Dharmavaram Narendranath, A., & Thelander, Z., & Hargrove-Leak, S. C. (2023, June), Measuring and Visualizing Metadiscursive Markers in Student Writing  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43894

\bibitem{asee_peer_43894}
  Dharmavaram Narendranath, A., \& Thelander, Z., \& Hargrove-Leak, S. C. (2023, June), \emph{Measuring and Visualizing Metadiscursive Markers in Student Writing}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43894

Aneet Dharmavaram Narendranath, Zachary Thelander, and Sirena C. Hargrove-Leak.  "Measuring and Visualizing Metadiscursive Markers in Student Writing".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/43894 Internet.  26 Dec, 2024

\bibitem{asee_peer_43894}
  Aneet Dharmavaram Narendranath, Zachary Thelander, and Sirena C. Hargrove-Leak.
  "Measuring and Visualizing Metadiscursive Markers in Student Writing".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/43894 Internet.  26 Dec, 2024

@INPROCEEDINGS{asee_peer_43894
author = "Aneet Dharmavaram Narendranath, Zachary Thelander, and Sirena C. Hargrove-Leak"
title = "Measuring and Visualizing Metadiscursive Markers in Student Writing"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/43894}
number = {10.18260/1-2--43894}
}

TY  - CPAPER
AB  - Meta-discourse markers (MDM) are words or phrases that help connect and organize ideas or attitudes in genre-specific written discourse.  The presence of MDM may be confirmed using rule-based or dictionary-based techniques, and their distribution may be measured to help deconstruct genre-specific discourse.  In cross-sectional or longitudinal writing analyses, such a deconstruction may help identify "something" about knowledge sharing, learning, topic or authorship attribution, and knowledge symmetries (or asymmetries).  

This paper introduces a methodology consisting of data processing and visualization at the intersection of genre analysis, statistics, dimensionality reduction, and natural language processing.  We first apply this methodology to publicly available newsgroup data, which is pre-labeled by topic to demonstrate that MDM distribution may be used to extract a visual dichotomy in the text structure of different topics.  In other words, text data about a specific topic have similar MDM distribution characteristics.  Next, we apply this methodology to unlabeled reflective essays authored by "n" students in an Engineering mechanics classroom infused with entrepreneurial mindset (EM) activities to identify if MDM distribution and clustering indicate the presence of EM. 

Future work will include exploring the confluence of MDM and rhetorical moves since we believe this will support identifying EM, metacognition, and/or achieving a threshold skill.  As part of this broader goal, we will create web-based digital tools to assess student writing and statistical regression models that would automatically classify the presence of an Entrepreneurial mindset in student writing.

AU  - Aneet Dharmavaram Narendranath
AU  - Zachary Thelander
AU  - Sirena C. Hargrove-Leak
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Measuring and Visualizing Metadiscursive Markers in Student Writing
UR  - https://peer.asee.org/43894
DO  - 10.18260/1-2--43894
ER  -