMs demonstrate notable performance across most financial tasks via zero-shot prompting, they generally lag behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study provides foundation evaluations for continuing efforts to build more advanced LLMs in the financial domain.Comment: Findings of EMNLP 2023 (short paper