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evidence. Like the documentation of Parnas’s software projects, I hope that Part IV serves as a rationale for VPS to the benefit of anyone who wishes to understand it, use it, or improve on it. Now it is time to look at some empirical findings. 246
Part V Empirical Investigations of Visual Program Simulation 247
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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
Page 204 and 205: Figure 13.2: The between-step stage
Page 206 and 207: Figure 13.4: UUhistle highlights a
Page 208 and 209: Figure 13.7: UUhistle’s interacti
Page 210 and 211: 13.4 Teachers can turn examples int
Page 212 and 213: Add another +, then the literal 1.
Page 214 and 215: Figure 13.11: The user has created
Page 216 and 217: Figure 13.14: A VPS assignment in t
Page 218 and 219: Figure 13.16: A visual algorithm si
Page 220 and 221: Chapter 14 Visual Program Simulatio
Page 222 and 223: the individual instructions of the
Page 224 and 225: Visual program simulation seeks to
Page 226 and 227: 14.2.2 VPS exercises can have eleme
Page 228 and 229: features prominently in many progra
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Page 232 and 233: The easiest examples for a programm
Page 234 and 235: may be necessary; at the very least
Page 236 and 237: Figure 14.1 continued M. H. van Emd
Page 238 and 239: integration of new material with pr
Page 240 and 241: The system designer vs. excessive d
Page 242 and 243: Level 3 By allowing what’s wrong.
Page 244 and 245: about this mistake to the user in a
Page 246 and 247: Table 15.1: Cognitive dimensions of
Page 248 and 249: parts that deal with different stag
Page 250 and 251: Error-proneness A VPS exercise in U
Page 252 and 253: something that resides ‘within th
Page 256 and 257: Introduction to Part V Reflecting o
Page 258 and 259: launched for sharing (e.g., Fincher
Page 260 and 261: elatively independent of the resear
Page 262 and 263: There are values that are internal
Page 264 and 265: • Authenticity is an abstraction
Page 266 and 267: • Triangulation: triangulation of
Page 268 and 269: Each assignment was worth a number
Page 270 and 271: Figure 16.1: The earlier version of
Page 272 and 273: Chapter 17 Students Perceive Visual
Page 274 and 275: Figure 17.1: The phenomenographic r
Page 276 and 277: Figure 17.4: The structure of human
Page 278 and 279: Figure 17.5: The structure of aware
Page 280 and 281: further note the importance of esta
Page 282 and 283: Whichever variant of the analysis p
Page 284 and 285: Different phenomenographers define
Page 286 and 287: choose what to do?”, “What does
Page 288 and 289: We sought to form an outcome space
Page 290 and 291: 17.4.1 A: VPS is perceived as learn
Page 292 and 293: 17.4.2 B: VPS is perceived as learn
Page 294 and 295: Interviewer 2 : Can you describe ho
Page 296 and 297: 17.4.5 E: VPS is perceived as learn
Page 298 and 299: through VPS is perceived as learnin
Page 300 and 301: Table 17.2: Qualitatively different
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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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assignments but not focal to user a
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Chapter 22 What is Next for Visual
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22.3 VPS can be taken to new users
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Appendix A Misconception Catalogue
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Table A.1 continued No. Topic Descr
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Assignment 1.7 (VPS) marks = float(
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else: print 'Better luck next time.
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ide.drive(15) print ride.get_fuel()
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def quaint(list, element): list[0]
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Appendix D 3×10 Bullet Points for
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Okay, what should I take into consi
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References ACM and IEEE Computer So
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Bareiss, R. and Radley, M. (2010).
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Biggs, J. B. and Collis, K. F. (198
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Burkhardt, J.-M., Détienne, F., an
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Cross, II, J. H., Hendrix, T. D., a
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Ehlert, A. and Schulte, C. (2009).
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Gilligan, D. (1998). An Exploration
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Harlow, S., Cummings, R., and Abera
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Johnson, R. and Onwuegbuzie, A. J.
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Ko, P. Y. and Marton, F. (2004). Va
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Lauer, T. (2006). Learner Interacti
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Markman, A. B. and Gentner, D. (200
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Miyadera, Y., Kurasawa, K., Nakamur
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Norman, D. A. (2007). Simplicity is
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Peterson, L. R. and Peterson, M. J.
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Renkl, A., Stark, R., Gruber, H., a
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Sajaniemi, J., Kuittinen, M., and T
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Shneider, E. and Gladkikh, O. (2006
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Stoodley, I., Christie, R., and Bru
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Vainio, V. (2006). Opiskelijoiden m
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Willingham, D. T. (2009). Why Don
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