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Aalto University publication series DOCTORAL DISSERTATIONS 61/2012 Visual Program Simulation in Introductory Programming Education Juha Sorva Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the School of Science for public examination and debate in Auditorium T2 at the Aalto University School of Science (Espoo, Finland) on the 30th of May 2012 at 12 noon. Aalto University School of Science Department of Computer Science and Engineering Learning + Technology Group (LeTech)
Page 4 and 5: Supervisor Professor Lauri Malmi Pr
Page 7: Tiivistelmä Aalto-yliopisto, PL 11
Page 10 and 11: Contents 1 Here is How to Make Sens
Page 12 and 13: 8 Interlude: Can We All Just Get Al
Page 14 and 15: V Empirical Investigations of Visua
Page 16 and 17: List of Figures 1.1 This thesis wit
Page 18 and 19: List of Tables 5.1 Terms related to
Page 20 and 21: Figure 1.1: This thesis - the red d
Page 22 and 23: Figure 1.2: This thesis as dialogue
Page 25 and 26: Part I The Challenge of Introductor
Page 27 and 28: Chapter 2 Introductory Programming
Page 29 and 30: There is some convergence of opinio
Page 31 and 32: Figure 2.2: Atherton’s (n.d.) met
Page 33 and 34: Chapter 3 Students Worldwide Do Not
Page 35 and 36: BRACElet: some evidence of skill de
Page 37 and 38: struggle with fundamental concepts,
Page 39 and 40: Part II Learning Introductory Progr
Page 41 and 42: Chapter 4 Psychologists Say: We For
Page 43 and 44: details, there is a general agreeme
Page 45 and 46: 4.3 Schemas keep the complexity of
Page 47 and 48: count = 0 sum = 0 number = float(ra
Page 49 and 50: Jeffries et al. (1981) concluded th
Page 51 and 52: 4.5 Cognitive load strains the huma
Page 53 and 54:
Figure 4.3: The different types of
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depends on prior knowledge in accor
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a program model and a domain model
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further point out that the directio
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5.1.1 We use mental models to inter
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Mental models allow users to be com
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meeting certain “esthetic princip
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executes the programs. 3 But what a
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5.4.2 Students struggle to form goo
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taught about a notional machine. In
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Détienne and Soloway’s parlance,
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Figure 5.1: Robust models transferr
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To me, this [research on misconcept
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5.7 The internal cognition of indiv
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maintains that physical and mental
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13. Effective learning has a social
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programme led by David Bloor (see,
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Context-dependence of knowledge The
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to reduce teaching that is based on
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it - intuitive and self-evident. An
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The challenge of the educational en
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phenomenon, although instruction ca
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6.8 Constructivisms have drawn some
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Skirmish 5: ignoring evidence Vario
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of postmodernism that is essentiall
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Figure 7.1: The phenomenographic re
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Table 7.1: Qualitatively different
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content. “The assumption is that
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Table 7.2: Different ways of experi
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Teaching should encourage well-foun
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If one dismisses educational norms
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Chapter 8 Interlude: Can We All Jus
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Figure 8.3: Views from three tradit
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Chapter 9 Certain Concepts Represen
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9.2 Identifying threshold concepts
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Program dynamics further takes the
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execution step by step. Without the
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Introduction to Part III The learni
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Table 10.1: Some strategies for tea
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10.1.2 Complexity is bad In teachin
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guidance and feedback to their stud
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While delaying programming exercise
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Figure 10.1: A visualization of a s
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Figure 10.4: A visualization of the
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or no need for simulating the proce
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programming is not a dominant techn
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A high-level OO machine? Sajaniemi
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Chapter 11 Software Tools can Visua
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Figure 11.1: Forms of software visu
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Teaching systems are further divide
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support for active learning, that i
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and presenting levels of the OET ou
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Figure 11.3: The two dimensions of
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Table 11.4: A legend for Tables 11.
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Table 11.6: A summary of how select
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Figure 11.5: A part of the Basic Pr
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Figure 11.7: A Pascal program in Am
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Producing snapshots: Kasmarik and T
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Figure 11.13: Two snapshots of the
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a system that automates the creatio
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166 Figure 11.17: The metaphor-base
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of the stronger students who enjoy
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Figure 11.20: An object viewer wind
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Figure 11.23: A C++ program within
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Figure 11.24: A Python program with
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Figure 11.26: A part of Jype’s us
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Figure 11.29: Gilligan’s (1998) p
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automatically) to the active area.
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Figure 11.34: An assignment on stri
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Figure 11.37: The user is executing
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Part IV Introducing Visual Program
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Chapter 12 In Visual Program Simula
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VPS leaves this task to the user. M
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specific requirements, and the embe
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You can adjust the sizes of various
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Figure 13.3: An example of a dialog
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Figure 13.6: An example of a UUhist
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Figure 13.8: A selection of predefi
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Figure 13.9: A visual program simul
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Figure 13.10: UUhistle reacts to a
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Figure 13.13: UUhistle attempts to
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Figure 13.15: UUhistle in the 2DET
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The intelligent tutoring system LIS
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changing an ingrained but flawed me
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The visualizations used in visual p
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Visual program simulation seeks to
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examine what they do. 2 A more ‘p
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14.3.2 Using a given visualization
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A crucial tool with which teachers
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in programming education. As VPS do
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Figure 14.1: Facing Edsger W. Dijks
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Chapter 15 UUhistle is the Product
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The third reason was pragmatic: the
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15.3 UUhistle is designed to addres
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Consider the misconception accordin
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15.4.2 UUhistle’s VPS exercises s
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components (variables, values, clas
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of color, the layout of objects and
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1. the amount of work needed to ini
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another panel. A better solution in
Page 255 and 256:
Part V Empirical Investigations of
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Chapter 16 We Investigated If, When
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We operationalized parts of these b
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. . . endorses eclecticism and plur
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Table 16.1: An overview of the empi
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As noted, the qualitative tradition
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Many students take CS1-Imp-Pyth mai
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Students typically spent a few minu
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We must acknowledge the possibility
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Section 17.3, we get to a more conc
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Figure 17.3: Aspects of learning ap
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of object (which is shown in full i
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esearch findings, and, in principle
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Analysis: individuals ←→ themes
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Here it is important to stress the
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We aimed for a minimum of 10 interv
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Supplementary observational data In
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Table 17.1: Ways of perceiving what
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Robert: Yeah, well, this UUhistle i
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Jan Erik: They [the boxes in UUhist
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William: When I write code myself,
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and only then which object it is ca
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Sue finds that what she has learned
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Category C: To learn through visual
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Category E describes a more product
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Class discussion and learning mater
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• the “What is this?” menu ch
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Chapter 18 We Explored What Happens
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Kimmo Kiiski and Teemu Koskinen sup
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Figure 18.1: List operators in the
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William is just beginning to work o
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students appears to think of the pa
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John 40 : At least I’d imagine it
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Not so that I first look and start
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18.2.4 We catalogued trouble spots
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Table 18.2: Students’ difficultie
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18.4.1 Teaching should facilitate r
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out what the program does or may ta
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VPS work from a teacher near the st
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19.1.1 We recruited a large number
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19.1.3 We investigated the improvem
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did not apply themselves to the pos
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print third second = third print se
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The previous chapters have already
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Chapter 20 The Students Liked It We
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Figure 20.1: Students’ answers to
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It clarified the material that we h
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At first, I didn’t even understan
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A better way could be to simulate c
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• Students get frustrated when th
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Introduction to Part VI In Parts I,
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as an important component of introd
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21.5 The contribution of the thesis
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the picture is hardly complete. Fut
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Part VII Appendices 357
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On Table A.1 In Table A.1, topics o
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Table A.1 continued No. Topic Descr
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Page 373 and 374:
Page 375 and 376:
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Appendix B Example Programs from Co
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Assignment 4.1 (animation) def gree
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def refuel(self, liters): actually_
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Appendix C Example Programs from Ex
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Q4 (pretest and post-test) def tral
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Already now, and especially in the
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Appendix E Statement of the Author
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Anderson, J. R., Conrad, F. G., and
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Bennedsen, J. and Schulte, C. (2006
Page 395 and 396:
Bower, M. (2008). A Taxonomy of Tas
Page 397 and 398:
Clancy, M. (2004). Misconceptions a
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Dönmez, O. and İnceoğlu, M. M. (
Page 401 and 402:
Fleury, A. E. (1991). Parameter Pas
Page 403 and 404:
Green, T. R. G. and Petre, M. (1996
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Hundhausen, C. D. (2002). Integrati
Page 407 and 408:
Karavirta, V. (n.d.). XAAL: eXtensi
Page 409 and 410:
Kumar, A. N. (2005). Results from t
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Lister, R. and Leaney, J. (2003). I
Page 413 and 414:
Mayer, R. E. and Alexander, P. A. (
Page 415 and 416:
Myller, N. and Bednarik, R. (2006).
Page 417 and 418:
Pashler, H., McDaniel, M., Rohrer,
Page 419 and 420:
Proulx, V. K. and Cashorali, T. (20
Page 421 and 422:
Rossy de Brito, S., Silva, A. S., d
Page 423 and 424:
Schumacher, R. M. (1987). Acquisiti
Page 425 and 426:
Smith, P. A. and Webb, G. I. (2000)
Page 427 and 428:
Thompson, E. (2008). How Do They Un
Page 429 and 430:
von Glasersfeld, E. (1998). Why Con
Page 432:
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