Based on the sample codes and comparison metrics provided, it appears the autoencoder is performing fairly well at identifying concepts and semantic relationships in the data, with agreement scores ranging from 0.52 to 0.85 compared to human coding. Some areas like clients and consultants show very strong agreement over 0.7. Names and design show some room for improvement around 0.5 agreement. Overall the autoencoder seems to be capturing important semantics at a level that could be useful for initial analysis or clustering, while still allowing for human validation and refinement of lower agreement areas
Personal and personalised learning and teaching (updated)
Similaire à Based on the sample codes and comparison metrics provided, it appears the autoencoder is performing fairly well at identifying concepts and semantic relationships in the data, with agreement scores ranging from 0.52 to 0.85 compared to human coding. Some areas like clients and consultants show very strong agreement over 0.7. Names and design show some room for improvement around 0.5 agreement. Overall the autoencoder seems to be capturing important semantics at a level that could be useful for initial analysis or clustering, while still allowing for human validation and refinement of lower agreement areas
Similaire à Based on the sample codes and comparison metrics provided, it appears the autoencoder is performing fairly well at identifying concepts and semantic relationships in the data, with agreement scores ranging from 0.52 to 0.85 compared to human coding. Some areas like clients and consultants show very strong agreement over 0.7. Names and design show some room for improvement around 0.5 agreement. Overall the autoencoder seems to be capturing important semantics at a level that could be useful for initial analysis or clustering, while still allowing for human validation and refinement of lower agreement areas (20)
Based on the sample codes and comparison metrics provided, it appears the autoencoder is performing fairly well at identifying concepts and semantic relationships in the data, with agreement scores ranging from 0.52 to 0.85 compared to human coding. Some areas like clients and consultants show very strong agreement over 0.7. Names and design show some room for improvement around 0.5 agreement. Overall the autoencoder seems to be capturing important semantics at a level that could be useful for initial analysis or clustering, while still allowing for human validation and refinement of lower agreement areas
1.
2.
3.
4. HOW RESEARCH INTO
EPISTEMIC NETWORK
ANALYSIS
MIGHT INFORM
DISCOURSE-CENTRIC
LEARNING ANALYTICS
David Williamson Shaffer
14. Students Teacher
How do learning
analytics and
discourse analysis
change this picture?
Lesson
Tests
Plan
15.
16.
17.
18. I would set up a experimental ultrafiltration
membrane and test the surfactants.
19. First you have to set up a controlled
experiment with no carbon nanotubes.
Record how membrane fouling acts upon the
membrane with no carbon nanotubes. Then,
slowly increase the carbon nanotube
percentage with each experiment and
compare them to the controlled experiment
along with all the experiments before it. You
should then be able to figure out what
percentage is most effective.
20. First you have to set up a controlled
experiment with no carbon nanotubes.
Record how membrane fouling acts upon the
membrane with no carbon nanotubes. Then,
slowly increase the carbon nanotube
percentage with each experiment and
compare them to the controlled experiment
along with all the experiments before it. You
should then be able to figure out what
percentage is most effective.
68. What are the necessary and sufficient
conditions of knowledge?
What makes justified beliefs justified?
69. What are the necessary and sufficient
conditions of knowledge?
What makes justified beliefs justified?
How do people
make decisions
and justify actions?
79. 1. Apply virtual communication 4. Improve the visibility of both
tools and facilitation techniques formal and informal skills.
to more effectively connect
people from around the world. 5. Use dashboards linked to
collaborative tools to capture
2. Use collaborative spaces to key realtime information about
gather knowledge, express people, activities and outcomes.
ideas and concerns and share
passions. 6. Provide more frequent
guidance and link performance
3. Spend more time on setting to recognition.
organizational context and
communicating where the
organization needs to go.
80. The environmental factors
within MMORPGs can be
75% applied to enhance
leadership effectiveness
for the globally integrated
enterprise
81.
82.
83.
84.
85.
86.
87. Game v Corporate Skills
100
80
Corporate Skills
60
40
20
0
0 20 40 60 80 100
Game Skills
88. The skills and techniques
61%
of the gaming leader
cannot be transferred
directly to the leadership
of the virtual corporation
89. Complex thinking happens in
cultures of practice
Cultures of practice are characterized by their
patterns of Discourse
103. Complex thinking happens in
cultures of practice
Cultures of practice are characterized by their
patterns of Discourse
Patterns of Discourse can be described in terms
of an epistemic frame
218. Complex thinking happens in
cultures of practice
Cultures of practice are characterized by their
patterns of Discourse
Patterns of Discourse can be described in terms
of an epistemic frame
An epistemic frame can be quantified using a
network model (ENA)
230. K2
Equiload
S1 x K2 Projection
S1 x V2
S1
V2
S1 x K1
K1
231.
232. Player 1: Good Morning
Player 2: Hello everyone, is this
chat with our new
group now?
Player 1: I think so
Player 3: Probably
Player 1: Good morning new
team. I excited to work
with with everyone.
Player 4: hey
Player 1: We are meeting and
currently discussing
our various materials.
233. Player 1: Good Morning
Player 2: Hello everyone, is this
chat with our new
group now?
Player 1: I think so
Player 3: Probably
Player 1: Good morning new
team. I excited to work
with with everyone.
Player 4: hey
Player 1: We are meeting and
currently discussing
our various materials.
234.
235. Player 1: We ranked the different
materials based on the
device specifications and
our experimental data.
Player 2: And we just need to
decide on the 5th team
experimental device.
Player 3: We looked at… how the
cost changed with each
matieral while controling
everything else
Player 4: hey team, I am going to
post the rankings of the
membranes as a public
document so everyone
can see it.
265. Complex thinking happens in
cultures of practice
Cultures of practice are characterized by their
patterns of Discourse
Patterns of Discourse can be described in terms
of an epistemic frame
An epistemic frame can be quantified using a
network model (ENA)
ENA can model complex thinking
285. Student
Chat Auto
Student
Hub Mentor
Student
Suggest Using ENA in games
to create
fummative assessments
Speech
Formative
Act
Utterance
Table
ENA
Playbook
Summative
286. Student
Chat Auto
Student
Hub Mentor
Student
Suggest Using ENA in games
to create
sormative assessments
Speech
Formative
Act
Utterance
Table
ENA
Playbook
Summative
287. Auto
Mentor
Using ENA in games
to create
sormative assessments
288. Art
Graesser Auto
Mentor
Using ENA in games
to create
sormative assessments
289. Art
Graesser Auto
Mentor
Andre
Rupp Using ENA in games
to create
sormative assessments
290. Art
Graesser Auto
Mentor
Andre
Rupp Using ENA in games
to create
sormative assessments
Kris
Scopinich
291. Art
Graesser Auto
Mentor
Andre
Rupp Using ENA in games
to create
sormative assessments
Kris
Scopinich
Asligul
Goḉmen