27,413 research outputs found
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
Describing typeforms: a designer's response
The paper sets out an overview of a pragmatic research investigation initiated within a doctoral enquiry, and which continues to inform design practice and pedagogy. Located within the fields of typography and information design, and very much concerned with design history, enquiry emphasized exploration of alternative design research methodologies in the production of a design outcome loaded with pedagogical ambition.
The issue being addressed within the investigation was the limited scope of existing typeface classificatory systems to adequately describe the diversity of forms represented within current type design practice and thus, recent acquisitions to an established teaching collection in London.
Addressing this issue unexpectedly came to utilize the researcher’s own design practice as a methodology for managing emergent enquiry, and for organizing and generating new knowledge through the employment of visual information management methods.
A primary outcome of the enquiry was a new framework for the description of typeforms. This new framework will be described in terms of its operation, divergence from existing models and potential for application
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Bias in textual data can lead to skewed interpretations and outcomes when the
data is used. These biases could perpetuate stereotypes, discrimination, or
other forms of unfair treatment. An algorithm trained on biased data ends up
making decisions that disproportionately impact a certain group of people.
Therefore, it is crucial to detect and remove these biases to ensure the fair
and ethical use of data. To this end, we develop a comprehensive and robust
framework \textsc{Nbias} that consists of a data layer, corpus contruction,
model development layer and an evaluation layer. The dataset is constructed by
collecting diverse data from various fields, including social media,
healthcare, and job hiring portals. As such, we applied a transformer-based
token classification model that is able to identify bias words/ phrases through
a unique named entity. In the assessment procedure, we incorporate a blend of
quantitative and qualitative evaluations to gauge the effectiveness of our
models. We achieve accuracy improvements ranging from 1% to 8% compared to
baselines. We are also able to generate a robust understanding of the model
functioning, capturing not only numerical data but also the quality and
intricacies of its performance. The proposed approach is applicable to a
variety of biases and contributes to the fair and ethical use of textual data.Comment: Under revie
Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
Bias detection in text is imperative due to its role in reinforcing negative
stereotypes, disseminating misinformation, and influencing decisions. Current
language models often fall short in generalizing beyond their training sets. In
response, we introduce the Contextualized Bi-Directional Dual Transformer
(CBDT) Classifier. This novel architecture utilizes two synergistic transformer
networks: the Context Transformer and the Entity Transformer, aiming for
enhanced bias detection. Our dataset preparation follows the FAIR principles,
ensuring ethical data usage. Through rigorous testing on various datasets, CBDT
showcases its ability in distinguishing biased from neutral statements, while
also pinpointing exact biased lexemes. Our approach outperforms existing
methods, achieving a 2-4\% increase over benchmark performances. This opens
avenues for adapting the CBDT model across diverse linguistic and cultural
landscapes.Comment: UNDER REVIE
A framework for understanding the factors influencing pair programming success
Pair programming is one of the more controversial aspects of several Agile system development methods, in particular eXtreme Programming (XP). Various studies have assessed factors that either drive the success or suggest advantages (and disadvantages) of pair programming.
In this exploratory study the literature on pair programming is examined and factors distilled. These factors are then compared and contrasted with those discovered in our recent Delphi study of pair programming.
Gallis et al. (2003) have proposed an initial framework aimed at providing a comprehensive identification of the major factors impacting team programming situations including pair programming. However, this
study demonstrates that the framework should be extended to include an additional category of factors that relate to organizational matters. These factors will be further refined, and used to develop and empirically evaluate a conceptual model of pair programming (success)
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