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Grauman, K., Betke, M., Gips, J., & Bradski, G. R. (2001). Communication via eye blinks: Detection and duration analysis in real time. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. I-1010-I-1017), Los Alamitos: IEEE Computer Society. Gu, H., & Ji, Q. (2004). An automated face reader for fatigue detection. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (pp. 111–116), Los Alamitos: IEEE Computer Society. Hamada, T., Ito, T., Adachi, K., Nakano, T., & Yamamoto, S. (2002). Detecting method for drivers’ drowsiness applicable to individual features. Proceedings of the Fifth International IEEE Conference on Intelligent Transportation Systems (pp. 1405–1410), Los Alamitos: IEEE Computer Society. Harasim, L., Hiltz, S. R., Teles, L., & Turoff, M. (1995). Learning networks, Cambridge, MA: MIT Press. Hilgard, E. R. (1962). Introduction to Psychology, New York: Harcourt, Brace & World. Hwang, K.-A., & Yang, C.-H. (2008). A synchronous distance discussion procedure with reinforcement mechanism: Designed for elementary school students to achieve the attending and responding stages of the affective domain teaching goals within a class period. Computers & Education, 51 (4), 1538–1552. Holt, B. J., & Hannon, J. C. (2006). Teaching-learning in the affective domain. Strategies, 20 (1), 11–13. Ito, T., Mita, S., Kozuka, K., Nakano, T., & Yamamoto, S. (2002). Driver blink measurement by the motion picture processing and its application to drowsiness detection. Proceedings of the Fifth International IEEE Conference on Intelligent Transportation Systems (pp. 168–173), Los Alamitos: IEEE Computer Society. Kubiszyn, T., & Borich, G. (1996). Educational testing and measurement (6 th Ed.), New York: HarperCollins. Lee, T., Park, S. K., & Park, M. (2005). A new facial features and face detection method for human–robot interaction. Proceedings of the 2005 9 th IEEE International Conference on Robotics and Automation (pp. 2063– 2068), Los Alamitos: IEEE Computer Society. Lefrancois, G. R. (1982). Psychological theories and human learning, Monterey, CA: Brooks/Cole. Liao, L.-F. (2006). A flow theory perspective on learner motivation and behavior in distance education. Distance Education, 27 (1), 45–62. Martin, B. L., & Briggs, L. J. (1986). The affective and cognitive domains: Integration for instruction and research, Englewood Cliffs, NJ: Educational Technology. Mason, R. (1997). Evaluation methodologies for computer conferencing applications. In Kaye, A. R. (Ed.), Collaborative learning through computer conferencing, Heidelberg: Springer-Verlag. Miyakawa, T., Takano, H., & Nakamura, K. (2004). Development of non-contact real-time blink detection system for doze alarm. Proceedings of SICE Annual Conference 2004 (pp. 1626–1631), Tokyo, Japan: The Society of Instrument and Control Engineers. Pena-Shaff, J., Martin, W., & Gay, G. (2001). An epistemological framework for analyzing student interactions in computer-mediated communication environments. Journal of Interactive Learning Research, 12 (1), 41–68. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological issues in the content analysis of computer conference transcripts. International Journal of Artificial Intelligence in Education, 12, 8–22. Saleem, N. (1994). Alternative perspectives of user participation: Practical implications. ACM SIGCPR Computer Personnel, 15 (2), 25–31. Saltz, J. S., Hiltz, S. R., Turoff, M., & Passerini, K. (2007). Increasing participation in distance learning courses. IEEE Internet Computing, 11 (3), 36–44. Smith, P., Shah, M., & Lobo, N. V. (2004). Determining driver visual attention with one camera. IEEE Transactions on Intelligent Transportation Systems, 4 (4), 205–218. Stiefelhagen, R., Yang, J., & Waibel, A. (1996). A model-based gaze-tracking system. Proceedings of IEEE International Joint Symposia on Intelligence and Systems (pp. 304-310), Los Alamitos: IEEE Computer Society. Woalfolk, A. E. (1997). Educational psychology, Boston: Allyn and Bacon. Wang, P., Tran, L. C., & Ji, Q. (2006). Improving face recognition by online image alignment. Proceedings of The 18th International Conference on Pattern Recognition (pp. 311–314), Los Alamitos: IEEE Computer Society. Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43 (4), 345–359. 35
Hsieh, P.-H., & Dwyer, F. (2009). The Instructional Effect of Online Reading Strategies and Learning Styles on Student Academic Achievement. Educational Technology & Society, 12 (2), 36–50. The Instructional Effect of Online Reading Strategies and Learning Styles on Student Academic Achievement Pei-Hsuan Hsieh Institute of Information Management, National Cheng Kung University, No. 1 University Road, Tainan 701, Taiwan // pei.peace@gmail.com Francis Dwyer Instructional Systems Program, The Pennsylvania State University, University Park, PA 16802, USA ABSTRACT The purpose of this study was to examine the instructional effectiveness of different online reading strategies for students identified as possessing different learning styles, either internal or external locus of control styles, on tests measuring different learning objectives. Participants were 169 undergraduate students, randomly assigned to four online reading treatments: none, rereading strategy, keyword strategy, and question and answer strategy. Immediately after interacting with their respective instructions, students received four individual criterion measures. Analyses indicated an insignificant interaction between learning style and treatment type; however, comprehension tests reflect a significant main effect for students receiving the online rereading treatment (F = 3.09, df = 3/169, p = .03), with an effect size of .40. The rereading treatment also appears to be significantly more effective than the control for the comprehension test. Results indicate that not all types of reading strategies are equally effective in facilitating different types of learning objectives. The results indicate that, even though different reading strategies may be structurally different, they are functionally identical for raising questions relative to the cost and amount of time required for student interaction. Keywords Reading strategy, Learning style, Locus of control, Learning objectives Introduction Many studies have found reading strategies useful when implemented before, during or after reading (e.g., Brown, 2002; Ediger, 2005; Fagan, 2003; McGlinchey & Hixson, 2004; Millis & King, 2001; Sorrell, 1996). For example, reading strategies include rereading, scanning, summarizing, keywords, context clues, question-answer relationships, inferring, thinking aloud, activating prior knowledge, setting a purpose, and drawing conclusions. Online learning environments are becoming popular for most teachers and students. However, few studies focus on appropriate online reading strategies for different types of learners, and most studies focus only on the effectiveness of text-based reading strategies. The online learning environment has become more and more popular for educators and learners, due to its multiple visual and audio representations. Online learning is a trend that has the potential to enhance learning and increases the importance of knowledge of new teaching methods which apply to new learning environments (Jung, 2001; Romero, Berger, Healy & Aberson, 2000). According to previous studies, some learners encounter difficulties learning online, since they have difficulty changing their learning habits to accept reading electronic texts (Aragon, 2004; Steinhauer & Friederici, 2001). Learners feel doubtful about their learning abilities and believe that they are not as skilled as readers who can overcome the changes in the learning process. Some learners, contrarily, always try their hardest to adapt to the current learning environment, since they believe that they will eventually become comfortable with reading electronic materials (Ehrlich, Kurtz-Costes & Loridant, 1993; Ferguson, 1999; Schommer- Aikins & Easte, 2006). The question remains: What is the best way to employ a text-based reading strategy in an online environment, so that the learners can maintain their confidence for learning? In addition, students with their own learning styles may process information differently in an online environment (Atkinson & Shiffrin, 1969; Burton, Moore & Holmes, 1995; Huitt, 2003). Different online reading strategies may influence the students’ information processing methods (Driscoll, 2005; Schunk, 2004). This study seeks to discover the proper ways of employing online reading strategies and explores the effects of those online reading strategies and different learning styles on academic achievement. Most studies indicated that students regarded as having an internal locus of control learning style used significantly more reading strategies than students having an external 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. 36
Page 1 and 2: April 2009 Volume 12 Number 2
Page 3 and 4: Abstracting and Indexing Educationa
Page 5 and 6: Engaging students in multimedia-med
Page 7 and 8: this study discusses computer-based
Page 9 and 10: Sampling for preliminary study One
Page 11 and 12: Part 2 Pearson correlation .349** 1
Page 13 and 14: Figure 1. Box and whisker plot of t
Page 15 and 16: characteristics and learning styles
Page 17 and 18: However, critical reflection and in
Page 19 and 20: novels, so they collected any relat
Page 21 and 22: 1. Theories of teaching: Comments m
Page 23 and 24: grammatical errors and basic writin
Page 25 and 26: Ho, B., & Richards, J. C. (1993). R
Page 27 and 28: Hwang, K.-A., & Yang, C.-H. (2009).
Page 29 and 30: e deduced from the Affective Domain
Page 31 and 32: Detection procedure Figure 1 shows
Page 33 and 34: avoided. Image processing is perfor
Page 35 and 36: falling asleep) as defined in this
Page 37 and 38: classmates or left their seats duri
Page 39: help teachers to recognize the lear
Page 43 and 44: significant comprehension effects o
Page 45 and 46: Treatment 3 (keyword group): Studen
Page 47 and 48: Criteria of achievement measures Th
Page 49 and 50: esults. A correlational analysis de
Page 51 and 52: A 2 x 1 ANOVA analyzed the effect o
Page 53 and 54: References Aragon, S. R. (2004). In
Page 55 and 56: Raphael, T. (1982). Improving quest
Page 57 and 58: educing maintenance costs. The cont
Page 59 and 60: Synchronization functions According
Page 61 and 62: inconsistency) and navigate directl
Page 63 and 64: the suitability of the instructiona
Page 65 and 66: Design of manipulation process The
Page 67 and 68: Table 1: Comparative analysis of au
Page 69 and 70: the convenience of mobile-based ass
Page 71 and 72: According to evaluation result, the
Page 73 and 74: Tan, O. S., Parsons, R. D., Hinson,
Page 75 and 76: At a deeper level, though, there wa
Page 77 and 78: theoretical assumptions. Two theore
Page 79 and 80: The students were not going to have
Page 81 and 82: slightly different. The group who c
Page 83 and 84: every item, not giving any result.
Page 85 and 86: edited and presented to the class u
Page 87 and 88: Figure 10. Screen captures from the
Page 89 and 90: 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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48% 35% 53% similar similar 69% 42%
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Table 4: Students’ attitudes Stat
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activities. Specifically, the histo
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Huang, Y.-M., Huang, T.-C., Wang, K
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• Could recommended learning sequ
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Analysis of sequences prediction me
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Figure 4. The sorting of learning s
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The figures 5a and 5b illustrate th
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Learning sequence recommendation sy
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In this study, forty subjects were
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The potential for LSRS to promote l
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They pointed out that LSRS helped t
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Cover, T. M., & Thomas, J. A. (1991
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Related Efforts An Internet forum i
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with handheld devices and they were
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agent would direct learners to a le
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Mobile RSS Aggregator In order to s
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learning, such as posting question
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With novel technological support, m
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Shen, L., Wang, M., & Shen, R. (200
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as an ‘affective loop’, which r
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Figure 2 is an example of two-dimen
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preference was gathered through dat
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compute the heart rate (HR) as a fu
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sessions. Of all the 18 learning se
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Burleson, W., Picard, R. W., Perlin
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Wu, C.-C., & Lai, C.Y. (2009). Wire
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The wireless handheld learning envi
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the lengthiness of the scales, whic
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Nursing dictionary We constructed a
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them. The computers were networked
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“The major difference was that I
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questionnaire. We do not consider t
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Faruque, F., Hewlett, P. O., Wyatt,
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etween a user and the system. Addit
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Figure 2. A concept map representin
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C5.0 can help teachers to generate
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that a selected programming exercis
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differences between our system and
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1(students can complete without any
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The PADS sometimes assigned very di
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Corno, L. (2000). Looking at Homewo
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Hwang, W.-Y., Hsu, J.-L., Tretiakov
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listed these functions as allowing
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In the research presented in this a
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Research Tools Web-based learning e
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Table 1. Operational definitions of
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action, interaction, and outeractio
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for them was getting the answer as
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Table 6 shows the learners’ prefe
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Bell, P., & Winn, W. (2000). Distri
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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.,
Page 339 and 340:
Richards, G. (2009). Book review: T
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expands the child’s proximal envi
Page 343:
Chapter 6 exemplifies the activitie
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