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involve VPS). 17 • The teacher uses UUhistle (or a similar system) in class in program animation mode to explore how example programs work. The visualization serves as an illustration of useful examples. The examples are carefully chosen so that the programs are difficult to understand while their execution is hidden but easier when it is explicit. VPS might also be used in class so that students vote on key steps, and incorrect answers are also explored and explained (cf. Pears and Rogalli, 2011). • Example selection draws on misconception catalogues so that the example programs encourage students to experience fruitful cognitive conflict and detect their misconceptions. When possible, teachers engage students in conversation about the mistakes they make during VPS. Teachers can explicitly warn about common misconceptions in class, and use the visualization to demonstrate their non-viability. • Students encounter example programs that work in mysterious or unexpected ways that cannot be explained without a better understanding of related concepts (e.g., references, parameters). UUhistle is then used to figure out why the program works as it does. • As a special case of the above, UUhistle is used to find a bug in an example program, which can then be fixed. • Students use (the full build of) UUhistle to visually debug their own programs. • VPS exercises are explicitly referred to in other assignments. This can take many forms. For instance, a program-writing assignment may refer to an earlier VPS exercise as a necessary prerequisite, or a VPS exercise may be embedded into a larger assignment, or it may be mentioned as a potential learning aid to be used in case the student has trouble with a program-writing task. Crucially, the purpose and goals of the VPS exercise in relation to the other assignment are made clear so that students know why they are doing the specific exercise. • Visualization-based assignments feature ‘planlike’ example programs that have explicit high-level goals. Encountering just one or a few early cases where a concrete benefit is gained from understanding the visualization may greatly affect students’ perceptions of the VPS system, and motivate them to adopt a deeper approach as they work on future VPS exercises. 17.6.3 We have already incorporated some of our advice into UUhistle Some of our recommendations on teaching about VPS can be incorporated into a software tool such as UUhistle. We have already begun work on some of them. As soon as the first drafts of our outcome space emerged, we reprioritized the items in UUhistle’s feature requests list, and added to it, on the basis of our results. Broadly speaking, we switched priorities from seeking greater coverage of language features and other technical improvements towards features that encourage students to seek meaning in the visualization and enable them to genuinely benefit from the tool. The main differences between the prototype that the students in this study used and the current version of UUhistle from Chapter 13 were outlined in Section 16.4.2. The following recent features were partially inspired by the results we have presented in this chapter: • the Info box and its associated explanatory texts, which seek to draw the learner’s attention to the conceptual content of the visualization and invite exploration of programming concepts; 17 We may reflect on the suggestions listed here against the context of the CS1–Imp–Pyth course that we studied. In that course offering, there was some use of UUhistle’s visualization in lectures, but for the most part, the integration of UUhistle and VPS with other aspects of the course was limited (see Section 16.4). In effect, it was largely left to the students to perceive meaning in the visualization and to relate it to other assignments and programming in general. 298
• the “What is this?” menu choices accessible through context menus (see Section 13.1.3), which serve a similar purpose; • the features that attempt to draw the user’s attention to points of interest or to directly address specific misconceptions through textual materials (see Sections 13.1.3, 13.4.2, and 15.3); • various small additions and quality improvements towards the goal of making the full build of UUhistle as usable and robust as possible so that students can use it to debug their own programs. Further improvements in this vein have high priority in UUhistle’s development at the present time. Such additions, useful though they may be, are unlikely by themselves to be sufficient to address the issues highlighted by this chapter. Presenting VPS in the right way in teaching and integrating it well with other course materials remains, we believe, of the utmost importance in order for students to make the most of VPS. This requires, at least at present, effort from human course personnel. 17.7 There are vague spots in our analysis Finding patterns is one result of analysis. Finding vagaries, uncertainties, and ambiguities is another. (Patton, 2002, p. 437) We comment briefly on some of the alternative analyses we discarded in favor of the one presented, and a couple of vague spots in our results. Our outcome space as presented has two branches. In contrast, the early versions of the outcome space were clean hierarchies in which each category extended a single other category. These early outcome spaces were problematic, however: it was difficult to determine which of the categories ‘in the middle’ were extensions of which other categories. Our data eventually led us to the current bifurcate outcome space as it appears to be possible to perceive learning through VPS as either computer-related or program-examplerelated while unaware of the other ‘branch’ (cf., e.g., how Lynda and Beth discuss their understandings above). It can be questioned – we did – whether there is a genuine qualitative difference between Categories A and D. Category D adds to Category A merely the realization that as one does VPS exercises, one ends up seeing program code, and some aspects of the code may end up being remembered. This realization is arguably very obvious. It is possible that even the simplest way of understanding the phenomenon encompasses this realization and that Categories A and D should be merged into one category in which all the other categories are rooted. However, our data does suggest that ways of understanding exist that involve no consideration of VPS as a platform for example code. At least, such consideration is not necessarily present in the dialogue of novice programmers, despite in-depth probing. The justification for the existence of Category C as a separate category can also be questioned, the alternative analysis being that execution order is just one aspect of computer behavior and Categories B and C are qualitatively similar. We did, however, feel that our data tentatively supports the existence of Category C as a category of its own, given the great emphasis that ‘actual execution order’ appeared to have in students’ thinking and dialogue. That said, Category C is arguably not quite as solidly founded on evidence as other parts of our outcome space. 18 Students do talk about execution order, they do focus on it as they solve problems, and they do find it difficult. Two students (William and Otto; Section 17.4.3) highlighted execution order as what UUhistle gives them a new perspective on. This is a vague area that further research and richer data could illuminate better. Our pedagogical recommendations are not predicated on the existence of Category C separately from Category B. Category E features another issue that we wish, in hindsight, that we had richer data on. This category is entirely based on a single interview. In particular, the role of program dynamics in this category is somewhat vague. What we can say from our data is that the interviewee, Beth, did not focus or comment on the computer’s role in program execution, and did not appear to relate the changes in the visualization to events in the computer. However, our data does not give a clear picture of just what Beth 18 All our categories are, to an extent, based on conjecture, as are, in our pragmatist view (see Section 16.2), most or all scientific results. 299
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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
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
Page 302 and 303: what the phenomenon is in reality,
Page 304 and 305: the students develop a sophisticate
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Page 310 and 311: solve the assignments using the ani
Page 312 and 313: Table 18.1: Types of information us
Page 314 and 315: As they start to work on the first
Page 316 and 317: print third second = third As they
Page 318 and 319: Elizabeth: And. . . So the function
Page 320 and 321: Otto: “True”. . . And then we g
Page 322 and 323: The pair fail to accommodate refere
Page 324 and 325: Parameter passing was the only topi
Page 326 and 327: 18.3 We have some quantitative resu
Page 328 and 329: and the student searches for meanin
Page 330 and 331: 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
Page 338 and 339: - attempts to make variables get th
Page 340 and 341: to predict program behaviors better
Page 342 and 343: • The use of programs with a doma
Page 344 and 345: in the course more generally, not o
Page 346 and 347: Good both as an idea and in functio
Page 348 and 349: In many cases the UUhistle assignme
Page 350 and 351: Boredom and excessive detail A fair
Page 352 and 353: A handful of students suggested tha
Page 355 and 356: 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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