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Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. Proceedings of the IEEE International Conference on Advanced Learning Technologies, Los Alamitos: CA: IEEE Computer Society Press, 43-46. Lévesque, J. (2006). BCIA-EEG, Université de Montréal, Canada. Leon, E., Clarke, G., & Callaghan, V. (2007). A user-independent real-time emotion recognition system for software agents in domestic environments. International Journal of Intelligent Real-time Automation, 20 (3), 337-345. Morishima, S. (2000). Real-time face Analysis and Synthesis using Neural Networks. Proceedings of the 2000 IEEE Signal Processing Society Workshop, IEEE press, 13-22. Nicholson, J., Takahashi, K., & Nakatsu, R. (2000). Emotion Recognition in Speech Using Neural Networks. Neural Computing & Applications, 9 (4), 290-296. Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1998). Discrete-Time Signal Processing (2 nd Ed.), New Jersey: Prentice-Hall. Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions, Cambridge: Cambridge University Press. Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1 (1), 95-123. Picard, R. W. (1997). Affective computing, Boston: MIT Press. Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., & Strohecker, C. (2004). Affective learning - a manifesto. BT Technology Journal, 22 (4), 253-269. Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (10), 1175-1191. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39 (6), 1161- 1178. Shen, L. P., Leon, E., Callaghan, V., & Shen, R. M. (2007). Exploratory Research on an Affective e-Learning Model. In J. Fong & F. L. Wang (Eds.), Proceedings of Workshop on Blended Learning' 2007, 267-278, retrieved March 2, 2009 from http://www.cs.cityu.edu.hk/~wbl2007/WBL2007_Proceedings_HTML/WBL2007_PP267-278_Shen.pdf. Shen, L. P., & Shen, R. M. (2005). Ontology-based intelligent learning content recommendation service. International Journal of Continuing Engineering Education and Life-Long Learning, 15 (3-6), 308-317. Shen, R. M., Han, P., & Yang, F. (2002). An open framework for smart and personalized distance learning. Lecture Notes in Computer Science, 2436, 19-30. Shen, R. M., Yang, F., & Han, P. (2003). A dynamic self-organizing e-Learner communities with improved multiagent matchmaking algorithm. Lecture Notes in Computer Science, 2903, 590-600. Sundström, P. (2005). Exploring the affective loop, Stockholm: Stockholm University. Vyzas, E., & Picard, R. W. (1998). Affective pattern classification. MIT Media Laboratory Affective Computing Technical Report No. 473, retrieved March 15, 2009 from http://vismod.media.mit.edu/pub/tech-reports/TR-473.pdf. Vyzas, E., & Picard, R. W. (1999). Offline and online recognition of emotion expression from physiological data. MIT Media Laboratory Affective Computing Technical Report No. 488, retrieved March 15, 2009 from http://vismod.media.mit.edu/pub/tech-reports/TR-488.pdf. 189
Wu, C.-C., & Lai, C.Y. (2009). Wireless Handhelds to Support Clinical Nursing Practicum. Educational Technology & Society, 12 (2), 190–204. Wireless Handhelds to Support Clinical Nursing Practicum Cheng-Chih Wu 1 and Chin-Yuan Lai 2 1 Graduate Institute of Information and Computer Education, National Taiwan Normal University, 162, Heping East Road, Section 1, Taipei, Taiwan // Tel: +886 2 23622841 ext. 13 // chihwu@ntnu.edu.tw 2 National Taichung Nursing College, 193, Sanmin Road, Section 1, Taichung, Taiwan // Tel: +886 4 22196964 // yuan@mail.ntcnc.edu.tw ABSTRACT This paper reports our implementation and evaluation of a wireless handheld learning environment used to support a clinical nursing practicum course. The learning environment was designed so that nursing students could use handhelds for recording information, organizing ideas, assessing patients, and also for interaction and collaboration with peers during an on-site clinical practicum. Our wireless handheld learning environment was field tested during a three-week practicum session. Analysis of data showed that both the instructor and the student benefited from using the Personal Digital Assistant (PDA) environment. The handhelds not only provided students with scaffolds to enhance learning but also facilitated peer cooperation and interaction with the instructor. Issues resulted from our implementation included things like the capacity of handhelds, network access, and participants’ preconceptions on using PC-based systems. Keywords Wireless, Handhelds, Clinical nursing practicum, Nursing education, PDAs Introduction “I was lucky to find a seat on the bus on my way to the hospital. I took out my PDA and checked the concept map I had drawn last night. I found a link was missing! I added it. …” “At the daily morning report, a doctor and his nurse reported how they handled a patient who cried for leaving the hospital and disrupted the ward last night. I recorded their report and wrote down key terms with my PDA. …. I later looked up the terms in my PDA’s Nursing Dictionary.” “The patient was curious about our PDA. We explained that it was for recording conversations and writing assignments. …. After the clinical interview, we returned to the nursing station and used the PDA to log into the course server to fill out the Patient’s ABC record. …. I saw my patient getting up from the bed and chatting with his neighbors, and then walking to the yard. He looked to be weighted down with a heavy heart. I switched my PDA to the Symptom Assessment page and record what I had observed. Unfortunately, the speed of the network was somewhat slow, and I couldn’t finish the task. …” “The instructor first asked everybody to transmit the concept maps to her notebook and then discussed with everyone individually. …. At the end of the meeting, the instructor asked us to download reading materials from her notebook to our PDA.” “On the bus going home, I didn’t use the PDA for fear of running out the battery. The first thing I did after going home was to charge my PDA and to synchronize it with my PC ….” (Adopted from students’ journals in the study) The use of handheld computers in clinical settings has been on the rise in recent years. Handheld computers have served not only as personal organizers but also as facilitators of efficient clinical diagnostic reasoning (Rempher, Lasome & Lasome, 2003). Surgeons, for example, have used PDAs to simplify data entry and retrieval at the patient’s bedside (Fowler, Hogle, Martini & Roh, 2002). Family practice residencies have also used handhelds as medication reference tools, electronic textbooks, clinical computational programs, and tracking of patient information (Criswell & Parchman, 2002). Healthcare professionals use PDAs to find appropriate medications, check for drug interactions, calculate dosages, and consult other clinical references (Tooey & Mayo, 2003). Today’s nurses have even used handheld-computer-based support systems to obtain patients’ preferences for functional bedside performance (Ruland, 2002). The use of handheld technology in clinical practice has been well documented. Huffstutler, Wyatt, and Wright (2002) suggested that these advanced technologies must be integrated into the nursing curricula to foster students’ proficiency in adapting to varied and expanding complex information systems. ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 190
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April 2009 Volume 12 Number 2
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Abstracting and Indexing Educationa
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Engaging students in multimedia-med
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this study discusses computer-based
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Sampling for preliminary study One
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Part 2 Pearson correlation .349** 1
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Figure 1. Box and whisker plot of t
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characteristics and learning styles
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However, critical reflection and in
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novels, so they collected any relat
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1. Theories of teaching: Comments m
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grammatical errors and basic writin
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Ho, B., & Richards, J. C. (1993). R
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Hwang, K.-A., & Yang, C.-H. (2009).
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e deduced from the Affective Domain
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Detection procedure Figure 1 shows
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avoided. Image processing is perfor
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falling asleep) as defined in this
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classmates or left their seats duri
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help teachers to recognize the lear
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Hsieh, P.-H., & Dwyer, F. (2009). T
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significant comprehension effects o
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Treatment 3 (keyword group): Studen
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Criteria of achievement measures Th
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esults. A correlational analysis de
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A 2 x 1 ANOVA analyzed the effect o
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References Aragon, S. R. (2004). In
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Raphael, T. (1982). Improving quest
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educing maintenance costs. The cont
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Synchronization functions According
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inconsistency) and navigate directl
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the suitability of the instructiona
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Design of manipulation process The
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Table 1: Comparative analysis of au
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the convenience of mobile-based ass
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According to evaluation result, the
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Tan, O. S., Parsons, R. D., Hinson,
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At a deeper level, though, there wa
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theoretical assumptions. Two theore
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The students were not going to have
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slightly different. The group who c
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every item, not giving any result.
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edited and presented to the class u
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Figure 10. Screen captures from the
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Some of the concerns that emerge fr
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Ajayi, L. (2009). An Exploration of
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As a result of the confluence of di
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peers (Schellens et al. 2005). Sche
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The integration of discussion board
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ecause the technology allowed the p
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The pre-service teachers perceived
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Furthermore, the study demonstrated
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Schellens, T., van Keer, H., & Valc
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McKean, 2003). Instructors will see
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program at The Ohio State Universit
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Knowledge and skills of developing
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their monthly reflections or assign
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Bull, K., Montgomery, D., Overton,
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frustrating the student). ITSs make
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• Link removal - advanced student
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Expert Module Expert Module’s mai
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Based on the questionnaire results
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students were told that their perfo
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thirds of the interviewed students
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Throughout the semester, students i
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human teacher or question developer
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To test the learning effectiveness
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APPENDIX A Computer Science and Sof
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Thus, the use of computers changes
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Specifically, two objectives were p
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Nonetheless, both teachers assign a
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Page 145 and 146: Table 4: Students’ attitudes Stat
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Page 153 and 154: Analysis of sequences prediction me
Page 155 and 156: Figure 4. The sorting of learning s
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Page 163 and 164: The potential for LSRS to promote l
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Page 167 and 168: Cover, T. M., & Thomas, J. A. (1991
Page 169 and 170: Related Efforts An Internet forum i
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Page 181 and 182: Shen, L., Wang, M., & Shen, R. (200
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Page 185 and 186: Figure 2 is an example of two-dimen
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Page 189 and 190: compute the heart rate (HR) as a fu
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Page 201 and 202: Nursing dictionary We constructed a
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Page 211 and 212: etween a user and the system. Addit
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Page 227 and 228: Hwang, W.-Y., Hsu, J.-L., Tretiakov
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Page 233 and 234: Research Tools Web-based learning e
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Askar, P., & Altun, A. (2009). CogS
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elations, interactions and activiti
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Figure 3. A comparison of knowledge
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ontology will easily be extensible
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Figure 7. Visual Representation of
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Step 5: Use Boolean to combine the
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2) CogSkillNet provides classroom i
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Neo, M., & Neo, T.-K. (2009). Engag
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• Conversation and collaboration
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IMAGE 2 2. Problem identification I
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Methodology At the end of the proje
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presentation skills (m = 3.72), and
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6. Can’t be denying that, sometim
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Lambert, N. M., & McCombs, B. J. (1
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not detected in a previous study, t
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et al. (2001) studied the learning
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all the students. At the midpoint o
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The observed pairs were aware of be
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Table 2 shows the results from a mo
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Table 5: The distribution of time (
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As discussed in the section on prev
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McDowell, C., Werner, L., Bullock,
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Comprehension monitoring is the awa
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or roles in a context, such as “I
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Correlation coefficient was also co
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(a) Text 1 (b) Text 2 (c) Text 3 (d
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Table 3 shows that the average read
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Figure 10. Trace results of the les
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monitor their reading process and h
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Conn, S. R., Roberts, R. L., & Powe
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The technology-mediated groups, wit
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hybrid model of supervision was pos
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Our research indicates that the hyb
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Janoff, D. S., & Schoenholtz-Read,
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goal of using storyboards. But, thi
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In general, learning activities nee
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Furthermore, free subjects, such as
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Storyboarding Roots and related wor
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Referees, on the other hand, may wa
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Symbols Interpretation Defines a un
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Figure 4. Top-level storyboard for
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may take subjects such as practical
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network security information envir
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The concrete procedure and interact
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(e.g., for graduation) using such i
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Thus, academic education modeling b
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Feyer, T., Kao, O., Schewe, K.-D.,
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Richards, G. (2009). Book review: T
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expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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