Epistemic network analysis of students’ longer written assignments as formative/summative evaluation

Simon Skov Fougt; Amanda Siebert-Evenstone; Brendan Eagan; Sara Tabatabi; Morten Misfeldt

doi:10.1145/3170358.3170414

Epistemic network analysis of students’ longer written assignments as formative/summative evaluation

Simon Skov Fougt
, Amanda Siebert-Evenstone
, Brendan Eagan
, Sara Tabatabi
, Morten Misfeldt

School of Education, University of Madison, Wisconsin
Aalborg University

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper investigates a method of developing pedagogical visualizations of student written assignments using keyword matching and Epistemic Network Analysis (ENA) on 16 teacher students’ longer written assignments on literacy analysis of fictional texts. The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. We applied two sets of keywords. The first set with 8 was general, the second set also with 8 focused on specific literary analysis concepts. Both results show that ENA can visually distinguish low, middle and high performing students, all though all not statistically significantly. Thus, our learning analytics trial provides a tool that supports understanding subject learning.

Original language	English
Book series	Proceeding
Volume	2018
Pages (from-to)	126-130
Number of pages	5
DOIs	https://doi.org/10.1145/3170358.3170414
Publication status	Published - 7 Mar 2018
Event	Learning Analytics & Knowledge Conference 2018 - SMC Conference & Function Centre, Sydney, Australia Duration: 5 Mar 2018 → 9 Mar 2018 Conference number: 8 https://latte-analytics.sydney.edu.au/

Conference

Conference	Learning Analytics & Knowledge Conference 2018
Number	8
Location	SMC Conference & Function Centre
Country/Territory	Australia
City	Sydney
Period	05/03/18 → 09/03/18
Internet address	https://latte-analytics.sydney.edu.au/

Keywords

education, professions and jobs

Access to Document

10.1145/3170358.3170414

Cite this

@inproceedings{1ec6e87b184c430696da5d17b5eead1e,

title = "Epistemic network analysis of students{\textquoteright} longer written assignments as formative/summative evaluation",

abstract = "This paper investigates a method of developing pedagogical visualizations of student written assignments using keyword matching and Epistemic Network Analysis (ENA) on 16 teacher students{\textquoteright} longer written assignments on literacy analysis of fictional texts. The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. We applied two sets of keywords. The first set with 8 was general, the second set also with 8 focused on specific literary analysis concepts. Both results show that ENA can visually distinguish low, middle and high performing students, all though all not statistically significantly. Thus, our learning analytics trial provides a tool that supports understanding subject learning.",

keywords = "education, professions and jobs",

author = "Fougt, \{Simon Skov\} and Amanda Siebert-Evenstone and Brendan Eagan and Sara Tabatabi and Morten Misfeldt",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright is held by the owner/author(s).; Learning Analytics \& Knowledge Conference 2018, LAK18 ; Conference date: 05-03-2018 Through 09-03-2018",

year = "2018",

month = mar,

day = "7",

doi = "10.1145/3170358.3170414",

language = "English",

volume = "2018",

pages = "126--130",

journal = "Proceeding",

publisher = "Association for Computing Machinery",

url = "https://latte-analytics.sydney.edu.au/",

}

TY - GEN

T1 - Epistemic network analysis of students’ longer written assignments as formative/summative evaluation

AU - Fougt, Simon Skov

AU - Siebert-Evenstone, Amanda

AU - Eagan, Brendan

AU - Tabatabi, Sara

AU - Misfeldt, Morten

N1 - Conference code: 8

PY - 2018/3/7

Y1 - 2018/3/7

N2 - This paper investigates a method of developing pedagogical visualizations of student written assignments using keyword matching and Epistemic Network Analysis (ENA) on 16 teacher students’ longer written assignments on literacy analysis of fictional texts. The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. We applied two sets of keywords. The first set with 8 was general, the second set also with 8 focused on specific literary analysis concepts. Both results show that ENA can visually distinguish low, middle and high performing students, all though all not statistically significantly. Thus, our learning analytics trial provides a tool that supports understanding subject learning.

AB - This paper investigates a method of developing pedagogical visualizations of student written assignments using keyword matching and Epistemic Network Analysis (ENA) on 16 teacher students’ longer written assignments on literacy analysis of fictional texts. The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. We applied two sets of keywords. The first set with 8 was general, the second set also with 8 focused on specific literary analysis concepts. Both results show that ENA can visually distinguish low, middle and high performing students, all though all not statistically significantly. Thus, our learning analytics trial provides a tool that supports understanding subject learning.

KW - education, professions and jobs

UR - https://latte-analytics.sydney.edu.au/

U2 - 10.1145/3170358.3170414

DO - 10.1145/3170358.3170414

M3 - Conference article

VL - 2018

SP - 126

EP - 130

JO - Proceeding

JF - Proceeding

T2 - Learning Analytics & Knowledge Conference 2018

Y2 - 5 March 2018 through 9 March 2018

ER -

Epistemic network analysis of students’ longer written assignments as formative/summative evaluation

Abstract

Conference

Keywords

Access to Document

Other files and links

Cite this