The Data Dilemma: Authors' Intentions and Recognition of Research Data in Educational Technology Research
- URL: http://arxiv.org/abs/2506.04954v1
- Date: Thu, 05 Jun 2025 12:29:47 GMT
- Title: The Data Dilemma: Authors' Intentions and Recognition of Research Data in Educational Technology Research
- Authors: Sandra Schulz, Natalie Kiesler,
- Abstract summary: We analyze the author's perspective provided via EasyChair where authors specified whether they had research data to share.<n>We found that not all research data was recognized as such by the authors, especially software and qualitative data.<n>This work has implications for training future generations of EdTec researchers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Educational Technology (EdTec) research is conducted by multiple disciplines, some of which annually meet at the DELFI conference. Due to the heterogeneity of involved researchers and communities, it is our goal to identify categories of research data overseen in the context of EdTec research. Therefore, we analyze the author's perspective provided via EasyChair where authors specified whether they had research data to share. We compared this information with an analysis of the submitted articles and the contained research data. We found that not all research data was recognized as such by the authors, especially software and qualitative data, indicating a prevailing lack of awareness, and other potential barriers. In addition, we analyze the 2024 DELFI proceedings to learn what kind of data was subject to research, and where it is published. This work has implications for training future generations of EdTec researchers. It further stresses the need for guidelines and recognition of research data publications (particularly software, and qualitative data).
Related papers
- CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers [3.929864777332447]
CS-PaperSum is a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences.<n>Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery.
arXiv Detail & Related papers (2025-02-27T22:48:35Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - The Nature of NLP: Analyzing Contributions in NLP Papers [77.31665252336157]
We propose a taxonomy of research contributions and introduce NLPContributions, a dataset of nearly $2k$ NLP research paper abstracts.<n>We show that NLP research has taken a winding path -- with the focus on language and human-centric studies being prominent in the 1970s and 80s, tapering off in the 1990s and 2000s, and starting to rise again since the late 2010s.<n>Our dataset and analyses offer a powerful lens for tracing research trends and offer potential for generating informed, data-driven literature surveys.
arXiv Detail & Related papers (2024-09-29T01:29:28Z) - Data-Centric AI in the Age of Large Language Models [51.20451986068925]
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs)
We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs.
We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
arXiv Detail & Related papers (2024-06-20T16:34:07Z) - Research information in the light of artificial intelligence: quality and data ecologies [0.0]
This paper presents multi- and interdisciplinary approaches for finding the appropriate AI technologies for research information.
Professional research information management (RIM) is becoming increasingly important as an expressly data-driven tool for researchers.
arXiv Detail & Related papers (2024-05-06T16:07:56Z) - SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers.
SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level.
Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
arXiv Detail & Related papers (2024-04-09T15:01:51Z) - A Survey on Data Selection for Language Models [148.300726396877]
Data selection methods aim to determine which data points to include in a training dataset.
Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive.
Few organizations have the resources for extensive data selection research.
arXiv Detail & Related papers (2024-02-26T18:54:35Z) - How Data Scientists Review the Scholarly Literature [4.406926847270567]
We examine the literature review practices of data scientists.
Data science represents a field seeing an exponential rise in papers.
No prior work has examined the specific practices and challenges faced by these scientists.
arXiv Detail & Related papers (2023-01-10T03:53:05Z) - Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature.
We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
arXiv Detail & Related papers (2022-07-18T11:38:32Z) - Research Scholar Interest Mining Method based on Load Centrality [15.265191824669555]
This paper proposes a research scholar interest mining algorithm based on load centrality.
The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node.
The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars.
arXiv Detail & Related papers (2022-03-21T04:16:46Z) - Studying the characteristics of scientific communities using
individual-level bibliometrics: the case of Big Data research [2.208242292882514]
We study the academic age, production, and research focus of the community of authors active in Big Data research.
Results show that the academic realm of "Big Data" is a growing topic with an expanding community of authors.
arXiv Detail & Related papers (2021-06-10T08:17:09Z)
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.