Week-2: Project Management Tool – JIRA

Using JIRA and SCRUM for MSc Research Project Management: A Modern Alternative to Traditional Tools

Introduction

Effective project management is a cornerstone of successful postgraduate research. Yet, many students and researchers still rely on rigid, timeline-based tools such as Microsoft Project, which follow a Waterfall approach – linear, sequential, and often unsuitable for the dynamic nature of research.

In contrast, JIRA, a product by Atlassian originally designed for software engineering teams, offers a flexible, agile-based approach to managing tasks, uncertainties, and evolving priorities. When used with the SCRUM framework, JIRA can transform a Master’s research project into a structured yet adaptive workflow – ideal for data-driven and iterative fields such as Artificial Intelligence, Computer Science, and Data Analytics. Continue reading “Week-2: Project Management Tool – JIRA”

Paper#1: A Report on the First Native Language Identification Shared Task

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Paper link: A Report on the First Native Language Identification Shared Task (Tetreault, Blanchard & Cahill, 2013)

Paper Reading – First Native Language Identification (NLI) Shared Task

This post summarizes the first shared task on Native Language Identification (NLI)- predicting a writer’s native language (L1) from essays written in a learned language (here, English). It standardizes data, tasks, and evaluation to enable meaningful comparison across 29 participating teams, and remains a foundational benchmark for educational NLP and authorship profiling.

Why this matters

NLI supports targeted feedback for language learners (different L1s show distinct error tendencies) and contributes to authorship profiling. Before this effort, research relied on small, inconsistent corpora (often ICLE), making results hard to compare. This shared task fixed that by providing a large, balanced corpus and uniform evaluation.

Continue reading “Paper#1: A Report on the First Native Language Identification Shared Task”

Week-1: My Research

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Mitigating Bias in Native Language Identification Using Large Language Models and Open-Set Recognition

Introduction

Language reflects traces of our linguistic background – even when we write in a second language. My research explores this fascinating connection through the task of Native Language Identification (NLI), which aims to predict an author’s first language (L1) based on their writing in another language (L2).

While early studies relied on formal learner essays such as the TOEFL11 corpus (Tetreault et al., 2013), these datasets capture only controlled, classroom-like writing. In contrast, today’s online communication is full of informal expressions, emojis, and cultural slang. My work therefore focuses on user-generated content (UGC) – specifically the Reddit-L2 dataset (Rabinovich et al., 2018) – to study NLI “in the wild,” where text is messy but authentically human. Continue reading “Week-1: My Research”