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Supervisor Professor Lauri Malmi Preliminary examiners Professor Mark Guzdial, Georgia Institute of Technology, USA Professor Emeritus Veijo Meisalo, University of Helsinki, Finland Opponent Associate Professor Mordechai Ben-Ari, Weizmann Institute of Science, Israel Aalto University publication series DOCTORAL DISSERTATIONS 61/2012 © Juha Sorva ISBN 978-952-60-4625-9 (printed) ISBN 978-952-60-4626-6 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) Unigrafia Oy Helsinki 2012 Finland The dissertation can be read at http://lib.tkk.fi/Diss/
Abstract Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Juha Sorva Name of the doctoral dissertation Visual Program Simulation in Introductory Programming Education Publisher School of Science Unit Department of Computer Science and Engineering Series Aalto University publication series DOCTORAL DISSERTATIONS 61/2012 Field of research Computing Education Research Manuscript submitted 27 February 2012 Manuscript revised 25 April 2012 Date of the defence 30 May 2012 Monograph Language English Article dissertation (summary + original articles) Abstract This thesis formulates and evaluates a pedagogical technique whose goal is to help beginners learn the basics of computer programming. The technique, visual program simulation (VPS), involves the learner in interactive simulations in which the learner takes on the role of the computer as the executor of a program. The student uses a given visualization of a so-called notional machine, an abstract computer, to illustrate what happens in memory as the computer processes the program. The purpose of these simulations is to help the beginner learn to reason about program execution, a skill whose development has been identified as a major challenge in introductory programming education. VPS promotes effective learning by seeking to cognitively engage the learner with a visualization. It can be made practical through visualization software. VPS software may also automatically assess students' simulations and provide personal feedback, which is a valuable asset especially in the large classes that are typical of introductory courses. The thesis contributes to VPS in four ways. First, it formulates the concept of visual program simulation and outlines its underpinnings in terms of learning theory. Second, it presents a new software prototype that facilitates the use of VPS in practice. Third, it reports on a preliminary empirical evaluation of VPS and the software in the context of an introductory programming course. Fourth, it makes recommendations on the use of VPS in teaching and the further development of VPS tools, which arise from the empirical work. The findings from a mixed-methods evaluation of VPS suggest that it is a promising pedagogical approach that helps many students learn programming. At the same time, the evaluation highlights certain important weaknesses. The purpose of VPS is not obvious to many students. Care must be taken to ensure that students develop a rich understanding of what VPS is and what they stand to gain from it. For best results, it is recommended that VPS be tightly integrated into the teaching and learning environment. The results from a controlled experiment further indicate that the short-term learning benefits of a VPS assignment are heavily dependent on which interactions the assignment demands from students. This implies that extreme care must be taken in the design of VPS systems and specific assignments so that required user interactions are aligned with intended learning goals. On a more general level, the thesis serves as an example of educational tool development that is grounded in learning theory and informed by empirical evaluations. A fairly broad review of the literature on learning and teaching introductory programming is also contributed. Keywords visual program simulation, introductory programming education, novice programmers, program visualization, interactive visualization for education ISBN (printed) 978-952-60-4625-9 ISBN (pdf) 978-952-60-4626-6 ISSN-L 1799-4934 ISSN (printed) 1799-4934 ISSN (pdf) 1799-4942 Location of publisher Espoo Location of printing Helsinki Year 2012 Pages 428 The dissertation can be read at http://lib.tkk.fi/Diss/
Page 3: Aalto University publication series
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
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Page 19 and 20: Chapter 1 Here is How to Make Sense
Page 21 and 22: On research traditions Each researc
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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
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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
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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
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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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