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ide.drive(15) print ride.get_fuel() second_car = Car(60) second_car.refuel(10) Assignment 9.4 (VPS) class Person: def __init__(self, first_name, profession): self.__name = first_name self.__profession = profession def greet(self, encounteree): return self.__name + '. Pleased to meet you, ' + encounteree.__name def get_profession(self): return self.__profession first = Person('Babar', 'doctor') print first.get_profession() second = Person('Sapphira', 'biologist') print first.greet(second) 374
Appendix C Example Programs from Experiment These programs were used in the experiment from Chapter 19. The identifiers and string literals used below roughly correspond to the original Finnish ones seen by the students. Initial example This code was shown to the treatment group as a UUhistle v0.2 animation and explained to the control group textually, line by line. first = [ 10, 5, 10, 6 ] print first[3] second = [ 3, 1, -2 ] print second second[2] = second[2] + 1 print second[2] First VPS task This code was given to the treatment group as a VPS exercise. The control group was also provided the correct output and asked to figure out and explain why the program worked as it did. first = [ 10, 5, 0 ] first[1] = -5 value = first[2] print first second = [ 3, 1, 3, value ] print second[value] third = second second[3] = 100 print second print third second = third print second Second VPS task def weird(list, index): test = list list = [3, list[0]] test[index] = list[0] return test[1] 375
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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
Page 332 and 333: Chapter 19 UUhistle Helps Students
Page 334 and 335: • The supervising research assist
Page 336 and 337: Table 19.1: Improvement from ‘non
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Page 348 and 349: In many cases the UUhistle assignme
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Page 352 and 353: A handful of students suggested tha
Page 355 and 356: Part VI Conclusions 347
Page 357 and 358: Chapter 21 Visual Program Simulatio
Page 359 and 360: assignments but not focal to user a
Page 361 and 362: Chapter 22 What is Next for Visual
Page 363: 22.3 VPS can be taken to new users
Page 366 and 367: Appendix A Misconception Catalogue
Page 368 and 369: 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 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 410 and 411: Lauer, T. (2006). Learner Interacti
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
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