Lauer, T. (2006). Learner Interaction with Algorithm Visualizations: Viewing vs. Changing vs. Constructing. SIGCSE Bulletin, 38(3):202–206. Lave, J. and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge University Press. Lego Group (n.d.). Lego Mindstorms (web site). Accessed October 2011. URL http://mindstorms.lego.com/ Lessa, D., Czyz, J. K., Gestwicki, P. V., and Jayaraman, B. (n.d.). JIVE: Java Interactive Visualization Environment (web site). Accessed April 2012. URL http://www.cse.buffalo.edu/jive/ Letovsky, S. and Soloway, E. (1986). Delocalized Plans and Program Comprehension. Software, 3(3):41– 49. Lewis, J. (2000). Myths about Object-Orientation and Its Pedagogy. SIGCSE Bulletin, 32(1):245–249. Lincoln, Y. S. and Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications. Lincoln, Y. S., Lynham, S. A., and Guba, E. G. (2011). Paradigmatic Controversies, Contradictions, and Emerging Confluences, Revisited. In: N. K. Denzin and Y. S. Lincoln (eds.), The Sage Handbook of Qualitative Research, pp. 97–128. Sage, 4th edition. Linn, M. C. and Clancy, M. J. (1992). The Case for Case Studies of Programming Problems. Communications of the ACM, 35(3):121–132. Linn, M. C. and Dalbey, J. (1985). Cognitive Consequences of Programming Instruction: Instruction, Access, and Ability. Educational Psychologist, 20(4):191–206. Lister, R. (2001). Objectives and Objective Assessment in CS1. SIGCSE Bulletin, 33(1):292–296. Lister, R. (2004). Teaching Java First: Experiments with a Pigs-Early Pedagogy. In: Proceedings of the Sixth Australasian Conference on Computing Education, ACE ’04, pp. 177–183. Australian Computer Society. Lister, R. (2011a). Concrete and Other Neo-Piagetian Forms of Reasoning in the Novice Programmer. In: J. Hamer and M. de Raadt (eds.), Proceedings of the 13th Australasian Conference on Computing Education (ACE ’11), volume 114 of CRPIT, pp. 9–18. Australian Computer Society. Lister, R. (2011b). Programming, Syntax and Cognitive Load. ACM Inroads, 2(2):21–22. Lister, R. (2011c). Programming, Syntax and Cognitive Load (Part 2). ACM Inroads, 2(3):16–17. Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., Simon, B., and Thomas, L. (2004). A Multi-National Study of Reading and Tracing Skills in Novice Programmers. SIGCSE Bulletin, 36(4):119–150. Lister, R., Berglund, A., Clear, T., Bergin, J., Garvin-Doxas, K., Hanks, B., Hitchner, L., Luxton-Reilly, A., Sanders, K., Schulte, C., and Whalley, J. L. (2006a). Research Perspectives on the Objects-Early Debate. SIGCSE Bulletin, 38(4):146–165. Lister, R., Clear, T., Simon, Bouvier, D. J., Carter, P., Eckerdal, A., Jacková, J., Lopez, M., McCartney, R., Robbins, P., Seppälä, O., and Thompson, E. (2009a). Naturally Occurring Data as Research Instrument: Analyzing Examination Responses to Study the Novice Programmer. SIGCSE Bulletin, 41(4):156–173. Lister, R., Fidge, C., and Teague, D. (2009b). Further Evidence of a Relationship between Explaining, Tracing and Writing Skills in Introductory Programming. SIGCSE Bulletin, 41(3):161–165. 402
Lister, R. and Leaney, J. (2003). Introductory Programming, Criterion-Referencing, and Bloom. SIGCSE Bulletin, 35(1):143–147. Lister, R., Simon, B., Thompson, E., Whalley, J. L., and Prasad, C. (2006b). Not Seeing the Forest for the Trees: Novice Programmers and the SOLO Taxonomy. SIGCSE Bulletin, 38(3):118–122. Lönnberg, J. (2012). Understanding and Debugging Concurrent Programs through Visualisation. Doctoral dissertation, Department of Computer Science and Engineering, Aalto University. Loftus, C., Thomas, L., and Zander, C. (2011). Can Graduating Students Design: Revisited. In: Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, SIGCSE ’11, pp. 105–110. ACM. Lopez, M., Whalley, J., Robbins, P., and Lister, R. (2008). Relationships between Reading, Tracing and Writing Skills in Introductory Programming. In: Proceedings of the Fourth International Workshop on Computing Education Research, ICER ’08, pp. 101–112. ACM. Lucas, U. and Mladenovic, R. (2006). Developing New ‘World Views’: Threshold Concepts in Introductory Accounting. In: J. H. F. Meyer and R. Land (eds.), Overcoming Barriers to Student Understanding: Threshold Concepts and Troublesome Knowledge, pp. 148–159. Routledge. Lui, A. K., Kwan, R., Poon, M., and Cheung, Y. H. Y. (2004). Saving Weak Programming Students: Applying Constructivism in a First Programming Course. SIGCSE Bulletin, 36(2):72–76. Ma, L. (2007). Investigating and Improving Novice Programmers’ Mental Models of Programming Concepts. Ph.D. thesis, Department of Computer & Information Sciences, University of Strathclyde. Ma, L., Ferguson, J., Roper, M., and Wood, M. (2011). Investigating and Improving the Models of Programming Concepts Held by Novice Programmers. Computer Science Education, 21(1):57–80. Ma, L., Ferguson, J. D., Roper, M., Ross, I., and Wood, M. (2009). Improving the Mental Models Held by Novice Programmers using Cognitive Conflict and Jeliot Visualisations. SIGCSE Bulletin, 41(3):166–170. Madison, S. and Gifford, J. (1997). Parameter Passing: The Conceptions Novices Construct. Research report. URL http://eric.ed.gov/PDFS/ED406211.pdf Maletic, J. I., Marcus, A., and Collard, M. L. (2002). A Task Oriented View of Software Visualization. In: Proceedings of the First International Workshop on Visualizing Software for Understanding and Analysis, VISSOFT ’02, pp. 32–40. IEEE. Malmi, L., Sheard, J., Simon, Bednarik, R., Helminen, J., Korhonen, A., Myller, N., Sorva, J., and Taherkhani, A. (2010). Characterizing Research in Computing Education: A Preliminary Analysis of the Literature. In: Proceedings of the Sixth International Workshop on Computing Education Research, ICER ’10, pp. 3–12. ACM. Mann, L. M., Linn, M. C., and Clancy, M. (1994). Can Tracing Tools Contribute to Programming Proficiency? The LISP Evaluation Modeler. Interactive Learning Environments, 4(1):96–113. Maravić Čisar, S., Pinter, R., Radosav, D., and Čisar, P. (2010). Software Visualization: The Educational Tool to Enhance Student Learning. In: Proceedings of the 33rd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO ’10, pp. 990–994. IEEE. Maravić Čisar, S., Radosav, D., Pinter, R., and Čisar, P. (2011). Effectiveness of Program Visualization in Learning Java: A Case Study with Jeliot 3. International Journal of Computers Communications & Control, 6(4):669–682. Markman, A. B. (1999). Knowledge Representation. Lawrence Erlbaum. 403
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Aalto University publication series
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Abstract Aalto University, P.O. Box
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Acknowledgements Lauri Malmi first
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4.5.3 Some effects of cognitive loa
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13.1.2 It is simple to watch an ani
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18.2.3 We saw a few pedagogically i
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11.16 PlanAni .....................
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Chapter 1 Here is How to Make Sense
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On research traditions Each researc
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The thesis should ideally be read f
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Introduction to Part I Introductory
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Figure 2.1: Bloom’s taxonomy of l
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2.2 The SOLO taxonomy sorts learnin
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2.2.3 The expected outcomes of prog
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Multi-institutional studies In the
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3.3 But many students do not learn
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long-term, action research study of
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Introduction to Part II What is inv
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Figure 4.1: A commonly used basic a
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play a decisive role in how and whe
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cognitive psychology also sought to
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terms for the two meanings. Followi
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It suggests that the growth of expe
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work (because of lack of motivation
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The effect of prior knowledge: the
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“writing a computer program is le
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model of program comprehension to o
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Chapter 5 Psychologists Also Say: W
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• are commonly deficient in a num
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expert stage. De Kleer and Brown ch
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5.3 Teachers employ conceptual mode
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Not only are there different notion
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the computer can carry out deductio
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Therefore: teach early and teach lo
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epresentation of a complex program
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A reasonable description of this fo
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are “somewhat contrary to the cla
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memory storage (see, e.g., Greeno,
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Chapter 6 Constructivists Say: Know
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Figure 6.1: A classification of con
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Social constructivist reasoning tak
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Figure 6.2: A radical destructivist
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certainly fall under the broad usag
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in the eyes of the members of the c
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of proper design, coding style, req
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In any particular course you will b
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precedes construction. Therefore, c
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Skirmish 7: minimal guidance pedago
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Chapter 7 Phenomenographers Say: Le
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the phenomenon. An individual’s e
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(Bowden and Marton, 2004). A way of
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The answer is none in particular, w
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Table 7.3: Different ways of experi
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7.6 Phenomenography has not escaped
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outcome spaces describe both the st
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Figure 8.1: Views from three tradit
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There is substantial agreement betw
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• it may mark boundaries in ‘co
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Telling TCs apart from other conten
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et al., 2007). The latter was later
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Part III Teaching Introductory Prog
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Chapter 10 CS1 is Taught in Many Wa
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it and right from the beginning. So
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10.1.3 Guidance mediates complexity
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10.2 Some approaches foster schema
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Many CS1 teachers have come up with
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Vagianou (2006) observes that a wea
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Figure 10.6: A view of Anchor Garde
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impressions of computing matter and
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The paradigm shift There is anecdot
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notional machines. Sajaniemi and Ku
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Program visualization vs. algorithm
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Figure 11.2: A part of Kelleher and
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aspects can play a decisive part in
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Table 11.1: The original engagement
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The 2DET The engagement taxonomy of
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Given content means that the learne
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Table 11.5: A summary of selected p
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Figure 11.4: The PyDev debugger for
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Figure 11.6: DynaLab executing a To
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Figure 11.10: The user has just com
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Figure 11.11: Korsh and Sangwan’s
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Figure 11.14: JIVE displays various
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Figure 11.16: PlanAni executing a P
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Figure 11.18: A snapshot of a Java
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Figure 11.19: Jeliot 3 executing a
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Figure 11.21: The Teaching Machine
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students used VIP in the way intend
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Python in the browser: Jype and the
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11.3.3 A few systems make the stude
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Figure 11.31: Students interact wit
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Online Tutoring System Kollmansberg
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Figure 11.36: A “Clouds & Boxes
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Table 11.7: Experimental evaluation
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Introduction to Part IV We have now
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An example Here is a short Python c
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Chapter 13 The UUhistle System Faci
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194 Figure 13.1: An animation of a
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Figure 13.2: The between-step stage
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Figure 13.4: UUhistle highlights a
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Figure 13.7: UUhistle’s interacti
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13.4 Teachers can turn examples int
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Add another +, then the literal 1.
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Figure 13.11: The user has created
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Figure 13.14: A VPS assignment in t
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Figure 13.16: A visual algorithm si
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Chapter 14 Visual Program Simulatio
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the individual instructions of the
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Visual program simulation seeks to
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14.2.2 VPS exercises can have eleme
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features prominently in many progra
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too, later decided to develop a sof
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The easiest examples for a programm
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may be necessary; at the very least
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Figure 14.1 continued M. H. van Emd
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integration of new material with pr
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The system designer vs. excessive d
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Level 3 By allowing what’s wrong.
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about this mistake to the user in a
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Table 15.1: Cognitive dimensions of
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parts that deal with different stag
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Error-proneness A VPS exercise in U
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something that resides ‘within th
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evidence. Like the documentation of
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Introduction to Part V Reflecting o
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launched for sharing (e.g., Fincher
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elatively independent of the resear
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There are values that are internal
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• Authenticity is an abstraction
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• Triangulation: triangulation of
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Each assignment was worth a number
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Figure 16.1: The earlier version of
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Chapter 17 Students Perceive Visual
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Figure 17.1: The phenomenographic r
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Figure 17.4: The structure of human
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Figure 17.5: The structure of aware
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further note the importance of esta
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Whichever variant of the analysis p
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Different phenomenographers define
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choose what to do?”, “What does
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We sought to form an outcome space
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17.4.1 A: VPS is perceived as learn
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17.4.2 B: VPS is perceived as learn
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Interviewer 2 : Can you describe ho
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17.4.5 E: VPS is perceived as learn
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through VPS is perceived as learnin
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Table 17.2: Qualitatively different
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what the phenomenon is in reality,
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the students develop a sophisticate
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involve VPS). 17 • The teacher us
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thought about the dynamics of VPS.
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solve the assignments using the ani
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Table 18.1: Types of information us
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As they start to work on the first
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print third second = third As they
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Elizabeth: And. . . So the function
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Otto: “True”. . . And then we g
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The pair fail to accommodate refere
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Parameter passing was the only topi
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18.3 We have some quantitative resu
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and the student searches for meanin
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in Section 18.2.4 is a step in this
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Chapter 19 UUhistle Helps Students
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• The supervising research assist
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Table 19.1: Improvement from ‘non
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- attempts to make variables get th
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to predict program behaviors better
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• The use of programs with a doma
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in the course more generally, not o
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Good both as an idea and in functio
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In many cases the UUhistle assignme
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Boredom and excessive detail A fair
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A handful of students suggested tha
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Part VI Conclusions 347
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Chapter 21 Visual Program Simulatio
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- Page 368 and 369: Table A.1 continued No. Topic Descr
- Page 370 and 371: Table A.1 continued No. Topic Descr
- Page 372 and 373: Table A.1 continued No. Topic Descr
- Page 374 and 375: Table A.1 continued No. Topic Descr
- Page 376 and 377: Table A.1 continued No. Topic Descr
- Page 378 and 379: Assignment 1.7 (VPS) marks = float(
- Page 380 and 381: else: print 'Better luck next time.
- Page 382 and 383: ide.drive(15) print ride.get_fuel()
- Page 384 and 385: def quaint(list, element): list[0]
- Page 386 and 387: Appendix D 3×10 Bullet Points for
- Page 388 and 389: Okay, what should I take into consi
- Page 390 and 391: References ACM and IEEE Computer So
- Page 392 and 393: Bareiss, R. and Radley, M. (2010).
- Page 394 and 395: Biggs, J. B. and Collis, K. F. (198
- Page 396 and 397: Burkhardt, J.-M., Détienne, F., an
- Page 398 and 399: Cross, II, J. H., Hendrix, T. D., a
- Page 400 and 401: Ehlert, A. and Schulte, C. (2009).
- Page 402 and 403: Gilligan, D. (1998). An Exploration
- Page 404 and 405: Harlow, S., Cummings, R., and Abera
- Page 406 and 407: Johnson, R. and Onwuegbuzie, A. J.
- Page 408 and 409: Ko, P. Y. and Marton, F. (2004). Va
- Page 412 and 413: Markman, A. B. and Gentner, D. (200
- Page 414 and 415: Miyadera, Y., Kurasawa, K., Nakamur
- Page 416 and 417: Norman, D. A. (2007). Simplicity is
- Page 418 and 419: Peterson, L. R. and Peterson, M. J.
- Page 420 and 421: Renkl, A., Stark, R., Gruber, H., a
- Page 422 and 423: Sajaniemi, J., Kuittinen, M., and T
- Page 424 and 425: Shneider, E. and Gladkikh, O. (2006
- Page 426 and 427: Stoodley, I., Christie, R., and Bru
- Page 428 and 429: Vainio, V. (2006). Opiskelijoiden m
- Page 430: Willingham, D. T. (2009). Why Don