Teaching Literature Reviewing for Software Engineering Research
- URL: http://arxiv.org/abs/2406.08369v1
- Date: Wed, 12 Jun 2024 16:16:29 GMT
- Title: Teaching Literature Reviewing for Software Engineering Research
- Authors: Sebastian Baltes, Paul Ralph,
- Abstract summary: The goal of this chapter is to support teachers in introducing graduate students to literature reviews.
It provides an overview of the overall literature review process and the different types of literature review before diving into guidelines for selecting and conducting different types of literature review.
- Score: 10.06895600671256
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The goal of this chapter is to support teachers in holistically introducing graduate students to literature reviews, with a particular focus on secondary research. It provides an overview of the overall literature review process and the different types of literature review before diving into guidelines for selecting and conducting different types of literature review. The chapter also provides recommendations for evaluating the quality of existing literature reviews and concludes with a summary of our learning goals and how the chapter supports teachers in addressing them.
Related papers
- Teaching Survey Research in Software Engineering [1.2184324428571227]
We provide teachers with a potential syllabus for teaching survey research.
We provide actionable advice on how to teach the topics related to each learning objective.
The chapter is complemented by online teaching resources, including slides covering an entire course.
arXiv Detail & Related papers (2024-07-30T18:38:59Z) - ChatCite: LLM Agent with Human Workflow Guidance for Comparative
Literature Summary [30.409552944905915]
ChatCite is an LLM agent with human workflow guidance for comparative literature summary.
The ChatCite agent outperformed other models in various dimensions in the experiments.
The literature summaries generated by ChatCite can also be directly used for drafting literature reviews.
arXiv Detail & Related papers (2024-03-05T01:13:56Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems [7.994912533454301]
We argue for the importance of offering novel and diverse research paper recommendations to scientists.
This approach aims to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research.
arXiv Detail & Related papers (2023-09-26T14:56:56Z) - Artificial intelligence technologies to support research assessment: A
review [10.203602318836444]
This literature review identifies indicators that associate with higher impact or higher quality research from article text.
It includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers.
arXiv Detail & Related papers (2022-12-11T06:58:39Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z) - Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised
Approach [89.56158561087209]
We study summarizing on arbitrary aspects relevant to the document.
Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme.
Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
arXiv Detail & Related papers (2020-10-14T03:20:46Z) - A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021.
We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z) - Survey on Visual Sentiment Analysis [87.20223213370004]
This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis.
The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view.
A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
arXiv Detail & Related papers (2020-04-24T10:15:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.