April 2012 Volume 15 Number 2 - Educational Technology & Society

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Georgeff, M. & Lansky, A. (1987). Reactive reasoning and planning. In K. D. Forbus, & H. E. Shrobe (Eds.), Proceedings of the 6th National Conference on Artificial Intelligence (pp. 677–682). Seattle, Washington: Morgan Kaufmann. Graesser, A. C. & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31(1), 104- 137. Graesser, A. C., Person, N. K., Harter, D., & Tutoring Research Group. (2001). Teaching tactics and dialog in AutoTutor. International Journal of Artificial Intelligence in Education, 12(3), 257-279. Hernandez, Y., Noguez, J., Sucar, E., & Arroyo-Figueroa, G. (2006). Incorporating an Affective Model to an Intelligent Tutor for Mobile Robotics. Proceedings of the 36th Annual Frontiers in Education Conference (pp. 22-27). NY: IEEE. Heylen, D., Nijholt, A., op den Akker R., & Vissers, M. (2003). Socially intelligent tutor agents. Lecture Notes in Computer Science, 2792, 341-347. John, O.D. & Atof Inc. (2003). The big five personality test. [Personality test]. Retrieved October 23, 2006 from http://www.outofservice.com/bigfive/ Johnson, A. & Taatgen, N. (2005). User modeling. In R. W. Proctor & L.V. Kim-Phuong (Eds.), The handbook of human factors in Web design (pp. 424-438). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Johnson, W.L., Rickel, J.W., & Lester, J.C. (2000). Animated pedagogical agents: Face-to-face Interaction in interactive learning environment. International Journal of Artificial Intelligence in Education, 11, 47-78. Lang, P. J. (1980). Behavioral treatment and bio-behavioral assessment: Computer applications. In J. B. Sidowski, J. H. Johnson, & T. A. Williams (Eds.), Technology in mental health care delivery systems (pp. 119–137). Norwood, NJ: Ablex. Linnenbrink, E. A. & Pintrich, P. R. (2002). Motivation as an enabler for academic success. School Psychology Review, 31(3), 313-327. Loftin, B., Mastaglio, T., & Kenney, P. (2004). Outstanding research issues in intelligent tutoring systems (Research Report No. N61339-03-C-0156). Retrieved from http://www.mymicsurveys.com/site/files/pub_4.pdf McCrae, R.R., & John, O.P. (1992). An introduction to the five-factor model and its applications. Special Issue: The five-factor model: Issues and applications. Journal of Personality, 60(2), 175-215. McCrae, R.R. & Costa, P.T. (1994). The stability of personality: Observations and evaluations. Current Directions in Psychological Science, 3(6), 173–175. McIntyre, G. & Göcke, R. (2007). Towards affective sensing. Lecture Notes in Computer Science, 4552, 411-420. Mehrabian, A. (1996). Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology, 14(4), 261-292. Morris, W. N, & Schnurr, P. P. (1989). Mood: The frame of mind. New York: Springer-Verlag. Neji, M., & Ammar, M. B. (2007). Agent-based collaborative affective e-learning framework. Electronic Journal of e-Learning, 5(2), 123 – 134. Parunak, H., Bisson, R., Brueckner, S., Matthews, R., & Sauter, J. (2006). A Model of Emotions for Situated Agents. In H. Nakashima, M. P. Wellman, G. Weiss, & P. Stone (Eds.), Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 993-995). New York, USA: ACM. Person, N. K., Graesser, A. C., & The Tutoring Research Group (2003). Fourteen facts about human tutoring: Food for thought for ITS developers. In V. Aleven, U. Hoppe, J. Kay, R. Mizoguchi, H. Pain, F. Verdejo, & K. Yacef (Eds.), Artificial Intelligence in Education 2003 Workshop Proceedings on Tutorial Dialogue Systems: With a View Toward the Classroom (pp. 335-344). Sydney, Australia: School of Information Technologies, University of Sydney. Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D.,…Stronhecker, C. (2004). Affective learning - A manifesto. BT Technology Journal, 22(4), 253-269. Picard, R. (2006). Building an affective learning companion. [PowerPoint slides] Retrieved from http://www.its2006.org/ITS_keynote/ITS2006_01.pdf Rodrigues, M., Novais, P., & Santos, M. F. (2005). Future challenges in Intelligent tutoring systems – A framework. In A. Méndez-Vilas, B. González-Pereira, J.A. Mesa González, (Eds.), Recent research developments in learning technologies: Proceedings from the International Conference on Multimedia, Information and Communications Technologies in Education (pp. 929-934). Badajoz, Spain: Formatex. Salovey, P. & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition, & Personality, 9(3), 185-211. 113
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April 2012 Volume 15 Number 2
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Supporting Organizations Centre for
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Contextualizing a MALL: Practice De
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Kop, R. (2012). The Unexpected Conn
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Human mediation and information flo
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elevant information based on some k
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of learners. A search facilitated t
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information as human mediation mean
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Blau, I., & Barak, A. (2012). How D
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examined whether the readiness to p
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Table 5 presents the ANOVA for the
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However, in discussing a non-sensit
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deviations showed smaller variance,
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actual participation discussing sen
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Mesch, G., & Elgali, Z. (2009). Soc
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Although prior research on overall
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with a score of 39 and below as “
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Table 2 demonstrates mean scores an
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Discussion Although the recent Inte
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Furthermore, the most preferred pla
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MEB. (2009). Internete erisim proje
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Learning Management Systems (LMS) b
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practice and repetition with feedba
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Online peer assessment Peer assessm
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At an international conference on m
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� Ability to produce rich assessm
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Meishar-Tal, H., & Tal-Elhasid, E.
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Tsai, S. C. (2012). Integration of
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order to decrease text complexity a
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matter how the learners’ aptitude
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If compared by programs, WP student
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mean F2F 4.64 4.52 4.35 4.44 *: p
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een analyzed and were compared with
Page 67 and 68: Chen, G.-D., Lee, J.-H., Wang, C.-Y
Page 69 and 70: and diverse cognitional and emotion
Page 71 and 72: Encourage and persuade students wit
Page 73 and 74: Materials and procedure Each studen
Page 75 and 76: The lower part of Figure 4 shows st
Page 77 and 78: Mayer, R. E., Sobko, K., & Mautone,
Page 79 and 80: that an item is answered correctly
Page 81 and 82: Table 1. Relative concepts frequenc
Page 83 and 84: The Account Management Module provi
Page 85 and 86: Experiment 1 and results Figure 7.
Page 87 and 88: Utilization of Test Item 36 33 30 2
Page 89 and 90: Utilization of Test Item 36 33 30 2
Page 91 and 92: 0.065 0.877 (17.56) 0.938 (22.55) 0
Page 93 and 94: Hwang, G.J., Hsiao, C.L., & Tseng,
Page 95 and 96: the diverse needs of modern industr
Page 97 and 98: Figure 1. The framework of interdis
Page 99 and 100: Research Design Figure 3. Recommend
Page 101 and 102: friendly. 8. I feel the layout and
Page 103 and 104: satisfaction, and system acceptance
Page 105 and 106: Ozok, A. A., Fan, Q., & Norcio, A.
Page 107 and 108: than students in a conventional cla
Page 109 and 110: shown in Figure 2 to include the st
Page 111 and 112: (1996) Pleasure-Arousal-Dominance (
Page 113 and 114: Based on this connection, we consid
Page 115 and 116: Final dominance measurements were p
Page 117: student mood and thereby allow comp
Page 121 and 122: The rationale behind the video form
Page 123 and 124: usefulness of the movies and their
Page 125 and 126: case of the first two questions and
Page 127 and 128: this paper in order to evaluate the
Page 129 and 130: The survey questions Appendix A In
Page 131 and 132: E-training in organizations E-train
Page 133 and 134: efficacy regarding online training,
Page 135 and 136: Method Sample and procedure Figure
Page 137 and 138: 0.874 which exceeded the recommende
Page 139 and 140: Even though the result does not con
Page 141 and 142: Brown, M., & Cudeck, R. (1993). Alt
Page 143 and 144: Wen, Y., Looi, C.-K., & Chen, W. (2
Page 145 and 146: when they are designing and impleme
Page 147 and 148: paper focuses on the first three st
Page 149 and 150: their self-esteem to be partners in
Page 151 and 152: Democratizing knowledge and symmetr
Page 153 and 154: 1) Reading the article: At the star
Page 155 and 156: Effects of RCKI principle-based ped
Page 157 and 158: References Alexander, C. (1979). Th
Page 159 and 160: Valsamidis, S., Kontogiannis, S., K
Page 161 and 162: The Analog system (Yan et al., 1996
Page 163 and 164: First, the number of the sessions a
Page 165 and 166: BioLayout uses a modified version o
Page 167 and 168: 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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