April 2012 Volume 15 Number 2 - Educational Technology & Society

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involved in other Twitter networks through the use of hash-tags. They highlight that not everyone was connected, represented in the outlying participants in Figure 1, but that some were highly connected in their extensive use of hash-tag networks. This proved to increase the streaming of serendipitous information onto the PLENK2010 network from other networks. The networks which contacts were engaged in were also relevant to the information aggregator, but relevant one step removed, with the potential of “unexpected relevance,” as Jarvis calls serendipity (Jarvis, 2010). Increasing the level of serendipity on the learning network These are only some basic indications of the positive effect of micro-blogging tool Twitter on the level of serendipity in open networked learning and that serendipity might be increased through communications with other people. If we were to define what would heighten a “degree of serendipity” on a learning network, it would be using the following factors: 1. The level of control over the information gathering process. 2. The involvement of people. Not only the number of contacts would be important, but also the degree of separation of the contacts and networks from the information gatherer, as it seems a higher level of serendipity is achieved if the information provider is somewhat removed from the information collector, while a higher level of relevance is realized from contacts closer to the collector. 3. The aggregation of information feeds. Not only is the number of feeds important to achieve serendipity, but also the variation of these feeds. One would expect that the higher the level of control, and the higher the level of micro-blogging re-tweets, #tags networks involved in, and the spread of RSS feeds would be, the higher the likelihood of unexpected and challenging pieces of information and serendipity in the aggregator’s information stream. This level of unexpectedness would also depend on the right level of distance between the aggregator and his contacts or feed/link providers. If the distance is too great, the information becomes too random and could become irrelevant, but if it is very close the level of unexpectedness shrinks. Especially receiving re-tweets would heighten serendipity as these tweets would be provided by contacts of contacts, so still be reasonably close to the aggregator. In the absence of an educator we would welcome a high level of serendipity in the networked learning environment as it would add to the level of critical reflection and analysis and depth of learning. Conclusion This paper has highlighted some challenges and opportunities for learners in dealing with the abundance of information available at a time when new technologies are emerging and shaping the new information landscape. The role of the educator might slowly but surely change, from provider of information to provider of guidance about technology used to aggregate information in the most suitable fashion to advance learning. Technologies are now available to empower the learner to take control over the information steam him or herself and start to be proactive in the quest for valuable information. In the words of Boyd: “As we continue to move from a broadcast model of information to a networked one, we will continue to see a reworking of the information landscape. Some of what is unfolding is exciting; some is terrifying” (Boyd, 2010, p. 2), but it is clear that to make the most of emerging technologies learners will have to take an active role in shaping their own learning environment and in controlling information flows and communication tools. An area for future inquiry would be to research more in-depth how emerging technologies can help people with the process of shaping their kaleidoscope of information most effectively. We need to know how people position themselves at the center of this process and how they can ensure that it provides complex, colorful and shifting patterns that are shaped by human connections and interactions, in order to at times surprise and challenge them. Empirical research to further test a ‘serendipity index’ based on micro-blogging features and RSS feeds might help in predicting the level and increase of serendipity in the information stream. It will also be important to research how levels of serendipity can be heightened in information gathering and especially if this will at all be possible by automated means through recommender systems. Even though attempts are being made to include serendipity in these new technologies, it seems that this can only be achieved at a very rudimentary level and that recommenders currently mainly provide information relevant to a search term used. The use of RSS and micro-blogging tools, such as Twitter, seem to be more promising in dealing with high volumes of 9
information as human mediation means that a higher level of serendipitous information is being collected than would be possible in an automated environment. References Andre, P., Schraefel, M. C., Teevan, J., & Dumais, S. T. (2009, October). Discovery is never by chance: designing for (un) serendipity. Paper presented at C&C09, Berkeley, California, USA. Barabasi, A. (2003). Linked: How everything is connected to everything else and what it means. New York: Penguin Books. Barnett, R. (2002). Learning to work and working to learn. In F. Reeve et al. (Eds.), Supporting lifelong learning Vol.2, organising learning (pp. 7-20). London: Routledge Falmer. Bouchard, P. (2011). The impact of social networks on teaching and learning [monograph]. Retrieved March 20, 2012, from, from Concordia University, Graduate Program Director of Adult Education website: http://bit.ly/f4qr2a. Bloch, E. (2011, January 24). Have we reached a world of infinite information? Retrieved March 20, 2012, from, http://bit.ly/dRXW6U. Boyd, D. (2010). Streams of content, limited attention: The flow of information through social media. EDUCAUSE Review, 45(5), 26–36. Boyd, D., Golder, S., & Litan, G. (2010, January). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. Paper presented at the 43th Hawaii International Conference on Sytem Sciences, Kauai, Hawaii. Buchem, I. (2010). Serendipitous learning: Recognizing and fostering the potential of microblogging. Formare Open Journal, 73. Retrieved March 20, 2012, from, http://scr.bi/k9sJAu. CIBER (2008). Information behaviour of the researcher of the future. Retrieved March 20, 2012, from, http://bit.ly/bRYGC Downes, S. (2004, February 1). RSS: Grassroots support leads to mass appeal [Web log post]. Retrieved March 20, 2012, from, http://bit.ly/fysFrj. Experian Hitwise (2010). Experian Hitwise reports Google monthly share of searches at 72 percent in September. Retrieved March 20, 2012, from, http://bit.ly/dAdIzZ. Falconer, A. (2010, April 9). Re: Serendipity is unexpected relevance [Web log comment]. Retrieved March 20, 2012, from, http://bit.ly/cPRp2P. Fisher, K., & Naumer, C. (2006). Information grounds: Theoretical basis and empirical findings on information flow in social settings. In A. Spink & C. Cole (Eds.), New directions in human information behaviour, Amsterdam, Kluwer, 93-111. Folke, C. (2010, January 26). How much disturbance can a system withstand? With roots in ecology and complexity science, resilience theory can turn crises into catalysts for innovation. Seed Magazine. Retrieved March 20, 2012, from, http://bit.ly/hvJl8F. Foster, A., & Ford, N. (2003). Serendipity and information seeking: An empirical study. Journal of Documentation, 59(3), 321- 340. Goldman, E. (2010). Search engine bias & the demise of search engine utopianism. In B. Szoka & A. Marcus (Eds.), The next digital decade: Essays on the future of the Internet (pp. 461-474). Washington, DC: TechFreedom. Granovetter, M. S. (1973). The strength of weak ties. The American Journal of Sociology, 78(6), 1360–1380. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420-1443. Grimmelmann, J. (2010). Some skepticism about search neutrality. In B. Szoka & A. Marcus (Eds), The next digital decade: Essays on the future of the Internet (pp. 435-460). Washington, DC: TechFreedom. Gritton, J. (2007). Of serendipity, free association and aimless browsing: Do they lead to serendipitous learning? Retrieved March 20, 2012, from, http://bit.ly/nVzteF. Hagel, J. (2006, December 5). The economics of attention. Retrieved March 20, 2012, from, http://bit.ly/HxRRm. Hurson, T. (2007). IdeaConnection, interview with Tim Hurson. Retrieved March 20, 2012, from, http://bit.ly/kd5hYj. Jarvis, J. (2010, March 30). Serendipity is unexpected relevance. Retrieved March 20, 2012, from, http://bit.ly/cPRp2P. Katz, E., & Lazarsfeld, P. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: The Free Press. 10
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Page 3 and 4: Supporting Organizations Centre for
Page 5 and 6: Contextualizing a MALL: Practice De
Page 7 and 8: Kop, R. (2012). The Unexpected Conn
Page 9 and 10: Human mediation and information flo
Page 11 and 12: elevant information based on some k
Page 13: of learners. A search facilitated t
Page 17 and 18: Blau, I., & Barak, A. (2012). How D
Page 19 and 20: examined whether the readiness to p
Page 21 and 22: Table 5 presents the ANOVA for the
Page 23 and 24: However, in discussing a non-sensit
Page 25 and 26: deviations showed smaller variance,
Page 27 and 28: actual participation discussing sen
Page 29 and 30: Mesch, G., & Elgali, Z. (2009). Soc
Page 31 and 32: Although prior research on overall
Page 33 and 34: with a score of 39 and below as “
Page 35 and 36: Table 2 demonstrates mean scores an
Page 37 and 38: Discussion Although the recent Inte
Page 39 and 40: Furthermore, the most preferred pla
Page 41 and 42: MEB. (2009). Internete erisim proje
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Page 45 and 46: practice and repetition with feedba
Page 47 and 48: Online peer assessment Peer assessm
Page 49 and 50: At an international conference on m
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Page 55 and 56: Tsai, S. C. (2012). Integration of
Page 57 and 58: order to decrease text complexity a
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een analyzed and were compared with
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Chen, G.-D., Lee, J.-H., Wang, C.-Y
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and diverse cognitional and emotion
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Encourage and persuade students wit
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Materials and procedure Each studen
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The lower part of Figure 4 shows st
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Mayer, R. E., Sobko, K., & Mautone,
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that an item is answered correctly
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Table 1. Relative concepts frequenc
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The Account Management Module provi
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Experiment 1 and results Figure 7.
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Utilization of Test Item 36 33 30 2
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Utilization of Test Item 36 33 30 2
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0.065 0.877 (17.56) 0.938 (22.55) 0
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Hwang, G.J., Hsiao, C.L., & Tseng,
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the diverse needs of modern industr
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Figure 1. The framework of interdis
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Research Design Figure 3. Recommend
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friendly. 8. I feel the layout and
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satisfaction, and system acceptance
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Ozok, A. A., Fan, Q., & Norcio, A.
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than students in a conventional cla
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shown in Figure 2 to include the st
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(1996) Pleasure-Arousal-Dominance (
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Based on this connection, we consid
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Final dominance measurements were p
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student mood and thereby allow comp
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Sessink, O., Beeftink, H., Tramper,
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The rationale behind the video form
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usefulness of the movies and their
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case of the first two questions and
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this paper in order to evaluate the
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The survey questions Appendix A In
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E-training in organizations E-train
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efficacy regarding online training,
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Method Sample and procedure Figure
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0.874 which exceeded the recommende
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Even though the result does not con
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Brown, M., & Cudeck, R. (1993). Alt
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Wen, Y., Looi, C.-K., & Chen, W. (2
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when they are designing and impleme
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paper focuses on the first three st
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their self-esteem to be partners in
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Democratizing knowledge and symmetr
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1) Reading the article: At the star
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Effects of RCKI principle-based ped
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References Alexander, C. (1979). Th
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Valsamidis, S., Kontogiannis, S., K
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The Analog system (Yan et al., 1996
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First, the number of the sessions a
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BioLayout uses a modified version o
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As shown in figure 3, all metrics c
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Figure 7. The detailed results for
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Enright, A. J., van Dongen, S., Ouz
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Chu, S. K. W., Kwan, A. C. M., & Wa
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teachers, as well as collaboration
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from the students’ ratings appear
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Table 5 summarizes the students’
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eading blogs (i.e., reminders, easy
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Luehmann, A. L. (2008). Using blogg
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Uzunboylu et al. (2009) examined th
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System implementation Overview of M
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Methods Before the experiment, a mo
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too simple, incomplete, or they eve
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Comparisons of UMLS Questionnaire i
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already described may play an impor
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Parker, D., Manstead, A. S. R., Str
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Avci, U., & Askar, P. (2012). The C
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Literature review The related varia
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collected through Personal Informat
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Self-efficacy 92 5 21 13.37 3.726 W
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In this problem, intention was take
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References Ajjan, H., & Hartshorne,
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Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
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Students perused the course materia
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problem. All articles were then sen
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tests, electronic tests are more li
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Students 10 and 11: My classmate Ed
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collection was also approved by all
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Ubiquitous Revision Seamless Collab
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Tai, Y. (2012). Contextualizing a M
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tasks were developed from task type
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experiences in this phase. Moreover
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test is administered again right af
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The collected data were analyzed wi
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Gorp, K. V., & Bogaert, N. (2006).
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4PL IRT model According to the numb
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examinee’s ability more accuratel
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would be more accurate if items j+1
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C�I or Change I�I) would be 0.2
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improved by rearrangement procedure
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Table 6. Repeated Measures ANOVA of
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Leppisaari, I., & Lee, O. (2012). M
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Selection of the virtual tool The t
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Environmental themes emerged strong
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Visualization of text (visual langu
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environments where visual technolog
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- Logging in to learning environmen
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McNaught, C., Lam, P. & Lam, S.L. (
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on-site to finally achieve a meanin
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Table 1. Digital game designers and
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worldviews are socio-cultural-histo
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Phase Methods Visitor data collecti
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operate the game without the help o
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Visitors (end users) must be heard.
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Islas Sedano, C., Pawlowski, J., Su
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part of something larger than the s
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Previous studies have developed rel
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Technology Acceptance. The 10 items
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Table 4. Model Fit Indices Model χ
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In the final path model of set 1, t
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Discussion Differing from prior stu
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Haythornthwaite, C., Kazmer, M. M.,
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Ge, Z.-G. (2012). Cyber Asynchronou
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college of a university situated in
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Table 3 shows us the following info
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Appendix B shows Class 1’s respon
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increase one’s ability to process
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Appendix A Responses concerning syn
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Shih, S.-C., Kuo, B.-C., & Liu, Y.-
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Adaptive U-learning Math Path Syste
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Figure 4. Online tutorial courses o
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Participants and Experimental Proce
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the influence of Test1 scores, an e
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Hall, T., & Bannon, L. (2006). Desi
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(Hofer & Pintrich, 1997; Liu, Lin &
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� Through online peer assessment,
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elativism.” High Internet self-ef
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Peng, H., Tsai, C.-C., & Wu, Y.-T.
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Since game-based learning has been
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The second reason is about the sust
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Methods Figure 5. Quest NPCs provid
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These differences indicated that pa
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quests further involves students’
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Chang, I.-H. (2012). The Effect of
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(1) Vision, planning and management
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Based on an examination of the lite
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Table 2. Analysis of reliability an
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λ10 - 0.69 0.83 ε5 14.50 * - 0.32
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Bailey, G. D. (1997). What technolo
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Stegall, P. (1998). The principal:
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instruction has studied the student
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espond and the estimation of possib
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Research question For the purpose o
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READI reliability To verify the con
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Physical -1.43333(*) .44699 .029 -2
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The results of this study suggest t
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Hsu, Y.-C., Ho, H. N. J., Tsai, C.-
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domains. Five research questions we
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Research sample groups (4) Faculty
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13. Policies, Social Culture Impact
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Non-Specified Others Engineering &
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(AR = -2.1) (n = 7) 2. Non-specifie
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(n): Total number of articles Resea
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Compared to the publications in 200
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Pedagogical Content Knowledge (TPAC
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