isbn9789526046266

Recommendations

Info

Figure 11.34: An assignment on string catenation within the Online Tutoring System (Kollmansberger, 2010). The student has first chosen the expression txtFirst.Text + txtSecond.Text for evaluation, then the subexpression txtFirst.Text. Now the system informs them that the next step is to access the variable: consequently the user has to look at the values in memory (on the right) and type in "11" (in quotes, to highlight that it is a string). Subsequent steps include typing in "4" and "114" at the appropriate moments. Figure 11.35: Stages of expression evaluation within the Online Tutoring System (Kollmansberger, 2010). Some steps have been omitted. 182
Figure 11.36: A “Clouds & Boxes” assignment in ViLLE has the user take charge of running a program. The buttons at the top allow the user to manipulate variables and the call stack. 11.3.4 Various systems take a low-level approach The systems reviewed in this chapter so far have represented notional machines that operate on high-level languages at various levels of abstraction that are not very close to the underlying hardware. One way to learn about program dynamics – albeit rare in CS1 – is to first build low-level foundations by visualizing an actual or simplified system that explains programming on the assembly language or bytecode level. Many existing program visualization tools might serve a purpose in such an endeavor. Biermann et al. (1994) created a system – This is How A Computer Works – for visualizing Pascal programs to freshmen at different levels of abstraction below the code: a compiler level, a machine architecture level, and a circuit level. The simulator MPC1 presented a simplified computer in terms of processor operation codes, RAM cells, and registers (Sherry, 1995). The SIMPLESEM Development Environment (Hauswirth et al., 1998) and MieruCompiler (Gondow et al., 2010) also map high-level language semantics to assembly code. EasyCPU (Yehezkel et al., 2007, and references therein) visualizes a model of computer components – registers, I/O ports, etc. – and highlights how each component is active in the execution of an assembly-language program. A different sort of low-level approach was implemented in ITEM/IP, which visualized the execution of a Pascal-like language in terms of a Turing machine (Brusilovsky, 1992). Some low-level systems have featured modes of interaction beyond controlling the view. In the CpuCITY virtual 3D world the student used an avatar to perform hardware-level operations such as “take this packet to RAM” (Bares et al., 1998). JV 2 M applies a similar immersive approach to Java programming by having a student-controlled avatar perform in-game tasks that match the bytecode instructions corresponding to a given Java program (e.g., Gómez-Martín et al., 2005; Gómez-Martín et al., 2006). Figure 11.37 shows a screenshot of JV 2 M. The way in which CpuCITY and JV 2 M engage the student makes them similar in a sense to our concept of visual program simulation (see Part IV), although the level of abstraction and the virtual world approach are quite different. Various other low-abstraction PV systems exist; the above is a fairly arbitrary selection. Many of the systems may be useful for the goal of teaching about low-level phenomena (which is indeed the stated goal of most of the systems). I will not review low-level systems in more detail, as my primary interest is in CS1 and systems that help students learn the runtime semantics of a high-level language. I consider systems that deal with assembly language too detailed to be practical for this purpose – the ‘black boxes’ inside 183
Page 3 and 4:
Aalto University publication series
Page 5:
Abstract Aalto University, P.O. Box
Page 9 and 10:
Acknowledgements Lauri Malmi first
Page 11 and 12:
4.5.3 Some effects of cognitive loa
Page 13 and 14:
13.1.2 It is simple to watch an ani
Page 15 and 16:
18.2.3 We saw a few pedagogically i
Page 17 and 18:
11.16 PlanAni .....................
Page 19 and 20:
Chapter 1 Here is How to Make Sense
Page 21 and 22:
On research traditions Each researc
Page 23:
The thesis should ideally be read f
Page 26 and 27:
Introduction to Part I Introductory
Page 28 and 29:
Figure 2.1: Bloom’s taxonomy of l
Page 30 and 31:
2.2 The SOLO taxonomy sorts learnin
Page 32 and 33:
2.2.3 The expected outcomes of prog
Page 34 and 35:
Multi-institutional studies In the
Page 36 and 37:
3.3 But many students do not learn
Page 38 and 39:
long-term, action research study of
Page 40 and 41:
Introduction to Part II What is inv
Page 42 and 43:
Figure 4.1: A commonly used basic a
Page 44 and 45:
play a decisive role in how and whe
Page 46 and 47:
cognitive psychology also sought to
Page 48 and 49:
terms for the two meanings. Followi
Page 50 and 51:
It suggests that the growth of expe
Page 52 and 53:
work (because of lack of motivation
Page 54 and 55:
The effect of prior knowledge: the
Page 56 and 57:
“writing a computer program is le
Page 58 and 59:
model of program comprehension to o
Page 60 and 61:
Chapter 5 Psychologists Also Say: W
Page 62 and 63:
• are commonly deficient in a num
Page 64 and 65:
expert stage. De Kleer and Brown ch
Page 66 and 67:
5.3 Teachers employ conceptual mode
Page 68 and 69:
Not only are there different notion
Page 70 and 71:
the computer can carry out deductio
Page 72 and 73:
Therefore: teach early and teach lo
Page 74 and 75:
epresentation of a complex program
Page 76 and 77:
A reasonable description of this fo
Page 78 and 79:
are “somewhat contrary to the cla
Page 80 and 81:
memory storage (see, e.g., Greeno,
Page 82 and 83:
Chapter 6 Constructivists Say: Know
Page 84 and 85:
Figure 6.1: A classification of con
Page 86 and 87:
Social constructivist reasoning tak
Page 88 and 89:
Figure 6.2: A radical destructivist
Page 90 and 91:
certainly fall under the broad usag
Page 92 and 93:
in the eyes of the members of the c
Page 94 and 95:
of proper design, coding style, req
Page 96 and 97:
In any particular course you will b
Page 98 and 99:
precedes construction. Therefore, c
Page 100 and 101:
Skirmish 7: minimal guidance pedago
Page 102 and 103:
Chapter 7 Phenomenographers Say: Le
Page 104 and 105:
the phenomenon. An individual’s e
Page 106 and 107:
(Bowden and Marton, 2004). A way of
Page 108 and 109:
The answer is none in particular, w
Page 110 and 111:
Table 7.3: Different ways of experi
Page 112 and 113:
7.6 Phenomenography has not escaped
Page 114 and 115:
outcome spaces describe both the st
Page 116 and 117:
Figure 8.1: Views from three tradit
Page 118 and 119:
There is substantial agreement betw
Page 120 and 121:
• it may mark boundaries in ‘co
Page 122 and 123:
Telling TCs apart from other conten
Page 124 and 125:
et al., 2007). The latter was later
Page 127 and 128:
Part III Teaching Introductory Prog
Page 129 and 130:
Chapter 10 CS1 is Taught in Many Wa
Page 131 and 132:
it and right from the beginning. So
Page 133 and 134:
10.1.3 Guidance mediates complexity
Page 135 and 136:
10.2 Some approaches foster schema
Page 137 and 138:
Many CS1 teachers have come up with
Page 139 and 140: Vagianou (2006) observes that a wea
Page 141 and 142: Figure 10.6: A view of Anchor Garde
Page 143 and 144: impressions of computing matter and
Page 145 and 146: The paradigm shift There is anecdot
Page 147 and 148: notional machines. Sajaniemi and Ku
Page 149 and 150: Program visualization vs. algorithm
Page 151 and 152: Figure 11.2: A part of Kelleher and
Page 153 and 154: aspects can play a decisive part in
Page 155 and 156: Table 11.1: The original engagement
Page 157 and 158: The 2DET The engagement taxonomy of
Page 159 and 160: Given content means that the learne
Page 161 and 162: Table 11.5: A summary of selected p
Page 163 and 164: Figure 11.4: The PyDev debugger for
Page 165 and 166: Figure 11.6: DynaLab executing a To
Page 167 and 168: Figure 11.10: The user has just com
Page 169 and 170: Figure 11.11: Korsh and Sangwan’s
Page 171 and 172: Figure 11.14: JIVE displays various
Page 173 and 174: Figure 11.16: PlanAni executing a P
Page 175 and 176: Figure 11.18: A snapshot of a Java
Page 177 and 178: Figure 11.19: Jeliot 3 executing a
Page 179 and 180: Figure 11.21: The Teaching Machine
Page 181 and 182: students used VIP in the way intend
Page 183 and 184: Python in the browser: Jype and the
Page 185 and 186: 11.3.3 A few systems make the stude
Page 187 and 188: Figure 11.31: Students interact wit
Page 189: Online Tutoring System Kollmansberg
Page 193: Table 11.7: Experimental evaluation
Page 196 and 197: Introduction to Part IV We have now
Page 198 and 199: An example Here is a short Python c
Page 200 and 201: Chapter 13 The UUhistle System Faci
Page 202 and 203: 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
Page 230 and 231: too, later decided to develop a sof
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 254 and 255:
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
Page 306 and 307:
involve VPS). 17 • The teacher us
Page 308 and 309:
thought about the dynamics of VPS.
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
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 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
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 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
show all

isbn9789526046266

Create successful ePaper yourself

Delete template?

Save as template?