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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
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
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)
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
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
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
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)
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
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).
References
11
References
Analyzing interactivity in asynchronous video discussions, H. Rothe, J. Sundermeier, M. Gersch, June 27th 2014, HCII2014
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research. Journal of management information systems 24(3): 45–77
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Instructional Media 35(1): 63
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Internet and Higher Education 10(1): 15–25
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solving. Educational technology research and development 49(1): 35–51
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environments. Quarterly Review of Distance Education 2(2): 93–104
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Computers & Education 54(4): 951–961
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Education Edition. http://www.nmc.org/publications/2013-horizon-report-higher-ed. Accessed 15 Feb 2013
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Analyzing Interactivity in asynchronous video Discussions

  • 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).
  • 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