The document discusses analyzing interactivity in asynchronous video discussions. It presents indicators to measure interactivity from a consumer perspective, including the ratio of videos viewed after and before posts. It demonstrates these indicators by analyzing data from an asynchronous video discussion with 140 students discussing the net economy. It concludes more research is needed to understand how interactivity indicators relate to learning outcomes and to develop models for selecting appropriate indicators based on learning contexts.

1. Analyzing interactivity in
asynchronous video
discussions
Hannes Rothe
Janina Sundermeier
Martin Gersch
Department Business Information Systems
Research Paper Presentation at the International
Human-Computer-Interaction Conference (HCII),
Heraklion, Greece, June 27th 2014

2. 2
I. Problem Definition
-- Indicators for interactivity in asynchronous
online discussions
II. Artifact
-- Developing of Indicators for a ‚consumer
perspective‘
III. Demonstration
-- Measuring interactivity within asynchronous
video discussion in Pinio
IV. Conclusion
Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
Agenda
Interactivity in asynchronous online discussions
Identify
problem &
motivate
Define
objectives
of a
solution
Design
&
develop
artifact
Demon-
stration
Evaluation

3. 3Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
I. Problem definition
Updating Dringus and Ellis Indicators for interactivity in Online Discussions
Are there key Indicators for measuring
interactivity automatically in Online Discussions?
Active
(‚producer perspective‘)
Amount of posts, words, sentences created
Time difference between posts
Amount of reviews or edits
Changes of subjects, Keywords, phrases, topics
Number of responses
Centrality of post, density of a discussion
Passive
(‚consumer perspective‘)
Number of posts read or ‚scanned‘
(estimated by time span)
Amount of website hits
Posts marked as ‚read‘
Only few papers mention this
perspective and measure it very
indirectly
‘[T]he associated indicators are found in the literature, but only in piecemail’.
(Dringus & Ellis 2005)

4. 4Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
I. Asynch. Video Discussions
The Technology: Pinio
Usage or Pinio
in our course
2nd week:
Mandatory
participation
3rd week:
Discussion
initiated by lecturer
4th week:
No initiation by
lecturer
10th week:
Presentation of
case study with
mandatory
participation
Every person has 30 seconds for a video statement.
Statements are complemented by facial expressions
Replies start with agree or disagree

5. 5Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
II. Methodology
Design Science Research…
Problem: How can we use data mining to evaluate online
discussions against the background of a multifaceted
view on interactivity?
…follows the procedure by Peffers et al. 2007
Identify
problem &
motivate
Define
objectives
of a
solution
Design
&
develop
artifact
Demon-
stration
Evaluation
Artifact: Design of indicators for a ‚consumer perspective‘ in
online discussions

6. 6Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
II. Artifact
Quantifiable Indicators for the ‘Consumer Perspective’
viewed comments (v) in a discussion (D) prior to a statement (px) at time (t).




T
t p
T
t v
rva
x
tD
x
tD
x
1
1
,
,
Ratio of Videos viewed
after a post





1
1
,
,
x
o
tD
x
o
tD
x
t
t p
t
t v
rvp
Ratio of Videos viewed
prior to a post
30s
time

7. 7Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
III. Demonstration
The Case Scenario: Net Economy
In 2013: 140 Students (from Germany, Ukraine, Indonesia)

8. 8Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
III. Demonstration
A ‘Consumer Perspective’ for Video Discussions
270 video statements
6438 videos watched

9. 9Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
IV. Conclusion
Conclusive Remarks and Future Research
There is no either/or between a consumer and
producer perspective (it is a continuum)
A Key Indicator has not been found, yet.
Initial results lead to a more nuanced understanding
of interactivity. Indicators need to be chosen, based
on the context.
Future Research
I currently design a Procedural
model to select indicators from
Learning Analytics for specific
learning scenarios.
Initial results of a cluster
analysis shows three types of
learners.
How does this effect the
learning outcome?
Thank you for your attention. You may find me online on
twitter (@wingsoft) or LinkedIn (Hannes Rothe).

10. References

11. 11
References
Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
1. Bures EM, Abrami PC, Amundsen C (2000) Student motivation to learn via computer conferencing. Research in higher
Education 41(5): 593–621
2. Swan K, Shea P (2005) The development of virtual learning communities. Learning together online: Research on
asynchronous learning networks: 239–260
3. Weber P, Rothe H (2013) Social networking services in e-learning. In: Bastiaens T, Marks G (eds) Education and
Information Technology 2013: A Selection of AACE Award Papers, AACE, vol 1. Chesapeake, pp. 89–99
4. Kear K (2004) Peer learning using asynchronous discussion systems in distance education. Open Learning: The Journal
of Open, Distance and e-Learning 19(2): 151–164
5. Hammond M (2005) A review of recent papers on online discussion in teaching and learning in higher education. Journal
of Asynchronous Learning Networks 9(3): 9–23
6. Cheng CK, Paré DE, Collimore L et al. (2011) Assessing the effectiveness of a voluntary online discussion forum on
improving students’ course performance. Computers & Education 56(1): 253–261
7. Webb E, Jones A, Barker P et al. (2004) Using e-learning dialogues in higher education. Innovations in Education and
Teaching International 41(1): 93–103
8. Jyothi S, McAvinia C, Keating J (2012) A visualisation tool to aid exploration of students’ interactions in asynchronous
online communication. Computers & Education 58(1): 30–42
9. Wise AF, Perera N, Hsiao Y et al. (2012) Microanalytic case studies of individual participation patterns in an
asynchronous online discussion in an undergraduate blended course. The Internet and Higher Education 15(2): 108–
117
10.Beaudoin MF (2002) Learning or lurking?: Tracking the “invisible” online student. The Internet and Higher Education
5(2): 147–155
11.Tobarra L, Robles-Gómez A, Ros S et al. (2014) Analyzing the students’ behavior and relevant topics in virtual learning
communities. Computers in Human Behavior 31: 659–669
12.Dringus LP, Ellis T (2005) Using data mining as a strategy for assessing asynchronous discussion forums. Computers &
Education 45(1): 141–160
13.Peffers K, Tuunanen T, Rothenberger MA et al. (2007) A design science research methodology for information systems
research. Journal of management information systems 24(3): 45–77
14.Harasim L (2000) Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education
3(1): 41–61
15.Kaye A (1989) Computer-mediated communication and distance education. In: Mason R, Kaye A (eds) Mindweave:
Communication, computers, and distance education. Pergamon, New York

12. 12
References
Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
16.Gibbs W, Simpson LD, Bernas RS (2008) An analysis of temporal norms in online discussions. International Journal of
Instructional Media 35(1): 63
17.Woo Y, Reeves TC (2007) Meaningful interaction in web-based learning: A social constructivist interpretation. The
Internet and Higher Education 10(1): 15–25
18.Jonassen DH, Kwon II H (2001) Communication patterns in computer mediated versus face-to-face group problem
solving. Educational technology research and development 49(1): 35–51
19.Prestera GE, Moller LA (2001) Exploiting opportunities for knowledge-building in asynchronous distance learning
environments. Quarterly Review of Distance Education 2(2): 93–104
20.Peters VL, Hewitt J (2010) An investigation of student practices in asynchronous computer conferencing courses.
Computers & Education 54(4): 951–961
21.Johnson L, Adams Becker S, Cummins M, Estrada V, Freeman A, Ludgate H (2013) NMC Horizon Report: 2013 Higher
Education Edition. http://www.nmc.org/publications/2013-horizon-report-higher-ed. Accessed 15 Feb 2013
22.Campbell JP, DeBlois PB, Oblinger DG (2007) ACADEMIC ANALYTICS. Educause Review 42(4): 40–57
23.Elias T (2011) Learning Analytics: Definitions, Processes and Potential.
http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf
24.Siemens G, Long P (2011) Penetrating the fog: Analytics in learning and education. Educause Review 46(5): 30–32
25.Romero C, Ventura S (2007) Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications
33(1): 135–146
26.Romero-Zaldivar V, Pardo A, Burgos D et al. (2012) Monitoring student progress using virtual appliances: A case study.
Computers & Education 58(4): 1058–1067
27.Bernard RM, Abrami PC, Borokhovski E et al. (2009) A meta-analysis of three types of interaction treatments in distance
education. Review of Educational Research 79(3): 1243–1289
28.Moore MG (1989) Editorial: Three types of interaction. American Journal of Distance Education 3(2): 86–89
29.Hamuy E, Galaz M (2010) Information versus communication in course management system participation. Computers &
Education 54(1): 169–177
30.Mazzolini M, Maddison S (2007) When to jump in: The role of the instructor in online discussion forums. Computers &
Education 49(2): 193–213
31.Thomas MJW (2002) Learning within incoherent structures: The space of online discussion forums. Journal of Computer
Assisted Learning 18(3): 351–366
32.Bayer J, Bydzovská H, Géryk J, Obšıvac T, & Popelınský L (2012) Predicting drop-out from social behaviour of students.
Proceedings of the 5th International Conference on Educational Data Mining

13. 13
References
Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
33.Romero C, López M, Luna J et al. (2013) Predicting students’ final performance from participation in on-line discussion
forums. Computers & Education 68(0): 458–472. doi: 10.1016/j.compedu.2013.06.009
34.Thomas MJW (2002) Learning within incoherent structures: The space of online discussion forums. Journal of Computer
Assisted Learning 18(3): 351–366
35.Hung J, Zhang K (2008) Revealing online learning behaviors and activity patterns and making predictions with data
mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching
36.Kumar V, Chadha A (2011) An Empirical Study of the Applications of Data Mining Techniques in Higher Education.
International Journal of Advanced Computer Science and Applications 2(3): 80–84
37.Lin F, Hsieh L, Chuang F (2009) Discovering genres of online discussion threads via text mining. Computers &
Education 52(2): 481–495
38.Ebner M, Holzinger A, Catarci T (2005) Lurking: An underestimated human-computer phenomenon. Multimedia, IEEE
12(4): 70–75
39.Lehr C (2011) Web 2.0 in der universitären Lehre. http://www.diss.fu-
berlin.de/diss/receive/FUDISS_thesis_000000035056, received at 2nd February 2014