1,721,012 research outputs found
Computing in School in the UK & Ireland: A Comparative Study
Get PDFMany countries have increased their focus on computing in primary and secondary education in recent years and the UK and Ireland are no exception. The four nations of the UK have distinct and separate education systems, with England, Scotland, Wales, and Northern Ireland offering different national curricula, qualifications, and teacher education opportunities; this is the same for the Republic of Ireland. This paper describes computing education in these five jurisdictions and reports on the results of a survey conducted with computing teachers. A validated instrument was localised and used for this study, with 512 completed responses received from teachers across all five countries The results demonstrate distinct differences in the experiences of the computing teachers surveyed that align with the policy and provision for computing education in the UK and Ireland. This paper increases our understanding of the differences in computing education provision in schools across the UK and Ireland, and will be relevant to all those working to understand policy around computing education in school
Reimagining Student Success Prediction: Applying LLMs in Educational AI with XAI
No full textSince the conception of Large Language Models (LLMs), their areas of application have increased significantly over time. This is due to their nature of being able to perform natural language processing (NLP) tasks (like question answering, text generation, text summarization, text classification etc.), which gives them flexibility in a multitude of spaces, including in Educational AI (EdAI). Despite their incredible wide range of use, LLMs are typically applied to generative AI, from text to image generation.
This paper aims to apply LLMs for a classification task in EdAI, by reproposing the original PreSS (Predicting Student Success) model which makes use of more traditional Machine Learning (ML) algorithms for predicting CS1 students at risk of failing or dropping out. There are two main goals for this work: the first is to identify the best and most accurate method to re-purpose LLMs for a classification task; the second is to explore and access the explainability of the model outputs. For the former we investigate different techniques for using LLMs like Few-Shot Prompting, Fine-Tuning and Transfer Learning using Gemma 2B as base model along with two different kind of prompting techniques. For the latter we focus on attention scores of LLMs transformers, aiming to understanding what are the most important features that the model considers for generating the response. The obtained results are then compared with the previous PreSS model to evaluate whether LLMs can outperform traditional ML algorithms: this paper finds that Naïve Bayes still outperforms all the others, once again confirmed as the best algorithm for predicting student success
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A Review of International Models of Computer Science Teacher Education
Get PDFThroughout the world, Computer Science Education (CSE) has ex- panded exponentially over the past decade, focused on teaching primary and secondary students computing ideas and tools. To teach all these students computer science (CS), models for teacher preparation range from one and done professional learning work- shops to full certificate and licensure programs. This report provides a landscape of how CS teachers are prepared academically in var- ious countries and makes evidence-based recommendations for how teachers should be educated to develop knowledge and skill to teach computer science. It also discusses how to develop these knowledge systems while promoting instruction that is equitable and centers students in the classroom. We brought together a group of international computer science education scholars who have been engaged in teacher preparation. In addition to what knowl- edge teachers need to teach CS, we also focused on how the field is preparing teachers and the role of computer science in the design of technology tools to achieve goals while mitigating potential societal harms
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Diverging Assessment: A Student Perspective
Get PDFDiverging assessment maintains a common question set for all students but varies the input data so that each student has a unique problem to solve. It is an approach in student assessment that offers a unique and authentic learning experience. Although such assessments have been implemented in computing courses, their effectiveness and students’ perceptions in different contexts remain unexplored. In this paper, we investigate student perspectives on diverging assessment. We surveyed students in four courses across three different universities. Each surveyed student was enrolled in one of the four courses on networking, operation systems, digital forensics or ethical hacking. Each course featured at least one diverging assessment. The students’ overall perceptions about diverging assessments and three different aspects of diverging assessment, namely authenticity, assessment-as-learning, and academic integrity, are surveyed, reported, and analyzed
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