January 2012 Volume 15 Number 1 - Educational Technology ...

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Lynch, M., & Metcalfe, M. (2006). Reflection, Pragmatism, Concets and Intuition. Journal of Information Technology Theory and Application, 7(4), 1-10. Mezirow, J. (1981). A critical theory of adult learning and education. Adult Education, 32(1), 3-24. Moon, J. (2004). A Handbook of Reflective and Experiential Learning. London: Routledge. Moon, J. A. (1999). Reflection in Learning and Professional Development: Theory and Practice. London: Kogan Page Limited. Moore, A. H., Fowler, S. B., & Watson, C. E. (2007). Active Learning and Technology: Designing Change for Faculty, Students, and Institutions. Educause Review, 42(5), 42–61. Moore, J., & Whitfield, V. F. (2008). Musing: A Way to Inform and Inpire Pedagogy Throught Self-Reflection. The Reading Teacher, 61(7), 586-588. Nichols, D. M. (1993). Intelligent Student Systems: An Application of Viewpoints to Intelligent Learning Environments. PhD Thesis, Lancaster University, UK. Ortiz, J. (2006). Reflective Practice and Student Learning in the Introductory Interpersonal Communication Course: Maricopa Institute for Learning. Phelan, A. (2001). Power and place in teaching and teacher education. Teaching and Teacher Education, 17(5), 583-597. Roscoe, R. D., & Chi, M. T. H. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 77(4), 534-574. Roscoe, R. D., & Chi, M. T. H. (2008). Tutor learning: the role of explaining and responding to questions. Instructional Science, 36, 321-350. Schön, D. A. (1987). Teaching artistry through reflection-in-action Educating the Reflective Practitioner. San Francisco, CA: Jossey-Bass Publishers. Senge, P. M. (1992). The Fifth Discipline – The Art & Practice of The Learning Organization. London: Century Business. Sterman, J. D. (1994). Learning in and about Complex Systems. System Dynamics Review, 10(2-3), 291-330. Surbeck, E., Han, E. P., & Moyer, J. E. (1991). Assessing reflective responses in journals. Educational Leadership, 48(6), 25-27. Wu, L., & Looi, C. K. (2008). Use of agent prompts to support reflective interaction in a learning-by-teaching environment. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 302-311. Xie, K., & Bradshaw, A. C. (2008). Using question prompts to support ill-structured problem solving in online peer collaborations. International Journal of Technology in Teaching and Learning, 4(2), 148-165. 353
Chen, Y.-H., Looi, C.-K., Lin, C.-P., Shao, Y.-J., & Chan, T.-W. (2012). Utilizing a Collaborative Cross Number Puzzle Game to Develop the Computing Ability of Addition and Subtraction. Educational Technology & Society, 15 (1), 354–366. Utilizing a Collaborative Cross Number Puzzle Game to Develop the Computing Ability of Addition and Subtraction Yen-Hua Chen 1 , Chee-Kit Looi 2 , Chiu-Pin Lin 3 , Yin-Juan Shao 2 and Tak-Wai Chan 4 1 Department of Computer Science and information Engineering, National Central University, Taiwan // 2 Learning Sciences Lab, National Institute of Education, 1 Nanyang Walk, Singapore 637616 // 3 Graduate Institute of eLearning Technology, National Hsinchu University of Education, Taiwan // 4 Graduate Institute of Network Learning Technology, National Central University, Taiwan // oliver@cl.ncu.edu.tw // cheekit.looi@nie.edu.sg // chiupin.lin@gmail.com // yj_shao@hotmail.com // chan@cl.ncu.edu.tw ABSTRACT While addition and subtraction is a key mathematical skill for young children, a typical activity for them in classrooms involves doing repetitive arithmetic calculation exercises. In this study, we explore a collaborative way for students to learn these skills in a technology-enabled way with wireless computers. Two classes, comprising a total of 52 students in Grade 4 (ages 10 or 11) participated in the study. They used the Group Scribbles software to run an adapted version of the “Cross Number Puzzle” that was designed with the “feedback” mechanism to assist students’ problem solving. In one class, students played the game individually and in the other class, students played the game collaboratively. The low-ability students in the collaborative class were found to have made the most significant progress in arithmetic skills through playing this game. Three dominant interactive collaboration patterns, one contributing to productive interactions and two to less productive interactions, were also identified in the students’ collaboration. Keywords Computer-supported collaborative learning, feedback mechanism, Cross Number Puzzle, computing ability Introduction Computational skills in addition and subtraction are an important component in any mathematics curriculum for young children. In seeking to design more interesting learning experiences for children to learn these skills, we designed and implemented a learning activity which is an adapted version of the “Cross Number Puzzle” on a technology platform. The aim of this game is to promote the flexible application of addition and subtraction skills, and to enhance children’s capacity to build up their arithmetic skills progressively. We conducted a study on two classes of students to explore different collaborative learning patterns that involved students working together on solving arithmetic problems. We also examined differences between individual learning and collaborative learning. A variety of educators have classified operating addition and subtraction story problems into four problem types: change, combine, compare and equalizer (Carpenter, Hiebrt, and Moser, 1981; Fuson, 1992; Gustein and Romberg, 1995). English (1998) points out that change and combine are easier while take-away and compare are more difficult challenges for elementary school students. In an arithmetic equation, any of the three numbers could be the unknown number. We adopted this widely used method in our study. Fuson (1992) defines these three types of “change” (placeholder) as: Missing End, Missing Change, and Missing Start. Van de Walle (2001) also classifies the type of “change” into three types: result-unknown, change- unknown and initial-unknown. These three types of problems present different levels of difficulty to the students. In Tompson’s study (1983), the initial unknown is most difficult. If the student applies the direct modeling strategy by using counters or tally marks to model directly the action or relationships described in the problem (Carpenter et al., 1993), he or she always does not know how many counters to be put down to begin with. Table 1 illustrates the three levels of change types in story problems. Level I is when the result number is unknown, Level II is when the change number is unknown, and Level III is when the initial number is unknown (Carey 1991; Peterson et al, 1991). With these three levels in Table 1 in mind, we designed our system to incorporate five stages of problem posing to the students (Table 2): In stage 1, the student is required to derive the answer of an arithmetic expression (result number unknown), inculcating the skills of basic addition and subtraction, for example: 3 ± 2 = □. In stage 2, the arithmetic operator is removed. Students were required to understand the concept of arithmetic operator, for example: 3 □ 2 = 5. In stage 3, the change amount is removed, for example: 3 ± □ = 5. 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. 354
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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Page 317 and 318: References ADL. (2006). Sharable Co
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Page 321 and 322: Mediating effect of learning flow A
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Page 325 and 326: addition, the cross-loadings of the
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Page 329 and 330: Connell, J. P., Spencer, M. B., & A
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Page 333 and 334: Learning is a cognitive activity th
Page 335 and 336: The survey consisted of 30 question
Page 337 and 338: Created data mining model needs to
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Page 341 and 342: Conclusion Conducted research showe
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Page 345 and 346: prompts as scaffolding strategy to
Page 347 and 348: Reflection Types This study attempt
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Page 351 and 352: Results Learning Outcomes Pre- and
Page 353 and 354: Map Analysis in Transfer Test Figur
Page 355 and 356: Generic Prompts and Specific Prompt
Page 357: Biswas, G., Schwartz, D., Bransford
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Page 365 and 366: Collaboration Questionnaire results
Page 367 and 368: following diagrams. The double-arro
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Page 371 and 372: Bangert-Drowns, R.L., Kulick, C. C.
Page 373 and 374: primary research questions. First,
Page 375 and 376: Individual learning Figure 2. Scree
Page 377 and 378: Experimental Tools This study emplo
Page 379 and 380: Table 3 shows that almost all items
Page 381 and 382: I usually engaged myself in listeni
Page 383 and 384: References Bloom, B. S. (Ed.). (195
Page 385 and 386: APPENDIX 2. Taxonomy for Informatio
Page 387 and 388: understand how we can effectively u
Page 389 and 390: 5. Is there a relationship between
Page 391 and 392: correlation between teachers’ IWB
Page 393 and 394: Q7. IWB provides advantages to me t
Page 395 and 396: training regarding this topic. This
Page 397 and 398: Conclusion This study provides a so
Page 399: Smith, H. J., Higgins, S., Wall, K.
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