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Introduction to Part I Introductory programming courses around the world are failing to teach students how to program. While many students certainly do succeed, too many others fall far short of the goals set by curriculum planners and teachers. Evidence from computing education research shows that the problem is significant and not just local to a few institutions. Programming is an activity that is central to the field of computing. This is evidenced in computing education by the dominance of programming-first approaches to teaching introductory computing courses (ACM and IEEE Computer Society, 2001). The primary goal of a typical introductory computing course – commonly called a CS1 course or simply CS1 – is that students learn to create programs in some programming language. Unfortunately, it turns out that this is a demanding goal for an introductory course, and one that is often not met. In Part I, I try to substantiate these claims. Part I consists of two chapters. Chapter 2 examines the goals of programming education from the viewpoint of two influential educational taxonomies, Bloom’s taxonomy and SOLO. Chapter 3 is about the unwelcome evidence: many students do not acquire even rudimentary programming skills in CS1, and the problem is widespread. These chapters set the scene for a more detailed look at what it takes to learn to program, which will follow in Part II. 18
Chapter 2 Introductory Programming Courses are Demanding This chapter takes a look at the goals of introductory programming education in the light of two educational taxonomies: Bloom’s taxonomy and the SOLO taxonomy. These taxonomies enable us to characterize programming tasks in terms of their cognitive and structural complexity, which gives us an idea of what is commonly expected of beginner programmers. In Section 2.1 below, I introduce Bloom’s taxonomy and its applications to programming. Section 2.2 deals with SOLO. 2.1 Bloom’s taxonomy sorts learning objectives by cognitive complexity Some learning objectives are harder to achieve than others. It is much harder to learn to evaluate the quality of computer programs than to list programming keywords, for instance. In the 1950s, a group of educators led by Benjamin Bloom defined a taxonomy that divides learning into three domains: the cognitive, affective, and psychomotor (Bloom, 1956). In the same work, they further detailed the cognitive branch of the taxonomy by presenting a hierarchy of learning objectives ranked according to their expected cognitive complexity (see Figure 2.1). Bloom’s work has received wide acclaim and remains highly influential. The name “Bloom’s taxonomy” is used in two different ways. It sometimes refers to the overall taxonomy, with cognitive, affective, and psychomotor branches. It is also commonly used to refer to the taxonomy of learning objectives within the cognitive domain. In this thesis, “Bloom’s taxonomy” is shorthand for “Bloom’s taxonomy of learning objectives for the cognitive domain”. 2.1.1 The taxonomy distinguishes between six types of cognitive process The six levels of the original 1956 taxonomy, from lowest to highest, are: 1. Knowledge: the student can recall specific facts or methods. This level is characterized by verbs such as enumerate, name, and define; 2. Comprehension: the student understands the meaning of facts or concepts. This level is characterized by verbs such as explain, discuss, and paraphrase; 3. Application: the student can solve problems by applying knowledge to new concrete situations. This level is characterized by verbs such as produce, implement, and solve; 4. Analysis: the student can break down information into its parts to determine motives or causes, or to make inferences. This level is characterized by verbs such as analyze, discriminate, and infer; 5. Synthesis: the student can combine elements in new ways to produce novel wholes. This level is characterized by verbs such as create, compose, and invent; 19
Page 3 and 4: Aalto University publication series
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Page 9 and 10: Acknowledgements Lauri Malmi first
Page 11 and 12: 4.5.3 Some effects of cognitive loa
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Page 21 and 22: On research traditions Each researc
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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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Page 64 and 65: expert stage. De Kleer and Brown ch
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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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